Limitations, delimitations, and Summary for Chapter 1 of dissertation

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FINANCIAL REGULATION AND ECONOMIC PERFORMANCE:

A DESCRIPTIVE CORRELATION STUDY OF AMERICAN

FINANCIAL INSTITUTIONS

Leslie E. Lynch

A Dissertation Presented in Partial Fulfillment

of the Requirements for the Degree

Doctor of Business Administration

UNIVERSITY OF PHOENIX

May 2013

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UMI Number: 3692081

Abstract

The purpose of this descriptive correlation study with a logistic regression analysis was to

examine the relationship between SEC regulatory compliance or malfeasance and the

economic performance of American, publicly traded financial institutions from 2005

through 2010. The research study examined three research questions. The research

questions addressed the relationship between a company’s financial performance and

associated regulatory malfeasance or compliance as well as the type and volume of

regulatory violations. Three predictor variables were operationalized as follows: (a)

regulatory compliance or malfeasance, (b) the type of malfeasance, and (c) the volume of

malfeasance. The study researched 74 publicly traded financial institutions categorized

under SIC 6211. The results demonstrated that (a) neither regulatory compliance nor

malfeasance were statistically related to the financial performance of the financial

institutions, and (b) no significant relationship exists between the volume or type of

regulatory malfeasance and the financial performance of the institutions researched. A

statistically significant relationship (p-value=0.054) was uncovered between one specific

type of regulatory violation and financial performance. Companies with violations

categorized as irresponsible or unfair treatment of customers reported the weakest

financial performance. The results were determined using several statistical methods of

analysis.

Dedication

This accomplishment is dedicated to my husband who patiently endured too many lonely

weekends, to my father and mother who taught me that hard work always pays off, and to

my sons who kept me grounded.

Acknowledgements

Thank you to my dissertation chair, Dr. Barbara Fedock for her guidance and

unbelievably positive encouragement throughout this journey. Thank you to my

committee members, Drs. Kofi Amoateng and Gerald Wiesenseel. Special mention is

offered to a fellow doctoral student, Robin Laukhuf, for her collaboration and ‘ear.’

vii

Table of Contents

List of Tables .................................................................................................................... xii

List of Figures .................................................................................................................. xiv

Chapter 1: Introduction ........................................................................................................1

Background of the Problem .................................................................................................2

Problem Statement ...............................................................................................................4

Purpose Statement ................................................................................................................6

Significance of the Study .....................................................................................................7

Significance to Leadership ...................................................................................................8

Nature of the Study ..............................................................................................................9

Research Questions and Hypotheses .................................................................................12

Operational Definition of Variables ...................................................................................14

Conceptual Framework ......................................................................................................15

Germinal and Historical Theory ............................................................................16

Economic Theory ...................................................................................................16

Public-Choice Theory ............................................................................................17

Leadership Theory .................................................................................................17

Current Theory .......................................................................................................18

Definition of Terms............................................................................................................19

Assumptions .......................................................................................................................23

Scope, Limitations, and Delimitations ...............................................................................24

Limitations .............................................................................................................25

Delimitations ..........................................................................................................26

viii

Summary ............................................................................................................................27

Chapter 2: Literature Review .............................................................................................28

Overview ............................................................................................................................28

Background ........................................................................................................................29

Related Legislation ............................................................................................................30 Securities Act of 1933 and Securities Exchange Act of 1934 ...............................32

Glass-Steagall Act of 1933 ....................................................................................35

Investment Company Act of 1940 .........................................................................36

Securities Acts Amendments of 1964 ....................................................................37

Sarbanes-Oxley Act of 2002 ..................................................................................38

Dodd-Frank Consumer Protection Act of 2010 .....................................................40

Regulatory Malfeasance.....................................................................................................41

U.S. Securities and Exchange Commission ...........................................................42

Public Company Restatements and Misstatements................................................43

Mandatory Disclosures ..........................................................................................45

Financial Statement Malfeasance and Securities Fraud .........................................47

Business Ethics ..................................................................................................................47

Individuals..............................................................................................................48

Normative Constructs ................................................................................48

Descriptive Constructs ...............................................................................49

Prescriptive Constructs ..............................................................................50

Leadership ..............................................................................................................51

Organizational Factors ...........................................................................................52

Culture........................................................................................................52

Governance ................................................................................................53

Miscellaneous Factors ................................................................................53

Economic Performance ......................................................................................................54

Earnings Management ...........................................................................................55

Valuation Measures ...............................................................................................56

Conclusion .........................................................................................................................56

Summary ............................................................................................................................57

Chapter 3: Methodology ....................................................................................................60

Research Method ...............................................................................................................60

Research Design.................................................................................................................62

Research Questions and Hypotheses .................................................................................64

Operational Definition of Variables ...................................................................................66

Sample and Setting ............................................................................................................67

Sampling Frame .....................................................................................................68

Archived Data Sample ...........................................................................................69

Geographic Location ..............................................................................................69

Data Collection ..................................................................................................................69

Archival Data .........................................................................................................69

Reliability and Validity ......................................................................................................70

Data Analysis .....................................................................................................................73

Screening................................................................................................................74

Statistics .................................................................................................................74

Canonical Correlation ................................................................................74

Multiple Regression ...................................................................................75

Summary ............................................................................................................................76

Chapter 4: Presentation and Analysis of Data ...................................................................78

Data Collection Process ....................................................................................................78

Appropriateness of Statistical Tests ..................................................................................82

Data Analysis Procedure ...................................................................................................83

Step 1: Correlation between Gross Profit Margin and After-tax Profit Margin ...84

Step 2: Repeated ANOVA ....................................................................................84

Step 3: RQ1 Logistic Regression ..........................................................................85

Step 4: RQ 2 Logistic Regression .........................................................................86

Step 5: RQ 3 Poisson Regression ..........................................................................88

Step 6: Additional Chi-squared Analysis ..............................................................89

Critical Values .......................................................................................................90

Summary ...........................................................................................................................91

Chapter 5: Conclusions and Recommendations ...............................................................92

Summary of Findings ........................................................................................................94

Theme 1(RQ2): Type of Regulatory Violations ...................................................95

Theme 2 (RQ1): Regulatory Compliance or Malfeasance ...................................96

Theme 3 (RQ3): Volume of Regulatory Violations .............................................97

Theme 4: Top-performing Companies ..................................................................97

Restatement of Limitations ...............................................................................................98

Other Limitations ..................................................................................................99

Conclusions and Implications .........................................................................................100

Implications to Leaders .......................................................................................101

Implications to the Study of Leadership .............................................................101

Recommendations for Further Study ..............................................................................102

Timeframe ...........................................................................................................102

Sector ..................................................................................................................102

Research Design ..................................................................................................102

Research Reflections ...........................................................................................103

Summary .........................................................................................................................104

References ........................................................................................................................106

Appendix A. Companies for SIC 6211 ...........................................................................139

Appendix B. Sample of Securities and Exchange Commission Enforcement Actions .......................................................................................................144

Appendix C. Twenty-First Century Corporate Scandals ................................................146

Appendix D. Data Analysis Procedure Step 1 Results ....................................................147

Appendix E. Data Analysis Procedure Step 2 Results .....................................................148

Appendix F. Data Analysis Procedure Step 3 Results .....................................................149

Appendix G. Data Analysis Procedure Step 4 Results ....................................................151

Appendix H. Data Analysis Procedure Step 5 Results ....................................................153

Appendix I. Data Analysis Procedure Step 6 Results ......................................................155

xii

List of Tables

Table 1 Criterion and Predictor Variables Ordered by Hypothesis..................................65

Table 2 Types of Regulatory Infractions ............................................................................81

Table 3 Parameter Estimates: Violation Type 2 ................................................................87

Table 4 Critical Values Synopsis .......................................................................................90

Table D1 Correlations between Gross Profit Margin and After-tax Profit Margin .......147

Table E1 Test of Model Effects .......................................................................................148

Table E2 Omnibus Test ...................................................................................................148

Table E3 Mauchley’s Test of Sphericity .........................................................................149

Table E4 Test of Within-subjects Effects ........................................................................149

Table F1 Omnibus Test: Regulatory Compliance or Malfeasance .................................150

Table F2 Parameter Estimates: Regulatory Compliance or Malfeasance .....................150

Table G1 Omnibus Test: Violation Type 1 .....................................................................151

Table G2 Parameter Estimates: Violation Type 1 ..........................................................151

Table G3 Omnibus Test: Violation Type 2 .....................................................................151

Table G4 Omnibus Test: Violation Type 3 .....................................................................151

Table G5 Parameter Estimates: Violation Type 3 ..........................................................152

Table G6 Omnibus Test: Violation Type 4 .....................................................................152

Table G7 Parameter Estimates: Violation Type 4 ..........................................................152

Table H1 Frequency Table of Total Volume ...................................................................153

Table H2 Omnibus Test: Volume ....................................................................................154

Table H3 Parameter Estimates: Volume ........................................................................154

Table I1 Mean Gross Profit Margin Percentiles ............................................................155

xiii

Table I2 Descriptive Crosstabs: Gross Profit or Loss to Malfeasance ..........................155

Table I3 Chi-squared Tests: Malfeasance ......................................................................155

Table I4 Descriptive Crosstabs: Gross Profit or Loss to Type 1 Violations ..................156

Table I5 Chi-squared Tests: Type 1 Violations ..............................................................156

Table I6 Descriptive Crosstabs: Gross Profit or Loss to Type 2 Violations ..................157

Table I7 Chi-squared Tests: Type 2 Violations ..............................................................157

Table I8 Descriptive Crosstabs: Gross Profit or Loss to Type 3 Violations ..................158

Table I9 Chi-squared Tests: Type 3 Violations ..............................................................158

Table I10 Descriptive Crosstabs: Gross Profit or Loss to Type 4 Violations ................159

Table I11 Chi-squared Tests: Type 4 Violations ............................................................159

Table I12 Frequency Table: Gross Profit Margin to Top-10, Profit, and Loss Companies .......................................................................................................159

Table I13 Descriptive Crosstabs: Top 10, Profit, and Loss to Malfeasance ..................160

Table I14 Descriptive Crosstabs: Top-10, Profit, and Loss to Type 1 Violations ..........161

Table I15 Chi-squared Tests: Type 1 Violations ............................................................161

Table I16 Descriptive Crosstabs: Top-10, Profit, and Loss to Type 2 Violations ..........162

Table I17 Chi-squared Tests: Type 2 Violations ............................................................162

Table I18 Descriptive Crosstabs: Top-10, Profit, and Loss to Type 3 Violations ..........163

Table I19 Descriptive Crosstabs: Top-10, Profit, and Loss to Type 4 Violations ...........164

Table I20 Fisher’s Exact Test: Malfeasance and Type 3 Violations ..............................164

xiv

List of Figures

Figure 1. Accessed Financial Statements by Year ............................................................80

Figure E1. Variance Boxplot of the Sample Companies’ Gross Profit Margins ............148

Figure H1. Total Volume Distribution ...........................................................................153

Chapter 1: Introduction

The 2008 financial crisis and subsequent failure of American financial institutions

were the result of global liquidity concerns and a poor regulatory framework (Blundell-

Wignall, Atkinson, & Lee, 2008). In 2008, a global investment bank based in New York

ceased to exist as acquisition by one of the oldest global financial services firms in the

United States was completed. Executives of the fourth largest investment bank in the

United States filed the largest bankruptcy in history. The board of directors of one of the

world’s largest financial institutions approved the acquisition of a financial-management

and advisory company, and leaders of an American multinational insurance company

accepted an $85 billion federal loan (Dolmetsch, 2008). In response to this economic

scenario, members of Congress passed new laws. Regulatory agencies concurrently

began the development of new governmental-malfeasance policy.

Financial executives anticipate forthcoming regulations and experience pressure

to maximize profits and meet earnings projections (Berrone, Surroca, & Tribo, 2007).

Regulatory advocates have argued that compliance with governmental policy generates

the trust and commitment of stakeholders, which ensures long-term performance

(O’Brien, 2010). Regulatory antagonists have contended, however that governmental

policy is burdensome and offers no validated payoff for stakeholders (Berrone et al.,

2007). These two adversaries may find the results of the quantitative study helpful in the

assessment of regulatory-compliance value.

Chapter 1 introduces the background of the problem and the purpose of the study.

The chapter continues with an analysis of the study’s significance. Research questions

are presented that guide the study and reflect the intent of the research (Salkind, 2009).

Hypotheses are stated to communicate the expected findings of the investigation (Leedy

& Ormrod, 2010). The research assumptions and the scope of data collected in the study

are examined, and the chapter concludes with a discussion of the study’s limitations and

delimitations.

Background of the Problem

Before the stock-market crash of 1929, the securities markets were governed by

minimal federal regulation (Li & Xu, 2008; Securities and Exchange Commission [SEC],

2008). The demand for public financial disclosure to prevent the fraudulent sale of stock

was not yet seriously pursued. American investors still believed in the promise of easy

credit and the American dream (SEC, 2008). However, public confidence in the

securities markets was destroyed by the 1929 crash (Securities Law Advisory, 2011).

Members of the U.S. Congress passed the Securities Act of 1933 and the Securities

Exchange Act of 1934 (Li & Xu, 2008; SEC, 2008). Collectively, this legislation

intended to restore public confidence in the capital markets by providing investors access

to reliable publicly traded securities information (State of Wisconsin Department of

Financial Institutions, 2010). Policy related to honest business practice was introduced,

and rules were written to protect investors against fraud and misrepresentation (SEC,

2008).

The Securities Exchange Act of 1934 facilitated creation of the U.S. SEC. The

mission of the SEC was designed to protect investors and maintain efficient markets as

well as to facilitate capital formation (SEC, 2008). The Commission was assigned the

law-enforcement authority to serve as the regulatory agency overseeing adherence to the

mandates of the Securities Act of 1933 and the Securities Exchange Act of 1934. Public

disclosure of financial and other information by executives of publicly listed companies

is required by the SEC (2008).

SEC regulators investigate violations of the rules and regulations established by

members of the Commission (SEC, 2008). Reporting managers of an organization may

misrepresent or omit important information related to securities, manipulate stock-market

price, or violate insider trading rules as established by those comprising the SEC.

Company executives have been found guilty of violating their responsibility to treat

customers fairly or of stealing customer funds or securities (Rhee, 2009). When

violations are committed, questions are raised if business ethics can be legislated

(O’Brien, 2010; Pellerin, Walter, & Wescott, 2009).

On July 21, 2010, members of the U.S. Congress enacted the Consumer

Protection Act, also known as Dodd-Frank (Anderson et al., 2011). Dodd-Frank is

considered to include the most sweeping securities law since the Securities Act of 1933

and the Securities and Exchange Act of 1934. Financial institutions are significantly

affected by this broad expansion of regulatory oversight and corporate governance

mandates (Anderson et al., 2011; Raidy, 2011). The increase in regulatory reform and

government oversight was introduced in response to the failure of American financial

institutions and the subsequent global financial crisis (Blundell-Wignall et al., 2008).

Sherman (2009) referred to the enactment of the new law and subsequent

regulations as providing greater transparency in business practice and hence more rapid

revelation of corporate fraud than prior policy. Edgar (2009) confirmed the need for

efficient and effective corrective regulation. A need has also been identified for greater

transparency of publicly traded companies (Burr, 2010). Few researchers have linked

increased government regulation with improved economic performance (Burr, 2010;

Edgar, 2009). Executive leadership may devalue long-term regulatory compliance to

meet short-term earnings estimates (Choi & Jung, 2008; Heneghan, 2010).

The quantitative study was conducted to examine the relationship between

regulatory compliance or malfeasance and the economic performance of publicly traded

financial institutions. New data was revealed that may serve as the basis for SEC

compliance by financial executives. Public policy makers may undertake further study to

extend this research.

Problem Statement

The problem is that chief executives of publicly traded financial institutions

encounter significant pressure to maximize profits within a legal and regulatory

framework (Heneghan, 2010). Overlooked by the investigators of existing empirical

research is evidence of the influence of regulatory compliance or malfeasance on

economic performance (Choi & Jung, 2008). O’Brien (2010) asserted that published

researchers have failed to show empirical evidence demonstrating a statistically

significant or insignificant relationship between regulatory compliance or malfeasance

and the economic performance of financial institutions. Lin and Hwang (2010) claimed

that published researchers have presented little consistent evidence on the relationship

between the type or volume of regulatory violations and the economic performance of

financial institutions.

Burton and Goldsby (2010) presented a philosophical basis for the connection

between ethics and business practice. No quantitative evidence was put forth. Svensson

and Wood (2008) advanced a conceptual framework for business ethics with no empirical

validation. This lack of evidence is problematic (Lin & Hwang, 2010). Tamny (2010)

recommended that government regulators create complex and restrictive financial

regulations ignoring the reality of failure as a critical economic input. Francis (2009)

postulated that financial executives oppose what is perceived to be government control of

free markets. Financial regulatory agents dispute the notion that increased government

oversight ends free-market capitalism (Rivlin, 2009). Regulatory agents and financial

executives fail to collaborate, which frequently results in ineffective and delayed

legislation and excessive cost (Sherman, 2009).

The relationship between established SEC regulatory compliance or malfeasance

and the economic performance of American, publicly traded financial institutions from

2005 through 2010 was examined in this quantitative descriptive correlation study with a

multiple regression analysis. Regression was used to recognize possible interdependence

among the predictor variables, regulatory compliance or malfeasance, including the

volume and type of malfeasance, and the criterion variable of economic performance.

The use of regression best analyzed the results of the research questions (Kerlinger &

Pedhazur, 1973).

The population is composed of 214 publicly traded financial institutions

categorized under Standard Industrial Classification (SIC) 6211. SIC 6211 includes a

list of 214 security brokers, dealers, and flotation companies (Federal Service Desk,

2010). The study’s sample includes 74 of the 214 financial institutions coded under SIC

6211 (see Appendix A for complete listing).

U.S. financial-service centers are geographically condensed into concentrated

districts that include Midtown Manhattan within New York City and specific regions of

New Jersey (Pohl, 2007). The companies analyzed in the study are headquartered in the

United States. Forty of the companies studied are concentrated in the northeast, and 34

are dispersed throughout the United States. All are listed on a U.S. stock exchange.

Purpose Statement

The purpose of the descriptive correlation study with a multiple regression

analysis was to examine the relationship between established SEC regulatory compliance

or malfeasance and the economic performance of American, publicly traded financial

institutions from 2005 through 2010. Multivariable regression was used to recognize

possible interdependence among the predictor variables of regulatory compliance or

malfeasance, including the volume and type of malfeasance, and the criterion variable of

financial performance of the institutions researched.

The population is composed of 214 publicly traded financial institutions

categorized under SIC 6211. SIC 6211 includes a list of 214 security brokers, dealers,

and flotation companies (Federal Service Desk, 2010). The study’s sample includes 74

of the 214 financial institutions coded under SIC 6211.

U.S. financial-service centers are geographically condensed into concentrated

districts that include Midtown Manhattan within New York City and specific regions of

New Jersey (Pohl, 2007). Forty of the companies analyzed in the study are located in the

northeast region of the United States, and 34 are dispersed throughout the United States;

all are listed on a U.S. stock exchange.

The quantitative method selected for the research is a suitable construct requiring

the analysis of relationships between study variables (Leedy & Ormrod, 2010).

Quantitative investigators focus on the examination of numeric data (Salkind, 2009).

Qualitative researchers concentrate on descriptive words and nonnumeric accounts of a

phenomenon under study (Gelo, Braakmann, & Benetka, 2008). The descriptive

correlation design is appropriate for statistically explaining the relationship between

several interrelated variables (Brace, Kemp, & Snelgar, 2009; Salkind, 2009).

Researchers quantify behavior in an observational design; however, such a design would

not be appropriate in the examination of financial reports and regulatory filings

(Creswell, 2009). Researchers employing a qualitative survey seek responses at a

specific moment in time, which would not be appropriate in an analysis of archived

economic performance and regulatory filings (Christensen, Johnson, & Turner, 2011).

Interviews conducted to draw responses related to company-specific, sensitive

regulatory-malfeasance matters would not likely be authorized by company officials of

the respective organization (Willis, 2007).

Relationships between the study variables of regulatory violations and economic

performance are determined in the study through the analysis of data collected to answer

quantitative research questions and to accept or reject predetermined hypotheses (Leedy

& Ormrod, 2010). The predictor variables, the types and volume of regulatory

compliance or malfeasance, were measured by researching the Electronic Data Gathering,

Analysis, and Retrieval (EDGAR) system of the SEC. The criterion variable, economic

performance, was measured through analysis of archived, audited public financial

records.

Significance of the Study

A challenge encountered by the chief executives of publicly traded financial

institutions is maximizing profits and meeting earnings projections while concurrently

adhering to complex government regulation (Berrone et al., 2007). The Securities Act of

1933 and the Securities and Exchange Act of 1934 were the regulatory result of the 1929

stock-market crash. The 2008 financial crisis has led to the Consumer Protection Act of

2010 (SEC, 2010). Legislators have increased financial regulation as well as enhanced

oversight and broadened regulatory authority. Financial executives encounter new

pressures (Anderson et al., 2011).

Greater transparency of publicly traded companies is needed (Organisation for

Economic Cooperation & Development, 2010). Few researchers have investigated the

relationship between government regulation and economic performance (Burr, 2010;

Edgar, 2009). Researchers have linked financial regulation to stock-market volatility (Li

& Xu, 2008). Ethical commitment and financial performance as well as stock valuation

have been researched empirically. The relationship between ethical responsibility and

economic performance has not been found statistically significant (Choi & Jung, 2008).

O’Brien (2010) questioned the future of financial regulation by asking if corporate

integrity could be enhanced through legislative design.

Significance to Leadership

Financial executives must lead their businesses with the highest level of business

ethics (Harshman & Harshman, 2008). Friedman and Friedman (2010) identified the

selfish nature of many financial executives as a causal factor of millions of lost jobs and

trillions of diminished assets. SEC officials reported misconduct and enforcement

actions in the amount of $1.65 billion as a result of the 2008 financial crisis (SEC,

2011b). Failing to meet ethical leadership responsibilities, 34 officers (i.e., chief

executive officers, chief financial officers, and other senior corporate officers) have been

charged individually with regulatory misconduct. The quantitative study was conducted

to provide a model for ethical leadership. Financial executives may use the study

findings to rationalize compliance within the SEC from a morale perspective.

Nature of the Study

Determining whether a relationship exists between financial regulatory

compliance or malfeasance and economic performance was a critical aim to the design of

the study. A descriptive correlation research design is appropriate to explain the variation

of two or more variates in a statistical manner (Fisher, 2003). The methodical approach

permitted a statistical analysis of whether the economic performance increased or

decreased when regulatory violations increased or decreased in a somewhat predictable

fashion (Leedy & Ormrod, 2010).

Several statistical-analysis techniques are available to communicate the results of

the data (Fisher, 2003). Descriptive statistics were used to detail the data. Correlation

analysis was employed to express a quantitative association between variables (Black,

1999). Multiple regression analysis allowed the prediction of the “collective and separate

contributions” (Kerlinger & Pedhazur, 1973, p. 3) of two or more variables, regulatory

compliance as well as volume and type of regulatory malfeasance, to the variation of the

dependent variable of economic performance.

Investigators employing descriptive correlation techniques collect data to test

hypotheses (Black, 1999). The numeric results are reported for one or more variables on

the units of analysis preestablished for the respective study (Simon, 2010). Descriptive

meta-analysis researchers synthesize available studies to arrive at a summary (Neuman,

2011). A causal-comparative investigator seeks to establish a cause-effect relationship

among study variables (Simon, 2010). Experimental designers use control and

experimental study groups to understand the cause-effect relationship between variables.

The cause variable is under the control of the researcher (Tukey, 1977).

One unit of analysis was investigated in the study to measure effectively the

theoretical concepts advanced in the research (Neuman, 2011). Financial institutions

were analyzed through a review of public, audited financial records. Those classified

under SIC 6211 are described as listed security brokers, dealers, and flotation companies

(Federal Service Desk, 2010). An analysis of specific institutions is appropriate for a

study of broader perspectives and influences (Roche, 1999).

Qualitative researchers explore individual or group meaning assigned to a

communal or anthropological problem. Qualitative research design is appropriate when

information is limited on a topic and when variables are unknown (Leedy & Ormrod,

2010). The goal of qualitative research is to understand a specific circumstance (Willis,

2007). A research design is the structural plan that interconnects the study’s approach,

philosophy, and assumptions to the method (Gelo et al., 2008). It provides credible

answers to preestablished research questions. Researchers employing a qualitative

research approach use naturalistic study designs to examine behavior within natural

settings (Creswell, 2009).

A qualitative design was not appropriate for the study because the research

questions were not behaviorally observable (Salkind, 2009). Interviews during which the

researcher seeks responses to sensitive malfeasance matters likely would not be

authorized by company officials (Willis, 2007). A survey design would not be

appropriate in analyzing archived economic performance and regulatory filings

(Christensen et al., 2011). Neuman (2006) confirmed that when researchers are focused

on topics involving information collected by large bureaucratic organizations, archival

study is appropriate. Researching regulatory violations on the SEC EDGAR database is

effective. Archival research is also appropriate for the assessment of economic

performance (Leedy & Ormrod, 2010).

The study sample was comprised of 74 publicly traded financial institutions

categorized under SIC 6211. SIC 6211 includes a list of 214 security brokers, dealers,

and flotation companies (Federal Service Desk, 2010). All are listed on a U.S. stock

exchange. Forty of the studied companies are within the northeast region of the United

States, and 34 are geographically dispersed throughout the United States.

The representative nature of a sample is important to any study (Rubin, 2008).

Various categories of survey results may translate to the broader financial services

population (Black, 1999). Seventy-three companies listed under SIC 6211 met the

sample’s qualifying criteria; all were used in the data analysis process. One company

was included in the sample that was misfiled on EDGAR without an SIC code. Upon

further investigation, the company qualified under SIC 6211 and was included in the

study. One hundred and thirty-nine of the 214 listed companies under SIC 6211 were not

used in the study.

Eleven of the listed companies were sold, acquired, or merged during the study’s

period. Fifty-six of the listed companies did not submit financial filings between 2005

and 2010. One company became private and was no longer required to post financial

records to EDGAR. Six of the listed companies were outside the United States and did

not meet the sample criteria. Four of the companies withdrew their registration and

therefore had no archived records. Fifteen of the listed companies were recorded in the

EDGAR database under more than one name. Forty-six of the SIC 6211 listed

companies were corporate trusts, portfolios, affiliated stockholders, or hedge funds and

were not required to post an annual report or other income statement records.

Research Questions and Hypotheses

Researchers of quantitative, descriptive correlation studies use preestablished

research questions to pursue answers pertaining to whether a relationship exists between

two or more variables is significant (Kerlinger, 1986). The investigator applies the

research question as guidance for the inquiry (Black, 1999). Research questions in a

quantitative design are related to characteristics of the variables (Christensen et al.,

2011). Quantitative researchers form hypotheses to provide a conjectural explanation for

the phenomenon under study (Black, 1999). What will be performed in the research,

rather than how, is descriptive of good hypotheses (Salkind, 2009). Null hypotheses state

the negative perspective of the same hypotheses often referred to as alternate hypotheses

(Christensen et al., 2011). The research hypothesis is a statement of the relationship

between variables (Black, 1999). Accepting or rejecting a hypothesis is determined by

the findings of the data analysis. Three research questions and corresponding hypotheses

were formulated for the study.

Research Question 1 asked, “What is the relationship between the economic

performance of 100 publicly traded financial institutions coded under SIC 6211 and

associated government regulatory compliance or malfeasance?”

Null Hypothesis 1 stated that no statistically significant relationship exists

between the economic performance of 100 publicly traded financial institutions under

SIC 6211 and associated government regulatory compliance or malfeasance.

Alternate Hypothesis 1 stated that a statistically significant relationship exists

between the economic performance of 100 publicly traded financial institutions under

SIC 6211 and associated government regulatory compliance or malfeasance. Several

types of regulatory violations exist. A company may misrepresent or omit important

information surrounding securities. Price manipulation of stock-market securities, or

violation of insider trading rules, as set forth by the SEC, represent other types of

violations. Company executives have stolen customer funds or securities, breaching their

responsibility to treat customers fairly (Rhee, 2009).

Research Question 2 asked, “Does a relationship exist between the economic

performance of 100 publicly traded financial institutions under SIC 6211 and the types of

associated regulatory violations?”

Null Hypothesis 2 stated that no statistically significant relationship exists

between the economic performance of 100 publicly traded financial institutions under

SIC 6211 and the type of associated regulatory violations.

Alternate Hypothesis 2 stated that a statistically significant relationship exists

between the economic performance of 100 publicly traded financial institutions under

SIC 6211 and the type of associated government regulatory violations. The types of

regulatory violations are shifting from the historically large categories of public company

misstatements and insider trading. Ponzi schemes and Foreign Corrupt Practices Act

violations have gained prominence (Larsen, Buckberg, & Overdahl, 2011).

Research Question 3 asked, “Does a relationship exist between the economic

performance of 100 publicly traded financial institutions under SIC 6211 and the number

of associated regulatory violations?”

Null Hypothesis 3 stated that no statistically significant relationship exists

between the economic performance of 100 publicly traded financial institutions coded

under SIC 6211 and the volume of associated regulatory violations.

Alternate Hypothesis 3 stated that a statistically significant relationship exists

between the economic performance of 100 publicly traded financial institutions under

SIC 6211 and the volume of associated regulatory violations. The number of regulatory

enforcement actions has been on a steady upward trend since 2006 (SEC, 2011b). Such

violations may increase as new regulations and enforcement are introduced.

Operational Definition of Variables

Legislators enact the laws under which public financial institutions are governed.

SEC regulatory agents write the rules and policies under which the institutions are

monitored (SEC, 2008). Regulatory violations are defined in the following four broad

categories by the SEC: (a) misrepresentation or omission of material information

surrounding public securities, (b) irresponsible or unfair treatment of customers, (c)

stealing customer funds or securities, and (d) security price manipulation and insider

trading. The variable of regulatory violations was measured in the study using archived,

audited data from the SEC’s publicly accessible Web site. The SEC requires public

disclosure of regulatory malfeasance (SEC, 2008). The types and number of regulatory

violations were examined for the calendar years of 2005 through 2010.

The basic measures of a company’s economic performance include revenue, pre-

tax and net income. The financial ratios of gross profit margin and after-tax profit margin

were calculated to compare appropriately the large- and small-companies’ performance.

Revenue is the total amount of money earned by a company for its goods and services.

The gross profit of a business, or profit, is the difference between revenue and

operational costs. The after-tax profit margin is an indicator of the percentage of money a

company earns per dollar of revenue (Kalluru & Bhat, 2009). Accountants report profits

on company income statements, which are financial statements that report how revenue is

transformed into net income or profits (Ross, Westerfield, Jaffe, & Jordan, 2009). Public

companies are required to file audited income statements annually (SEC, 2008). The

variable of economic performance in the study was measured using audited financial

statements from the publicly accessible EDGAR database maintained by the SEC.

Conceptual Framework

Framing research on well-established theory is critical for quantitative study

(Black, 1999). Theories are the basis of research studies (Alvesson & Sandberg, 2011).

Organizational boundaries are secured by the theoretical framework. Research questions

and hypotheses are developed and the data-collection procedure is conceived within

theoretical boundaries (Pajares, 2007). In the descriptive correlation study, the

relationship between SEC government regulatory compliance or malfeasance and the

economic performance of 100 financial institutions were described. Broad theoretical

explanations of economic performance and financial regulation grounded the research

(Kerlinger, 1986). Germinal, historical, and current literature was reviewed. Important

issues and perspectives were explored as well as controversies. A discussion of how the

research fits within existing related studies was also presented.

Germinal and historical theory. Economic and public-choice theories as well as

leadership theories served as the broad theoretical framework for the quantitative

research. The designs of behavioral economic theorists were reviewed (Fung, 2010) and

an exploration of agency theory was conducted. Agency theorists study the alignment of

stakeholder interests (Nyberg, Fulmer, Gerhart, & Carpenter, 2010). Existing theoretical

research on ethical leadership was analyzed (Brown & Mitchell, 2010).

Economic theory. Economic theorists attach dominant importance to capital

accumulation and growth (Kim, 2009). The germinal economic text supporting the

common concept that “businesses are in business to make money” is the Smith (1776)

publication entitled An Inquiry into the Nature and Causes of the Wealth of Nations.

According to Sturn (2010), Smith advanced a clear view of “unfettered self-interest and

free marketeering” (p. 263). The fundamental tenets of capitalism espoused by Smith

have been subject to serious critique (Levin, 2010). His economic theory was expanded

by Robert Commons to include the economics of institutions, or the conceptualization of

economic behavior (as cited in Peach, 2009). Advocates of institutional economics treat

the economy as a social-science mechanism. Both behavioral psychology and knowledge

are the basis for social action. Legal and economic processes are foundational elements

to understanding basic organizational problems (Streeck, 2009).

The terms managed equilibrium (Commons, 1924) and coordinated capitalism

(Streeck, 2009) are descriptive terms for collaborative free markets and state and

institutional reform (Kaufman, 2007). According to Commons (1924), economics should

not separate “ethics, public welfare, or national public interest as a postscript, different

from economic theory” (p. 237). Equilibrium ideology is complex (Caldari, 2010) and

conflicts with the Smith free-market capitalism (as cited in Sturn, 2010).

Public-choice theory. Public-choice theorists focus on a sequence of common-

sense premises (Buchanan & Tullock, 1962). Member interests are self-serving and

constrictive. Collective action problems are easily overcome in narrowly defined groups

(Mashaw, 1997). Legislators are motivated to serve solely these narrow interests, and

regulators are controlled by legislators. Regulatory agents are motivated to please self-

interested members rather than the public (Croley, 2008).

Powell and Stringham (2009) argued that government enforcement is weak,

corrupt, or absent. Because of government regulatory imperfections, the interests of

public stakeholders are at risk or weakened, as demonstrated by the 2008 financial crisis

(Blundell-Wignall et al., 2008). Benmelech and Moskowitz (2010) contended that

government enforcement motives are contradictory to the Powell and Stringham theories.

Public-interest theorists have argued that government intervention is an inefficient social-

welfare reformation. The design of effective regulatory and corporate-governance rules

is questioned regarding whether the oversight is in support of, or in conflict with, public-

choice theory (O’Brien, 2010).

Leadership theory. Agency cost theorists have propounded that the potential for

managerial malfeasance applies when the interests of managers and owners deviate

(Nyberg et al., 2010). An institutional vulnerability can be exposed in the absence of

moral constraint (Heath, 2009). Employed executives can be more motivated than

owners to act in the best interests of the respective company and its stakeholders (Miller

& Sardais, 2011).

The formative period of agency cost theory was the early 1970s. The construct

was developed by Michael Jensen and William Meckling during those years (Lan &

Heracleous, 2010). Agency cost theorists have purported that knowledgeable agents act

upon opportunism unless controlled or incentivized not to do so (Heath, 2009). Shirking

responsibility and perquisite consumption of agents and owners may be opportunistic

behavior representative of a lack of alignment (Nyberg et al., 2010). Stewardship

theorists present a contrarian view (Miller & Sardais, 2011). Not all agents are created

equally. Although some executives strive to be effective institutional stewards, some are

blamed for the irresponsible risk taking and short-term decision making that initiated the

2008 financial crisis (Blundell-Wignall et al., 2008).

Pratch (2009) commented that chief executives are accountable to a multiplicity

of conflicting agency interests. Moral leadership, under the guidance of agency theory, is

possible when leaders engage in a deliberate decision-making process of careful analysis

and principled judgment (Rhode, 2006). To uphold their responsibility to all

stakeholders, financial executives must adhere to a personal code of ethics that considers

and respects others external to their traditional advisors (Cosans, 2009). Williams and

Ryan (2007) contended that executives will exploit stakeholder vulnerabilities by

manipulating information.

Current theory. Researchers have supported and scrutinized germinal and

historical studies throughout the literature. Miller and Sardais (2011) contended that

agency theory is outdated and agents are not selfish opportunists seeking to exploit

owners or principals. Lan and Heracleous (2010) contested that agency theory lacks

supporting empirical research. Novak (2010) disagreed with regulating the economy

under the construct of public-choice theory. In the absence or limitation of government

regulation, public-choice scholars purposed new ways to control opportunistic behaviors

(Powell & Stringham, 2009). Socialistic economists have dismissed the Smith capitalism

(Levin, 2010). Ethics researchers have described a deterioration of public confidence in

American business leadership. Burton and Goldsby (2010) attributed a breach of

leadership ethics to diminished confidence.

The descriptive correlation study was designed to examine the relationship

between regulatory compliance or malfeasance and the economic performance of

publicly traded financial institutions. The research is expected to support the findings of

other research within the field. Public-choice theory is aligned to the accord and

controversies of increased government regulation (Powell & Stringham, 2009).

Regulatory compliance and malfeasance is a business-ethics concern. Financial

executives are under tremendous pressure to deliver earnings, which may result in agency

conflict (Berrone et al., 2007).

Definition of Terms

Financial terminology can be confusing and overlapping (Brady, 2007). To

convey meaningful information and to ensure an understanding of the terms used in this

study, the following definitions are offered. All terms are defined for purposes of this

research.

Annual reports are documents disclosing business activities for a calendar year,

including strategy and acquisitions as well as new investments. Balance sheets are forms

that document the financial condition of a business as of a specific date. Revenues and

expenses for a specific period are summarized on the income statement. Cash-flow

statements are ledgers indicating the inflow and outflow of all company funds. By

analyzing these documents, investors can determine the liquidity, financial leverage,

efficiency, and profitability of any business (Wardrope, 2008). Individual and

institutional investors use key measures from quarterly and annual reports posted to

EDGAR as form 10-K to ascertain the value of an investment.

Economic performance is a broad financial term that includes revenue, gross

profit, and net profit. Revenue is the total amount of money earned by a company for its

goods and services. The gross profit of a business is the difference between the

company’s revenue and cost of sales. Net profit is a calculation that reflects the

difference between the company’s revenues and all expenses, including the cost of sales

(Kalluru & Bhat, 2009).

Financial ratios are indicators of a company’s economic performance and

financial situation. Gross profit margin and after-tax profit margin are calculated from

information provided on the income statement. The ratios are positive if the company has

generated profits. The margin formulas for the calculations of gross profit margin and

after-tax profit margin follow:

GPM = R – CGS R

where:

GPM = Gross profit margin

R = Revenue

CGS = Cost of goods sold

and

AFPM = R – CGS – T R

where:

AFPM = After-tax profit margin

R = Revenue

T = Taxes

Financial crisis is applied broadly to situations in which the value of financial

institutions or assets drops rapidly. Economists refer to the 2008 financial crisis as the

period between the global financial “meltdown” beginning in 2008 and the subsequent

recession through 2010 (Fehr, 2010).

A listed company, also referred to as a listed security, is described as a publicly

traded company whose stock is traded on a particular U.S. stock exchange (Baruch &

Saar, 2009). Listed companies are required by the SEC to provide public access to

corporate information. Free public access is allowed to the SEC (2008) database,

EDGAR, of corporate information including financial records and disclosure filings.

Financial professionals of listed companies post to EDGAR by a SIC. In 1987, the U.S.

Department of Commerce established a standardized method of numerically classifying

business and industrial activities.

SIC 6211 refers to security brokers, dealers, and flotation companies. These

business establishments are primarily engaged in the purchase, sale, and brokerage of

securities, originating and underwriting securities, and issuing shares of money-market

and mutual funds as well as unit investment trusts and face-amount certificates.

Companies listed under SIC 6211 are generally referred to as investment bankers.

Mandatory disclosure includes the reporting of regulatory violations such as

market-price manipulation or insider trading. Market-price manipulation, also referred to

as price manipulation, is described as a deliberate attempt to interfere with the free

market and create false or misleading appearances with respect to the price of a security

(Kyle & Viswanathan, 2008). Insider trading is described as the buying and selling of

publicly traded securities by an insider based upon the benefit of insider information.

Insider trading is also referred to as insider dealing (Prentice & Donelson, 2010).

Regulator-led governance refers to the complex and extensive legislation that

grants regulatory authority for the creation of regulations and the investigation of

violations (SEC, 2008). The U.S. financial markets are governed by a regulator-led

approach (Waring, 2007).

A regulatory agency is a government body formed and mandated under the terms

of a specific legislative act or statute. It is also referred to as a regulatory authority or a

regulatory body. The SEC is the financial regulatory agency; its creation was facilitated

by the Securities Act of 1933 and the Securities Exchange Act of 1934. The Commission

is responsible for safeguarding compliance with the Acts’ provisions.

Regulatory transparency is the mandatory disclosure of methodized and factual

information by listed companies listed on a U.S. stock exchange (Weil, Fung, Graham, &

Fagotto, 2006). With the goal of regulation by transparency, SEC regulators mandate

listed companies to post publicly financial disclosure to EDGAR (SEC, 2008).

Shareholder wealth is directly aligned with the financial performance of the

respective company. Chief executives are responsible for maximizing shareholder wealth

(Pratch, 2009). Several performance metrics are used. Profit margin is the difference

between the price received by a company for its product or services and the total

production costs. Earnings before interest, taxes, depreciation, and amortization, or

EBITDA, are a measure of the underlying performance of a company (Bishop, 2004).

The price-to-earnings ratio is calculated by dividing the share price by earnings per share

to reach a “snapshot” of the value investors place on a listed company (Brady, 2007).

Stock value is reflective of the financial performance of a company (Bauer &

Braun, 2010). Top-decile rankings are a result of strong financial performance. Decile

rank refers to the measure of performance on a scale of 1 through 10. A measure of 1

denotes the return of a security in the top 10% of similar securities. A measurement of 10

is indicative of a bottom 10% placement (Brady, 2007).

Assumptions

Research assumptions are “self-evident truths, the sine qua non” of the research

study (Leedy & Ormrod, 2010, p. 5). Effective researchers validate all assumptions

(Black, 1999). The researcher does not attempt to control a research study’s assumptions

(Fisher, 2003). Alvesson and Sandberg (2011) referenced assumptions as differing in

depth and scope. The first underlying assumption to be accepted in the construct of the

study was that the SIC 6211 listed companies had posted all required filings and

mandatory disclosures to the SEC and EDGAR for 2005 through 2010. Greenstone,

Oyer, and Vissing-Jorgensen (2006) reported that the percentage of listed companies

complying with all disclosure requirements has increased over time. The highest

compliance level reached was 80.8%.

Because the study was reliant upon archival documents, the second underlying

assumption to be accepted during the investigation was that the SIC 6211 listed

companies had prepared accurate annual financial reports. A difficult challenge

encountered by accounting researchers is the measurement of the quality of financial

reporting (Chambers, Hermanson, & Payne, 2010). In 2010, financial-restatement

disclosures increased by 7.6% with an average adjustment down of $5.9 million in

income (Cheffers, Whalen, & Usvyatsky, 2011).

The relevance of academic research to practicing executives has been a topic of

debate for many years. Academic researchers have argued that executives do not take

full advantage of management studies as a source of knowledge (Hughes, Bence, Grisoni,

O’Regan, & Wornham, 2011). The third underlying assumption in the study was whether

executives may view the results of academic research as relevant in a practical sense or

significant. Policy makers have turned to academic studies to supplement government

research (U.S. Government Accountability Office, 2009). They are informed of a myriad

of factors that exceed the policy itself. Academicians may offer input that brings diverse

views to a policy forum (Price, Verhulst, & Morgan, 2007). The significant value of the

study to policy makers was the fourth underlying assumption to be accepted during the

course of the research.

Scope, Limitations, and Delimitations

The descriptive correlation study with multiple regression analysis was conducted

with the intent to describe the relationship between SEC governmental regulatory

compliance or malfeasance and economic performance. The scope of the study

encompassed the examination of the archived public records of 74 American financial

institutions. The selected sample was limited to the population of United States’ financial

institutions listed under SIC 6211.

Generalization is accepted widely as a quality standard in quantitative research.

The term refers to an analytical process that incorporates the development of broad

interpretations from narrow cognizance (Polit & Tatano Beck, 2010). The answers to

two questions facilitate determination of generalizability. The first question is whether

general inferences can be drawn from the data. The second is whether the results of the

respective research are valid not only for the study, but also for the population upon

which the research questions were based (Mayring, 2007). The relationship between

SEC regulatory compliance or malfeasance and economic performance was examined in

the study by analyzing the public records of 74 American financial institutions. The first

aspect involving generalization of the research was whether broad inferences could be

formulated from the targeted study of 74 financial institutions categorized under SIC

6211 and applied broadly to all SIC codes. The second aspect involving generalization

was whether the results of the study would be applicable to the 74 financial institutions

examined as well as to all financial institutions classified under SIC 6211.

Limitations. Limitations of a research study are weaknesses potentially serving

as obstacles in generalization of the findings to other populations (Creswell, 2009). A

limitation of this research concerned a time restriction. The examination analyzed

archived data solely for the years 2005 through 2010. Additionally, the archived

financial records and regulatory reports may have been unreliable (Cheffers et al., 2011).

Consequently, another limitation of this study was the potential for unreliable data

leading to unreliable conclusions. Economic performance was examined as measured by

gross profit margin and after-tax profit margin. Both financial ratios are calculated from

information reported on the income statements of the companies examined.

Descriptive-correlation researchers are limited in their ability to infer causality

(Christensen et al., 2011). Statisticians using correlation alone cannot imply causality

(Rubin, 2008). A causal explanation is reflective of a cause-effect relationship among

variables (Black, 1999). Statisticians using a multivariate model identify a more accurate

estimate of predictor and criterion variables than do those applying a single-variable

explanatory model (Brace et al., 2009). To study a variable scientifically, quantitative

researchers must identify the sources of the variable’s variation (Kerlinger & Pedhazur,

1973).

The study was delimited to a sample of 74 financial services firms listed under

SIC 6211 and geographically located in the United States. SEC regulators index more

than 15,000 listed securities with broad geographic dispersion. The restricted sample

may limit the statistical accuracy of the results (Neuman, 2006). The financial services

sector is experiencing heightened regulatory scrutiny as a result of the 2008 financial

crisis (Rivlin, 2009). All industry sectors are expected to learn from the financial crisis

and implement reforms (Paulson, 2010). The potential dismissive reaction by certain

sectors was also a limitation of this quantitative study.

Delimitations. Research delimitations are stated to clarify where the scope of a

study ends (Leedy & Ormrod, 2010). Creswell (2009) referred to delimitations as a

narrowing of the research to a specific research design. Delimitations of the study are

foreseen. Three stock exchanges host thousands of listed securities. The National

Association of Securities Dealers Automated Quotations lists 2,820 securities. The New

York Stock Exchange follows 3,255 publicly traded securities. The American Stock

Exchange is comprised of 536 listed securities (National Association of Securities

Dealers Automated Quotations, 2011). Although a sample of 74 financial services firms

under SIC 6211 were selected for examination in the study, the population and industry

classification may have limited generalization of the results to other companies within

other industry sectors.

Summary

The financial regulatory framework is a causal factor in the 2008 financial crisis

and subsequent failure of many American financial institutions (Blundell-Wignall et al.,

2008). Regulatory violations are on the rise (SEC, 2011b). A need has been identified

for greater transparency of publicly traded companies (Burr, 2010), and regulators are

developing new government-malfeasance policies. Overlooked in the existing empirical

research is evidence of the influence of regulatory compliance or malfeasance on

economic performance (Choi & Jung, 2008).

The purpose of this descriptive correlation study with a multiple regression

analysis was to explore the relationship between SEC government-regulatory compliance

or malfeasance and economic performance. The public records of 74 financial

institutions were analyzed. By understanding the relationship between regulatory

compliance and economic performance, financial leaders may use the findings to support

policy reform. The literature review conducted for the research details critical points

surrounding current knowledge and research methodologies related to the research topic

(Leedy & Ormrod, 2010). The review is organized thematically with a critical evaluation

and synthesis of existing related literature.

Chapter 2: Literature Review

A literature review is conducted to provide background and context for a current

study (Randolph, 2009). Leedy and Ormrod (2010) maintained that such a review is an

acknowledged source of new perspectives and arguments from which conclusions are

drawn regarding existing conceptualizations (Salkind, 2009). Creswell (2009) views the

literature review as a basis of comparison between current study and the results of prior

research. The purpose of this descriptive correlation study with a multiple regression

analysis was to examine the relationship between established SEC regulatory compliance

or malfeasance and the economic performance of American, publicly traded financial

institutions. The public records of 74 financial institutions were analyzed from 2005

through 2010 along with gaps in existing literature. A chronological, evaluative, and

synthetic analysis of the literature reviewed is presented.

Overview

This review of existing literature presents both historical and current research

regarding financial regulation and economic performance. The review includes material

accessed from the University of Phoenix online library of peer-reviewed journals and

abstracts, as well as published research studies and dissertations. The databases of the

Chicago Public Library and the University of Illinois-Chicago research library were also

searched for relevant studies. Additionally, the text and bibliographic databases of

EBSCOhost and Gale PowerSearch, as well as ProQuest, were used to complete a

thorough search of relevant sources.

This literature review is a collection and analysis of numerous reports and filings

(Leedy & Ormrod, 2010). The SEC (2008) EDGAR online database is a public-access

repository of corporate information. The financial and operational information of

researched companies as well as regulatory events were accessed through EDGAR. The

reports were supplemented by information gathered from the Dun & Bradstreet Key

Business Ratios and ProQuest Historical Annual Reports. Historical analyst reports were

accessed through the Morningstar Investment Research Center, an online database of

financial information related to the New York Stock Exchange, American Stock

Exchange, and the National Association of Securities Dealers Automated Quotations.

Two Web sites were accessed in the compilation of this review of related

literature. A nonprofit and nonpartisan research organization, the Ethics Resource Center

is dedicated to independent research on ethics (Harned, 2010). The Center for the Study

of Financial Regulation is a nonprofit academic organization studying the economics of

financial regulation. Quantitative literature typically includes an exhaustive review of

existing literature to present a neutral representation of facts (Leedy & Ormrod, 2010).

Contrary research findings and alternative interpretations are presented (Randolph, 2009).

Historical and scholarly sources from within the five years preceding the respective study

are referenced.

Background

Regulator-led governance is a behavior-modification phenomenon (Pellerin et al.,

2009). Regulators confirm the necessity of regulation to serve public interests and

support confidence in the stock markets (Thomadakis, 2007). However, self-regulatory

advocates believe regulation may do more harm than good (Braithwaite, Coglianese, &

Levi-Faur, 2007). Since 1852, financial regulators have responded to economic events

and market failures with reactive laws and regulations (Sherman, 2009). Legislators were

prompted by the Civil War to pass the National Bank Act of 1863. The Federal Reserve

Act of 1913 and the Grain Futures Act of 1922 are attributed to the financial panics and

instability of the late 1800s and early 1900s (U.S. Government Accountability Office,

2009).

Legislators wrote the Glass-Steagall Act of 1933, as well as the Securities Act of

1933 and the Securities Exchange Act of 1934, following the Great Depression (Kroszner

& Rajan, 1993). The enactment of the Investment Company Act followed in 1940 (State

of Wisconsin Department of Financial Institutions, 2010). The Financial Institutions

Reform, Recovery and Enforcement Act of 1989, the Federal Housing Enterprises

Financial Safety and Soundness Act of 1992, and the 1996 National Securities Markets

Improvement Act were all subsequent to the savings-and-loan crisis of the 1990s (U.S.

Government Accountability Office, 2009). Legislators followed by signing the Gramm-

Leach-Billey Act of 1999 (Anderson et al., 2011) and the 2002 Sarbanes-Oxley (SOX)

Act into law (Rashkover & Winter, 2005).

The 2008 financial crisis is accredited with “triggering” the Dodd-Frank Wall

Street Reform and Consumer Protection Act of 2010 (MacDonald & Lettow, 2010). The

resulting regulatory system is complex and fragmented (U.S. Government Accountability

Office, 2009). Financial regulation is cyclical and regulators respond to wrongdoings by

enacting laws. Financial executives, in turn, respond to regulations by innovating new

products and distribution strategies.

Related Legislation

Financial regulation in the United States has been recorded as early as 1852 when

the state of Massachusetts required the registration of railroad securities (State of

Wisconsin Department of Financial Institutions, 2010). In 1911, Kansas legislators

enacted the first broad securities law requiring securities registration (Alvarez & Astarita,

2001). This set the precedent for 23 states to enact “blue-sky laws” during the following

two years, named after the blue skies of Kansas. The early securities statutes were crude

and incomplete (State of Wisconsin Department of Financial Institutions, 2010). The

first test of the blue-sky laws by the U.S. Supreme Court under the U.S. Constitution

occurred in 1917. The federal-court judge upheld the blue-sky securities laws and

affirmed that they were within the power of the states. However, the judge concluded

that the state laws created barriers to capital formation (Hall v. Geiger-Jones Co., 1917).

The maximum legal interest rates on loans are regulated at the state level and

referred to as usury laws (Coco & DeMeza, 2009). Such laws within the United States

date back to 1841. Massachusetts legislators confirmed an 8% maximum legal interest

rate (Benmelech & Moskowitz, 2010). Mill (1891) posited that usury laws interfere with

the course of industrial transaction. Holmes (1892) provided data to establish both

maximum legal rates and penalties. Originative market-participant contractors

commonly circumvent financial regulation (Wright, 1949). Friedman (1953) surmised

that states with more liberal usury laws drained the capital of more restrictive states. He

questioned if governmental regulation stifled a market economy and jeopardized

American free society (Friedman, 1978). North (1990) posited that camouflaged

contracts would not exist in the absence of usury laws. Coco and DeMeza (2009)

defended usury laws by demonstrating the merit of interest-rate caps through empirical

research. DeConto (2010) argued against usury laws by commenting that credit-card

companies make enticing offers before subsequently increasing interest rates. Usury

legislators create mandates perceived to advocate for investors while detractors seek

circumvention.

Securities Act of 1933 and Securities Exchange Act of 1934. Legislators

established no legal requirements forcing public companies to disclose information to the

financial markets prior to 1933 (Barnett, 1934). Disclosure of financial results was

purely voluntary. Financial preparers customized income statements and balance sheets

and decided whether to have the financial statements audited (Li & Xu, 2008). The rapid

growth of the financial markets during the 1920s was displaced by the stock-market crash

of 1929 and the resultant Great Depression (White, 1990). Lawmakers held

congressional hearings to analyze the cause of the stock-market crash (Li & Xu, 2008).

The Pecora Commission of 1933 and 1934 was established under the U.S. Senate

Banking and Currency Committee to uncover the causes of the Great Depression

(Borgers, 2009). Lawmakers blamed large-scale financial frauds. The Commission

denounced stock manipulation by “men . . . of economic power and wealth” (“Pecora

Denounces,” 1933, p. 33). These events were the impetus of the first federal securities

legislation (State of Wisconsin Department of Financial Institutions, 2010).

The initial issuance and registration of securities was governed by the Securities

Act of 1933, referred to as the truth in securities law. It was the first federal securities

legislation (SEC, 2010) and its mandates required public disclosure of all new public

issues sold on or after July 27, 1933 (S. Res. 74, 1933). Investors had access to financial

and other narrative regarding securities offered for public sale, and the fraudulent sale of

new issues was proscribed (SEC, 2010).

Landis (1934) presented opposing arguments to modification of the Securities Act

of 1933. In August 1934, the Securities Exchange Act of 1934 was passed to amend the

Securities Act of 1933 (Li & Xu, 2008) and congressional lawmakers created the SEC.

The securities industry, including brokerage firms and transfer and clearing agents, is

regulated by the broad authority of the SEC (S. Res. 881, 1934). The SEC (2010) is

charged with oversight of self-regulatory agencies such as the New York Stock Exchange

and the American Stock Exchange as well as the National Association of Securities

Dealers Automated Quotations. Legislators of the Securities Act of 1933 inaugurated the

U.S. regulator-led governance of publicly traded securities.

The objective of the developers of the Securities Exchange Act of 1934 was to

regulate postdistribution securities trading. Public disclosure of material information was

determined to be mandatory (State of Wisconsin Department of Financial Institutions,

2010). Corrective actions for securities trading fraud and market manipulation were

enacted. Insider trading was defined and mandated as illegal (S. Res. 881, 1934). Thus,

legislators of the Securities Exchange Act of 1934 federalized financial safeguards

(Dombalagian, 2010). The Securities Act of 1933 was substantially amended by

constituting the Securities Exchange Act of 1934, but not without regulatory controversy

(Barnett, 1934).

In two separate studies, Stigler (1965, 1960) tested whether the Securities Act of

1933 influenced the returns actualized by investors through new stock issues by

comparing the investor returns before and after enforcement of the SEC mandatory

disclosure legislation. No significant difference was found in realized returns. Benston

(1973) examined the import of the periodic disclosure mandated by the Act. The stock

performance of voluntary-disclosure firms versus nondisclosure firms indicated that the

nondisclosure firms did not outperform the voluntary disclosure firms. Pankoff and

Virgil (1970) conducted a controlled laboratory study of security analysts. One control

group made decisions to purchase stock based upon disclosed financial statements. The

other control group followed a buy-and-hold strategy. Pankoff and Virgil found that the

returns of the first control group were not as high as those realized by the second control

group.

Authors of “An economic analysis of Section 16(b) of the Securities Act of

1934” (1976) analyzed the Securities Act of 1933 and the Securities Exchange Act of

1934 in terms of the collective effects of this legislation on stock-market volatility.

Jarrell (1981) studied corporate-bond performance both pre-SEC and post-SEC and found

that default risks declined following SEC implementation of mandatory disclosure.

Simon (1989) found a substantial reduction in stock-price variance during the post-SEC

period.

Empirical researchers have offered evidence both in advocacy and opposition of

the Securities Act of 1933. Abnormal returns may not be a direct result of regulation

(Smith, Bradley, & Jarrell, 1986). Simon (1989) examined the influence of the Securities

Act of 1933 on the performance of new issues. Abnormal returns were significantly

lower following the legislation. Li and Xu (2008) studied daily returns of the New York

Stock Exchange from 1890 to 1970 and proved that both the Securities Act of 1933 and

the Securities Exchange Act of 1934 substantially reduced market volatility. Mahoney

and Mei (2008) reported that the mandated disclosures of the Securities Exchange Act of

1934 did not contain value-relevant information on listed companies.

Glass-Steagall Act of 1933. Congressional lawmakers passed additional

legislation pertaining to securities regulation (Anderson et al., 2011; Li & Xu, 2008).

The Glass-Steagall Act of 1933, also known as the Banking Act of 1933, limited the

alleged conflict of interest within commercial banks underwriting stocks and bonds

(S. Res. 162, 1933). Following the stock-market crash of 1929, nearly 5,000 banks—one

in every five banks within the United States—failed (“Day-By-Day Chronology,” 1933).

Commercial banks were denounced as overly speculative (Asher, 1981). Legislators of

the Glass Steagall Act introduced new regulations to the securities markets (Anderson

et al., 2011). Commercial bankers were banned from underwriting securities (Tabarrok,

1998). Bank deposits were insured, while federal credit control was ensured by creating

the Federal Deposit Insurance Corporation (Kroszner & Rajan, 1993). Supreme Court

judges supported the disjunction of commercial and investment banking activities in a

brief filed by the First National City Bank in 1970 (Hendrickson, 2001). Early opponents

of the Glass-Steagall Act argued that the new legislation would result in a loss of profits

(Lopez, 2010). Thirty-six percent of national bank profits were derived from investment-

banking activities (“Day-By-Day Chronology,” 1933).

Mester (1996) contended that, during the 1920s and early 1930s, empirical studies

neither confirmed nor negated commercial and investment-bank conflicts. Puri (1994)

demonstrated that default rates adjusted for the age of issues. The default rates of new

issues underwritten by a commercial bank were significantly lower than those

underwritten by an investment bank. Kroszner and Rajan (1993) completed a matched-

sample test of issues collateralized during the first quarters of 1921 through 1929. Fewer

aggregate defaults were reported among the bonds underwritten by a participating

commercial bank than by the investment bank. Ang and Richardson (1994) determined

that underwritten issues by financial institutions not classified as either a commercial or

investment bank outperformed those underwritten by either a commercial or investment

bank.

Benston and Kaufman (1996) investigated the evidence used to justify the

enactment of the Glass-Steagall Act of 1933. By reexamining congressional-hearing

documents and other historical transcripts, Benston and Kaufman found that allowing

commercial and investment banking to combine services did not lead to investor fraud.

Grande, Puri, Saunders, and Walter (1997) examined debt securities underwritten by

subsidiaries of bank holding companies relative to those underwritten by investment

banks. No conflicts of interest existed in the comparative analysis of securities offered

by commercial versus investment banks.

Investment Company Act of 1940. Congressional legislators enacted the

Investment Company Act of 1940 to regulate specific types of financial businesses (State

of Wisconsin Department of Financial Institutions, 2010). They claimed the national

public interest was adversely affected when investment companies were not subject to

sufficient independent examination (S. Res. 789, 1940). The Act allows legislative

oversight of face-amount certificate companies and unit-investment trusts as well as

management companies (i.e., mutual funds; Vito, 2011).

Mathews (1972) researched the criminal provisions of the Investment Company

Act of 1940. Prior to the mid-1930s, the arbitrators of the U.S. Department of Justice

arraigned securities crimes under the Federal Mail Fraud Statute. Mathews identified

four prosecutions. Criminal malfeasance by investment-company managers is curtailed

by the deterrent effect of, and close regulation by, the SEC (Kelly, Bramhandkar, &

Movassaghi, 2009). In 2003, regulators of the SEC launched an investigation of mutual

funds. Investment companies were implicated for market timing and late trading of

funds. Preferred clients were provided with nonpublic information related to the plans of

a portfolio manager to buy or sell a large stock position in a mutual fund (Frankel &

Cunningham, 2007).

Yeung and Freeman (2010) examined Section 36(b) of the Investment Company

Act. The legislators wrote this section to define the term fiduciary. Yeung and Freeman

identified key congressional and industry themes in an effort to clarify the use of the

term. The distinction between an investment company, as defined by the Act, and a

commodity pool was researched by Vito (2011). Kelly et al. (2009) examined the failure

of senior management at Putnam Investments to execute ethical decisions surrounding

mutual-fund trading practice.

Securities Acts Amendments of 1964. Congressional members created the 1964

Securities Acts Amendments to extend disclosures mandated of New York Stock

Exchange firms to firms trading in the over-the-counter market (S. Res. 565, 1964). The

legislators expanded the disclosure requirements of publicly traded security issuers.

Improved qualification criteria and corrective actions for registered brokers and dealers

became law (Owens, 1964). The Securities Acts Amendments also substantially

strengthened the power of both the SEC and the National Association of Securities

Dealers Automated Quotations in denying registration to unqualified entities (S. Res.

565, 1964).

Greenstone et al. (2006) summarized evidence indicating that investors valued the

new disclosure directives under the 1964 Securities Acts Amendments. In the weeks

closely following the public announcement of the legislation, financial reporters of over-

the-counter firms declared abnormal excess returns (Ferrell, 2007). Battalio, Hatch, and

Loughran (2011) found no statistical difference in the returns for over-the-counter firms

moving to the New York Stock Exchange before or after the legislation.

Participants of the New York Stock Exchange were beneficiaries of the 1964

Securities Acts Amendments. Arnold, Hersch, Mulherin, and Netter (1999) noted that

large, established firms moved to the New York Stock Exchange from regional

exchanges in response to the increased rigor of state blue-sky laws and the expanded

disclosure requirements of the Amendments. Stigler (1965) argued that government

intervention was ineffective, as demonstrated by lower securities returns after

implementation of the mandatory disclosure requirements. The federal mandatory

disclosure of U.S. securities presents minimal value (Battalio et al., 2011).

Sarbanes-Oxley Act of 2002. American taxpayers were charged $124 billion for

the savings and loan collapse of the 1980s and 1990s. More than 1,000 banks failed

(Friedman & Friedman, 2010). The corporate malfeasance of Enron, Adelphia,

WorldCom, Tyco, and Global Crossing involved accounting fraud and financial

irregularities (Greve, Palmer, & Pozner, 2010). Approximately 33% of all Fortune 500

corporate profits between 1995 and 2000 were the result of stock-option accounting

practice (Bernstein, Binns, Hyman, & Staubus, 2002). Legislators enacted the SOX Act

of 2002 in response to these problems (Chambers et al., 2010).

Members of Congress cited the need to regain the economic confidence of the

public as an impetus for the enforcement initiatives of the SEC under the SOX Act of

2002 (Luke, 2006). Congressional members drafted the SOX Act to amend sections of

the Securities Act of 1933 and the Securities Exchange Act of 1934. The SOX Act is

expansive legislation obligating certification requisites for public-company filings with

the SEC (S. Res. 745, 2002). The Public Company Accounting Oversight Board is a

result of the SOX Act (Li & Xu, 2008). Controversy continues related to the costs and

benefits of the broad federal oversight and regulation of the Act (Chambers et al., 2010).

Romano (2005) referred to the legislation as “quack corporate governance” (p. 1521).

Cohen, Dey, and Lys (2008) supported the improved financial-reporting requirements of

the SOX Act.

Accrual-based earnings management was examined in the prescandal period

between 1987 and 1999 and the scandal period between 2000 and 2001, as well as the

post-SOX period between 2002 and 2005. Earnings levels increased during the

prescandal period and sharply increased during the scandal period. During the post-SOX

timeframe, earnings levels decreased (Cohen et al., 2008). Zhang, Zhou, and Lobo

(2006) reported less risky financial reporting, as evidenced by higher quality reports post-

SOX. Bartov and Cohen (2009) demonstrated reduced earnings management using

accrual-based methods post-SOX. Chambers et al. (2010) measured accrual reliability

before and after SOX. Reported accrual reliability was significantly higher post-SOX.

The expanded power of the SEC under the SOX Act of 2002 was researched by

Rashkover and Winter (2005). Their findings indicated that the SEC became more

aggressive in enforcement remedies.

Dodd-Frank Consumer Protection Act of 2010. The 2008 financial crisis was

the result of imperfect regulation and global macroliquidity policies (Blundell-Wignall

et al., 2008). The Federal Reserve chairman blamed the crisis on an undue relaxation of

regulatory or supervisory discipline (Freeland, 2010). Legislators created the Financial

Crisis Inquiry Commission under the Fraud Enforcement and Recovery Act of 2009 to

explore causes for the domestic and global crisis (Borgers, 2009). On July 21, 2010,

legislators signed the Dodd-Frank Wall Street Reform and Consumer Protection Act into

law in response to the crisis. Banks and other financial institutions were directly affected.

All corporations conducting business within the United States were indirectly affected

(MacDonald & Lettow, 2010). Legislators intended the Act to facilitate job growth and

otherwise protect consumers, as well as to prevent a future financial crisis (S. Res. 4173,

2010).

A year after legislators enacted the Dodd-Frank Wall Street Reform and

Consumer Protection Act regulators had still failed to write the required statute-mandated

rules (MacDonald & Lettow, 2010). Executives of financial firms and regulators

continue to debate the prospective rules (Benson & Mattingly, 2011). Implementation

deadlines are delayed because regulatory agencies cannot manage the complexity and

volume of the assignment under the Congress chronology. The 2,300-page Act is

estimated to result in 15,000 to 20,000 pages of compliance rules and regulations for

financial institutions (Anderson et al., 2011). Public commentary and opinion are mixed

with regard to the Act. Financial leaders argue that the legislation goes too far (“JP

Morgan’s Dimon,” 2011). Regulators claim that the Act has motivated bank managers to

set aside capital and eliminate off-balance-sheet business. Accounting is more

transparent (Benson & Mattingly, 2011). Because of the 2010 enactment and delayed

implementation of the regulations instituted by the Dodd-Frank Wall Street Reform and

Consumer Protection Act of 2010, no peer-reviewed sources are available and no

empirical research has studied the effects of the Act.

Regulatory Malfeasance

As a result of 150 years of enactments and amendments of existing Acts, financial

regulation within the United States is complex and fragmented (U.S. Government

Accountability Office, 2009) while concurrently expansive and explicit (Pellerin et al.,

2009). Regulatory advocates have argued that rules and oversight are effective strategies

in the control of managerial malfeasance (He & Ho, 2011). Conversely, regulatory

antagonists have purported that business ethics cannot be regulated (Dobbin & Zorn,

2005). Both the denial and recanting narratives present strong points and deficiencies.

Explicit surveillance with public authority controls and regulation is considered

utilitarian.

Kedia and Rajgopal (2011) confirmed through a statistical investigation that

regulation is effective; however, Li and Xu (2008) provided evidence that regulation

reduces market volatility. Fox, Durnev, Morck, and Yeung (2003) developed

econometric techniques to test share-price accuracy and liquidity. Share-price accuracy

increased and market liquidity improved. Cumming and Johan (2008) examined

securities commissions from 25 international jurisdictions. A positive relationship

between jurisdictions with market surveillance in relation to market quality and integrity

was substantiated through empirical analysis.

Regulatory interventions are the problem, not the solution (Dobbin & Zorn,

2005). Dombalagian (2010) questioned whether regulation is capable of combating the

inherent procyclicality of the securities market. Gordon (2010) argued that regulation

must seek to prevent abuse, not undermine or replace the market. The political self-

interests of legislators and the desire to micromanage the markets are unavoidable (Levin,

2010). The securities market is adaptable (U.S. Government Accountability Office,

2009). Although the market may offer favorable circumstances for wealth creation,

opportunities exist for corporations to commit regulatory malfeasance. Regulatory

malfeasance is fraud (Rhee, 2009). Although the intentions of legislators are admirable

in aiming to prevent corporate malfeasance through regulation, financial markets are

adaptable and financial executives are opportunistic.

U.S. Securities and Exchange Commission. Regulatory agencies are authorized

to establish the rules by which publicly traded financial institutions operate and to ensure

adherence to those rules (Pellerin et al., 2009). The U.S. SEC is a regulatory agency

designed to protect investors by maintaining efficient markets and facilitating capital

formation (SEC, 2008). The legislators of the Securities Exchange Act of 1934 created

the SEC. President Franklin D. Roosevelt (1932) advocated for the federal regulation of

securities exchanges. The platformists of the Democratic Party called for extensive

banking and financial reform in 1932 that included the regulation of stock exchanges

(Seligman, 2003). Republican delegates represented voluntarism (Woolley & Peters,

2011).

Regulators of the SEC serve to enforce securities laws and promote healthy

capital markets (Industry Profile, 2011). Agency officials regulate both the sale of

securities and the people and organizations involved in selling securities. They also

ensure the financial disclosure of public companies (SEC, 2008). The SEC is organized

around five presidentially appointed commissioners with staggered five-year terms (SEC,

2008). By law, and to ensure nonpartisanship, no more than three of the commissioners

may associate with the same political party. Reported 2010 revenues were $1.159

million (Industry Profile, 2011) with five SEC divisions, 18 offices, and approximately

3,500 staff (SEC, 2008). During its first year of operation, the SEC put 12 insignificant

stock exchanges out of business. A total of 2,300 potential securities-fraud cases were

investigated (Seligman, 2003). The volume of cases has remained steady over the past

five years averaging 650 on an annual basis (Cheffers et al., 2011). The magnitude of the

cases escalates each year.

SEC regulators ordered $1.1 billion in penalties through June 22, 2011 as a result

of the 2008 financial crisis (SEC, 2011b). In Fiscal Year 2010, SEC regulators brought

681 enforcement cases involving financial wrongdoing (U.S. SEC, 2010). The

misconduct included concealed-risk cases and improper pricing in collateralized debt

obligations and other complex, structured products, as well as misleading disclosures

specific to mortgage-related risk and exposure (SEC, 2011b; see Appendix B).

Public company restatements and misstatements. Revision of previously

reported, public financial information is known as a financial-statement restatement and

can be distributed on a voluntary basis or requested by auditors or regulators (U.S.

General Accounting Office, 2002). The quantity of restatements peaked in 2006 with

1,795 disclosures. The 5-year average from 2006 through 2010 was 1,070 (Cheffers et

al., 2011). The industry areas with the highest number of reporting disputes are revenue

recognition and accounting for stock options and other equities (Bartov & Cohen, 2009).

Bookkeeping for reserves, accruals, and contingencies are also problems.

Accounting errors that cause restatements on annual reports can result in

decreased income, which in turn, can lower shareholder value (Badertscher & Burks,

2010). Preceding the 2008 financial crisis, large negative adjustments were reported.

During 2004, the Federal National Mortgage Association restated its net income to show

a negative $6.335 billion consequence (Cheffers et al., 2011). During 2005, the

American International Group, Inc. disclosed a reduction in net income of $5.193 billion.

Navistar International Corporation admitted a negative $2.337 net-income effect during

2006. The 5-year average from 2006 through 2010 of the largest negative restatements

each year was $893 million.

Public-company misstatements differ from restatements; a misstatement is

manipulative and fraudulent (Giroux, 2008). Enron is one of the most widely publicized

examples of financial misstatement. In 1985, this energy conglomerate was created from

the combination of two gas-pipeline companies. By 2001, Enron was a multinational

corporation that owned and operated gas pipelines, electricity, and pulp and paper plants.

The corporation also traded for the same products and services (Healy & Palepu, 2003).

The market value of the corporation was nearly $70 billion; 2000 reported revenues were

$100 billion. The stock price was over $90 (Giroux, 2008). Enron’s executives, under

rating pressure from financial analysts, investors, and trading partners, misstated

significant revenues. Investors were defrauded by the company’s misleading financial

reports. The SEC and the U.S. Department of Justice began an investigation in 2001

(Healy & Palepu, 2003). Enron was mandated to restate its financial reporting back to

1997. In its restatements, revenue was decreased by $591 million and debt was increased

by $658 million (Healy & Palepu, 2003); other misstatement scandals followed (Giroux,

2008; see Appendix C).

Regulators are concerned that financial restatements fail to provide timely public

information to investors. Badertscher and Burks (2010) examined the duration of

disclosure lags and their causes. When fraud is a lag factor, it may take weeks or months

to release restatements of SEC 10-Q and 10-K filings. When fraud is not a lag factor, the

earnings effect of restatements can be disclosed within a day of the initial restatement

announcement (Dechow, Ge, Larson, & Sloan, 2011).

Dechow et al. (2011) questioned what causes managers to misstate financial

statements. Loughran and McDonald (2011) gathered and analyzed the number of words

within a SEC 10-K filing. In 2001, the typical number of words was 27,062. In 2009,

the median publicly traded company within the United States added 20,000 additional

words. Through textual analysis, Loughran and McDonald determined certain phrases

that can predict a future fraud accusation. Park (2009) assessed the quantitative and

qualitative materiality of financial misstatements. Fundamental analysts provided firm

guidance for distinguishing the financial misstatements that matter to investors. Kravet

and Shevlin (2009) examined the association between accounting restatements and

information risk pricing. A significant increase in information risk pricing is evidenced

in the number of times a company restates.

Mandatory disclosures. Federal securities legislation reflects mandates that

public companies disclose to investors financial and other information. The registration

statement filed with the SEC is typically the first significant public disclosure of any

company (SEC, 2010). Registration is the genesis of an intent to engage in securities

trading (Pear, 2011). After a registration statement is filed, the respective company is

likely to file reports publicly on an ongoing and timely basis (SEC, 2010). Reports of

information and significant events, as well as shareholder actions and ownership, are

examples of public filings (Pear, 2011). The SEC forms list is extensive with 155 total

forms (SEC, 2010). Governmental investigations into regulatory malfeasance are

prevalent (Stuart & Wilson, 2009). When a public company is under regulatory scrutiny

for possible wrongdoing, company officers decide whether to disclose the investigation to

investors and, if the decision is made to do so, when, how, and where. Legislators

administer rules and regulations that dictate a duty to disclose specific events that may

arise during an investigation (Pear, 2011).

The 2008 financial crisis exemplifies the need for high-quality disclosures in

maintaining market confidence (Deloitte Audit Group, 2011). Kalish documented

common qualitative issues in a review of 130 listed entities (Kalish, 2011). Accounting

policies are unclear or inappropriate (IASB Forms, 2008). Financial analysts cannot rely

exclusively upon accounting statements to explain the impact of continuing economic

and liquidity or market conditions. Financial professionals fail to provide disclosure of

material information in accounting statements. Stuart and Wilson (2009) posited that

disclosure decisions related to regulatory malfeasance are layered with judgments against

the interpretive guidance of the SEC. Sinha and Gadarowski (2010) examined

information leakage surrounding disclosures and confirmed that such leaks reduce stock

returns. These researchers identified a positive correlation between stock returns before

and after disclosure. Financial regulators aim to increase share-price accuracy and

improve market liquidity through mandatory public disclosure (Stuart & Wilson, 2009).

Durnev, Fox, Morck, and Yeung (2009) empirically tested the effects of share pricing and

trading on the SEC imposition of public disclosure. The results of mandatory disclosure

were improved liquidity and increased share-price accuracy.

Financial statement malfeasance and securities fraud. The Association of

Certified Fraud Examiners (2011) defined financial fraud as the intentional misstatement

or omission of material information or accounting facts. Berrone et al. (2007) argued that

firms with strong business ethics achieve greater financial performance. Lin and Hwang

(2010) completed a meta-analysis identifying the relationship between corporate

governance and audit-quality variables on earnings management. Perols and Lougee

(2011) characterized firms involved in financial-statement fraud as firms with a higher

likelihood of previous earnings manipulation to meet or beat analyst earnings

expectations. Although organizational misconduct is common (Shadnam & Lawrence,

2011), legal regulation, albeit necessary may not be a sufficient response (Rhode, 2006).

Business Ethics

Fieser (2010) reported that business leaders describe business ethics in terms of

avoidance behaviors. Leaders must abstain from breaking the criminal law in work-

related activities. Business heads must refrain from actions that could result in civil

lawsuits against the company. Managers must desist from actions that dilute the

company image. Financial regulators, most recently via the legislative enactment of the

Dodd-Frank Wall Street Reform and Consumer Protection Act, reignited the debate

surrounding the effectiveness of regulation in controlling malfeasance (He & Ho, 2011).

Business ethics is a more recent subtopic within the field of ethics research (Tran,

2008). Burton and Goldsby (2010) referred to business ethics as the moral rules and

regulations governing the business world. Principles of business ethics are honesty and

integrity, as well as truth telling and fair dealing (Enderle, 2010). Generally accepted

behavioral norms are useful in guiding individual behaviors (Trevino, Weaver, &

Reynolds, 2006). Ethical decisions are those considered legally and morally acceptable

by the broader society. Decisions are unethical if morally unacceptable to the larger

community and illegal (Tran, 2008). Decision-making ethicists are actively expanding

related research (Tenbrunsel & Smith-Crowe, 2008).

Individuals. Ethical decision making in business is based upon the application of

ethical theory to a practical problem (Geva, 2006). Business leaders adhere to various

ethical decision-making constructs in making business judgments (Fard & Noruzi, 2011).

Whittier, Williams, and Dewett (2006) concluded that three types of constructs dominate

the ethical decision-making literature—normative, descriptive, and prescriptive.

Normative constructs. Researchers applying the normative method of study

conceptually analyze and critique (Alzola, 2011). A four-component model for

individual ethical decision making and behavior was advanced by Rest (1986) who

proposed the admission of moral issues and the making of moral judgments by moral

agents. Moral concerns are placed ahead of other concerns by moral agents (Rhode,

2006). Trevino (1992) presented an alternative model described as a person-situation

interactionist model. Moral judgments are controlled by individual and situational factors

such as job context and organizational culture.

Decision makers are affected by contingency factors (Ferrell & Gresham, 1985).

Ferrell and Gresham (1985) presented that an ethical issue materializes from the social or

cultural environment. Dubinsky and Loken (1989) proposed an ethical decision-making

model based upon the Fishbein and Ajzen (1975) theory of reasoned actions (i.e.,

subjective norms such as attitudes leading to the intentional engagement of ethical or

unethical behavior). In the ethical decision-making model crafted by Brommer, Gratto,

Gravender, and Tuttle (1987), environmental factors and personal and professional

activities are combined with individual attributes. Hunt and Vitell (1986) added an

evaluative stage to the decision-making process. Jones (1991) presented an ethical

decision-making model inclusive of moral intensity with the intent to capture issue-

related moral imperatives. Svensson and Wood (2008) introduced the first dynamic

business-ethics model by constructing a framework based upon expectations and

perceptions, as well as evaluations.

Descriptive constructs. Business ethicists include empirical evidence in

descriptive decision-making models (Whittier et al., 2006). Researchers applying the

descriptive method test predictions (Alzola, 2011). Hegarty and Sims (1978) evaluated

ethical decision making under various reinforcement contingencies. These investigators

conducted experimental simulations resulting in empirical evidence of increased

unethical decisions when unethical behavior is rewarded. Fritzsche and Becker (1984)

conducted an empirical survey of managerial attitudes to various ethical vignettes. The

vignettes represented ethical dilemmas of coercion and conflicts of interest. The physical

environment, as well as paternalism and personal integrity, were also denoted. Fritszsche

and Becker found the vignette decisions to be predominantly utilitarian.

Weber (1990) offered empirical evidence of corporate managers employing

different moral-reasoning approaches with different types of moral issues. Chonko and

Hunt (1985) administered a quantitative questionnaire addressing the decision making of

business managers. The perceptions of the participating managers with regard to the

extent of existing ethical problems were found to be affected by the codes of conduct to

which they subscribed. Laczniak and Inderrieden (1987) examined ethical decision

making by engaging students in an “in-basket” exercise. The findings indicated that,

when particular codes of conduct and sanctions are involved, more ethical decisions and

behavior is concurrently evident. Gaudine and Thorne (2001) introduced a cognitive-

affective ethical decision-making model and reported that emotional arousal plays a role

in moral, cognitive decision making.

Prescriptive constructs. Researchers employing prescriptive constructs expand

upon the descriptive frame of empirical, evidence-based decision making (Tsang, 1997).

Decision makers improve decision-making performance by including empirical evidence

within the context in which the decision was made (Whittier et al., 2006). The Petrick-

Quinn integrity-capacity model was developed for the study of ethical decision making

(Petrick & Quinn, 2000). The framework facilitates the examination and adaption of

behavior and moral complexities in matters of business ethics. Bartlett (2003) applied the

model in a study of the decision-making process and found that consideration of the

intraorganizational and extraorganizational contexts of decisions enhance the process.

Christensen (2008) expanded upon the prescriptive decision-making models to

include the role of law as a boundary constraint. The majority of individuals will turn to

the law in order to determine the right action or behavior in a given situation. Geva

(2006) developed the phase model to conceptualize adequate solutions to various types of

ethical problems. She confirmed the effectiveness of the construct as an ethical decision-

making model through illustrative case studies.

Leadership. Researchers conducting traditional decision-making studies

typically have focused on the individual (Harshman & Harshman, 2008). When

confronted with an ethical dilemma, individuals decide upon their responses (Geva,

2006), whereas ethical business decisions typically require moral leadership (Rhode,

2006). Harshman and Harshman (2008) opined that inefficient research is devoted to

discerning why executives breach the ethical and legal standards of business. Ethical

decision making is associated with the capacity of leaders to inspire moral conduct in

followers (Rhode, 2006). The ethical performance of individuals within an

organizational culture is primarily guided by the behavior of leadership (Luban, 2003).

However, Burton and Goldsby (2010) opined that managers simply behaving in a manner

deemed ethically acceptable will not guarantee corporate profitability.

The ethical decision-making patterns of leaders change over time. Kujala, Lamsa,

and Penttila (2011) administered an empirical survey to a sample of business leaders.

This longitudinal study was conducted from 1994 through 2004. The researchers

concluded that the ethical decisions of business leaders became more multidimensional

during the study period. Moore and Flynn (2008) argued that business decision makers

must be deliberative and analytical. Zhong (2011) offered an oppositional view in an

exploration of the potential dangers of deliberative decision making. Through

experimentation, he tested the ramifications of a moral dilemma as a deliberate decision

as opposed to a decision guided by intuitive reaction. The conscious decisions of

participants were found to be more deceptive than those intuitively motivated.

Organizational factors. Both ethical and unethical organizational factors affect

the moral or immoral decisions made by business leaders (Shadnam & Lawrence, 2011).

Organizational factors may include the values and culture of an organization as well as its

environment (Moore & Flynn, 2008). Ethicists have identified a number of studies in

which ethics training and the use of codes of conduct served as organizational influences

in ethical decision making (Schwietzer, Ordonez, & Douma, 2004).

Culture. Organizational culture is comprised of the norms, values, and beliefs

instilled within a company (Greve et al., 2010). This culture is conveyed through the

“language” of a company as well as through the rewards and punishments imposed

within the organization (Ezirim, Nwibere, & Emecheta, 2010). Ezirim et al. (2010)

determined through empirical study that a positive and significant relationship exists

between organizational performance and culture. Organizational cultures may permit

unethical decision making (Greve et al., 2010). Lewis and Ricciulli (1990) described the

culture of Salomon Brothers as an example. Sales representatives of the firm earned

superior commissions while producing inferior client returns. Sims and Brinkmann

(2003) noted that the “win-at-all-costs” culture created by the chief executive of Salomon

Brothers led to unethical and illegal consequences.

Sims and Brinkmann (2003) opined that Enron executives reinforced a culture of

moral flexibility, which led to unethical and illegal activities. A culture of arrogance is

likely to draw ethical consequences (Shadnam & Lawrence, 2011). When chief

executives hold out-of-the-money stock options, organizations are more likely to

manipulate earnings (Zhang, Bartol, Smith, Pfarrer, & Khanin, 2008). Mishina, Dykes,

Block, and Pollock (2010) studied the hubris of high-performing companies and found

that the likelihood of illegal activities is increased when economic performance exceeds

expectations.

Governance. Corporate-governance professionals are concerned with the balance

of power between executives, directors, and shareholders (Ryan, Buchholtz, & Kolb,

2010) and draw upon multiple disciplines, such as legal, finance, and management, to

offer protection against managerial malfeasance (Ahrens, 2008). Marciukaityte,

Szewczyk, Uzun, and Varma (2006) determined that internal control structures improve

both long-term stock prices and operating performance.

He and Ho (2011) evaluated the effectiveness of the SOX Act of 2002. The

legislators of the Act included corporate governance rules. He and Ho opined that this

negated the effectiveness of the Act, concluding that rigid regulation and government

surveillance did not enhance shareholder value. Conversely, Vogel (2011) maintained

that governments are essential to the improvement of corporate behavior. Codes of

conduct are a form of corporate governance and routine within the workplace (Ethics

Resource Center, 2009). The effectiveness of a code of conduct to dissuade unethical

behavior is neither affirmed or negated (Helin & Sandstrom, 2007); however, such codes

are necessary standards in discouraging wrongdoing (Chua & Rahman, 2011).

Miscellaneous factors. Organizational behaviorists have argued the benefits of

ethics training. A contrarian view confirmed with empirical evidence that ethics courses

are ineffectual and counterproductive (Donaldson, 2008). Schweitzer et al. (2004)

described the influence of goal setting on unethical behavior. The relationship between

goal setting and such behavior was stronger when individuals fell short of reaching

established goals. Organizational structures can present a susceptibility to unethical

decisions (Greve et al., 2010). James (2006) opined that successful companies build an

organizational structure inclusive of an ethical culture.

Yuan, Yuan, Deng, and Yuan (2008) determined through a logistic-regression

model that the turnover of Chinese chief executives is related positively to the probability

of financial fraud; manager compensation was negatively correlated. Johnson, Ryan, and

Tian (2009) presented a contrarian view. These researchers found that U.S. firms

involved in fraud receive significantly greater compensatory incentives from unrestricted

stockholders, thus incenting managers to commit fraud to elevate earnings and

subsequent stock returns.

Economic Performance

The relationship between regulatory malfeasance and economic performance and

the valuation of American publicly traded financial institutions has yet to be thoroughly

quantified or examined (Choi & Jung, 2008). Berrone et al. (2007) conducted an

empirical study assessing the relationship between corporate ethical identity and

stakeholder satisfaction and no relationship was confirmed between corporate ethics and

economic performance. Ezirim et al. (2010) reported greater shareholder satisfaction in

Nigerian firms with a stronger ethical identity. Choi and Jung (2008) found an

insignificant relationship between corporate ethical commitment and the valuation

measures of Korean companies.

Kavousy, Fard, Kangarluei, and Bayazidi (2010) analyzed the relationship

between ethics reference points and earnings management in companies listed on the

Tehran Stock Exchange. Their findings presented a low explanatory power of 4% for

ethics criteria on earnings management. An empirical examination of activity-based

earnings management as a causal link between corporate governance and firm

performance was completed by Kang and Kim (2011). These researchers demonstrated

that genuine activity-based earnings management can be effectively controlled by

corporate governance.

Earnings management. Earnings, which is also referred to as the “bottom line”

or net income, is the most significant item on a financial statement (Ross, Westerfield, &

Jordon, 2008). Earnings management is management decision making and reporting with

the intent to achieve stable and predictable financial results (Lev, 1989). To meet

earnings targets, managers make accounting choices among generally accepted

accounting principles (Kang & Kim, 2011), which exemplifies accrual-based earnings.

When managers deviate from normal business practice in an attempt to meet target

earnings, it is referred to as real activity-based earnings management. Examples are

cutting expenses to manage earnings or delaying a new project (Graham, Harvey, &

Rajgopal, 2005).

Accrual-based and real activity-based earnings management are acceptable

accounting practices (Kang & Kim, 2011). Kang and Chun (2010) reported an

empirically significant negative correlation between the abnormal real activities of

managers and firm performance, earnings, and cash flow. Bartov and Cohen (2009)

confirmed that the accounting professionals of firms facing lawsuits frequently shift

earnings-management strategies from accrual-based to activities-based methods.

Valuation measures. Braga-Alves and Shastri (2011) constructed a theoretical

portfolio of stocks to determine if corporate governance results in improved valuation.

The stock of firms with high corporate-governance scores was purchased and stocks with

low corporate-governance scores were sold. The portfolio would have earned 10.68%

per year from 2001 to 2005. Skeptics have argued that changes in voluntary accounting

methods are, in reality, opportunistic earnings manipulation. Linck, Lopez, and Rees

(2007) could not confirm a significant relationship between voluntary changes in

accounting methods and valuation.

Market-based researchers may experience artificial results due to the effects of

financial variables and the differing sizes of participating companies (Jackson, 2008).

The overwhelming influence of large firms is referred to as the scale effect. Net income

is affected by the size of the respective company (Choi & Jung, 2008). Earnings per

share may change based upon the number of issued shares while profitability

concurrently is unchanged (Barth & Clinch, 2009). Market-based researchers use a proxy

to measure the scale of companies (Choi & Jung, 2008). Market capitalization and other

nonfinancial measures are used as benchmarks (Jackson, 2008).

Conclusion

Business ethicists are expanding theoretic research (Tenbrunsel & Smith-Crowe,

2008). Practical application by business leaders has suffered significant failure due to the

business scandals since the 1960s (Brenkert, 2010). Rhode (2006) rebuked ethicists for

the lack of evidentiary quantitative and qualitative research. Tran (2008) claimed that

practitioners follow a contractualist business-ethics paradigm, whereas ethicists follow

ideology. Moore and Flynn (2008) criticized the lack of empirical study on decision-

making behavior. The review of related public literature conducted by these researchers

identified many normative constructs such as the Svensson and Wood (2008) model of

business ethics and the Rhode (2006) moral-agent model. Descriptive theories were

presented such as the Gaudine and Thorne (2001) cognitive-affective ethical decision-

making model. Few studies designed with prescriptive frameworks were reviewed.

Regulatory malfeasance is fraud (Rhee, 2009). Practitioners need applicable and

pertinent research to understand better the basis for regulatory malfeasance (Shadnam &

Lawrence, 2011). Researchers meet practitioner demands by focusing on prescriptive

constructs that prove theories in an empirical manner and offer practical and operational

applications (Tenbrunsel & Smith-Crowe, 2008). The inability of business and legal

ethicists to offer empirical evidence that improves the ethical decision making of

practitioners represents a gap in existing literature. Empirical evidence correlating

regulatory malfeasance and economic performance is limited. Researchers have

addressed predictive factors of firms likely to engage in corporate malfeasance

(MacLean, 2008). Ma (2009) opined that the academic field of business ethics is unclear.

Others support this view by emphasizing the need for further research and application of

ethical work-related behavior (Greve et al., 2010).

Summary

Researchers are interested in the relationship between regulatory malfeasance and

economic performance (Berrone et al., 2007). Thorough investigative and quantifiable

study on such malfeasance and its association with financial performance and valuation

has yet to be conducted (Choi & Jung, 2008). The intention of this current literature

review was to provide the background and a context for the study (Randolph, 2009) and

to acknowledge the sources of existing arguments and new perspectives (Leedy &

Ormrod, 2010). Relevant literature was presented to provide an overview of securities

regulation and regulatory malfeasance. A synopsis of the reviewed literature was

presented to understand ethical decision-making constructs and the influence of

leadership and organizational factors (Neuman, 2011). Research applicable to discerning

the relationship between ethical decision making and economic performance was

examined.

The purpose of statistical research is to generate knowledge through mathematics

(Fisher, 2003). The literature reviewed identified relationships between ethical decision

making and corporate malfeasance (Greve et al., 2010). Limited empirical research has

evidenced a relationship between corporate malfeasance and economic performance.

This review confirms the need for study into the research questions presented for this

study. The findings may make a valuable contribution to statistical understanding

surrounding the relationship between regulatory malfeasance and the economic

performance of companies listed on a U.S. stock exchange. Minimal evidence exists in

the existing literature to demonstrate mathematically the effects of regulatory compliance

or malfeasance on economic performance.

The objective of the study was to fill the gap in existing literature regarding the

relationship between regulatory compliance or malfeasance and economic performance.

Limited quantitative studies have affirmed or negated statistically such a relationship.

Critical points related to current knowledge of the topic of the study were detailed in this

review of the literature (Leedy & Ormrod, 2010). Existing research strategies were

analyzed and improved methodologies identified.

Chapter 3 details the methodology selected to answer the research questions. The

rationale for the descriptive correlation study and its appropriateness are presented in the

discussion of the selected methodology. The sampling strategy, planned instrumentation,

and data-collection procedures are described. A summation of the reliability and validity

of the measurement instruments conclude the discussion of the methodology.

Chapter 3: Methodology

The problem of interest in the study involves the significant pressure encountered

by the chief executives of publicly traded financial institutions to maximize profits within

a legal and regulatory framework (Heneghan, 2010). The purpose of this descriptive

correlation study with a multiple regression analysis was to examine the relationship

between established SEC regulatory compliance or malfeasance and the economic

performance of American, publicly traded financial institutions from 2005 through 2010.

The sample was comprised of 74 publicly traded financial institutions categorized under

SIC 6211. This represents an important study group because financial institutions will be

greatly affected by the proposed congressional regulations resulting from the 2008 global

financial crisis (Benson & Mattingly, 2011).

The quantitative, descriptive correlation design of the study drew upon archived

public financial records and regulatory filings to collect data conducive to answering the

research questions. Details are presented to describe the research methods used for the

study and the appropriateness of the method. The data-analysis procedures are described

and validity and reliability of the instrument are detailed.

Research Method

Three methodological research approaches may be applied to present sound

research (Christensen et al., 2011). Qualitative researchers focus on the complexities of

phenomena within natural settings (Leedy & Ormrod, 2010), while quantitative

researchers explore the correlation among two or more phenomena (Neuman, 2011).

Mixed-method investigators combine qualitative and quantitative approaches to provide a

clearer understanding of research problems than one approach alone can produce

(Symonds & Gorard, 2010). The purpose of the study and the nature of the data directed

the selection of the quantitative research method (Lodico, Spaulding, & Voegtle, 2010;

Neuman, 2011). This approach involved a construct requiring an examination of the

relationship between variables (Black, 1999). Quantitative researchers collect and

analyze data numerically with an emphasis on the precise measurement of variables and

the testing of hypotheses (Schweitzer, 2009).

The purpose of the descriptive correlation study with a multiple regression

analysis was to examine the relationship between established SEC regulatory compliance

or malfeasance and the economic performance of 74 American, publicly traded financial

institutions from 2005 through 2010. The quantitative method was appropriate for an

analysis of this relationship (Kerlinger, 1986). The different types of regulatory

malfeasance and the volume of each were compared to economic performance.

Quantitative methodologists adopt a nomothetic scientific approach. Nomothetic

science consists of the extensive collection and generalized assimilation of facts (Lamiell,

1998). Quantitative approaches are deductive through the testing of hypotheses and are

verification-oriented through confirmation of the hypotheses (Gelo et al., 2008). This

research method was appropriate for the study of the degree to which regulatory

malfeasance or compliance and economic performance are related by testing and

confirming the study hypotheses.

Qualitative researchers develop themes through descriptive words and

nonnumeric data (Salkind, 2009). The principles of cultural meaning and the

examination of specific social cases are emphasized in qualitative study (Neuman, 2011)

while ideographic science is adopted to comprehend individual events (Gelo et al., 2008).

The qualitative method is not appropriate for the study with the aim to generalize rather

than individualize, predict rather than interpret, and verify rather than discover

(Christensen et al., 2011; Mayring, 2007).

Research Design

The research problem and questions are used to guide the selection of an

appropriate research design (Neuman, 2011). Quantitative designs are either descriptive

or experimental (Black, 1999). Descriptive designs are used to examine a situation as it

is and do not require alteration of the situation under investigation (Hopkins, 2008). A

correlation, or covariation, research design is employed to measure the amount of

variation present (Fisher, 2003).

Experimental designs are pertinent to cause-and-effect relationships (Salkind,

2009). A descriptive design was appropriate for the study because the purpose was to

examine possible correlations among regulatory compliance or malfeasance, including

the volume and type of malfeasance, and economic performance, including gross and

after-tax profit margin. The examination was completed with the situation investigated

unchanged (Christensen et al., 2011).

An experimental design is not appropriate because the study involved neither the

comparison of differences between groups nor the determination of presumed causality

with premeasures and postmeasures within study groups (Fisher, 2003). Ex post facto

researchers use similar statistical tools as experimental and quasi-experimental

researchers; however a lack of direct control over the independent variables exists (Black,

1999).

Researchers applying an observational design, record direct observations through

field notes and quantify observed behavior (Salkind, 2009). Developmental designers

sample and compare various age-groups via a cross-sectional design or assess a single

age-group over the course of several months or years via a longitudinal design (Leedy &

Ormrod, 2010). Survey researchers gather information from groups or individuals by

asking questions and tabulating answers (Neuman, 2011).

The purpose of the study was to examine the relationship between established

SEC regulatory compliance or malfeasance, including the volume and type of

malfeasance (the predictor variables) and the economic performance (the criterion

variable) of 74 American, publicly traded financial institutions. The extent to which the

variables covaried, and the strength and direction of the relationship, were analyzed.

Archived data were used to examine the historical financial performance and regulatory

filings of the sample under investigation.

To best examine the relationship between total volume and types of regulatory

malfeasance and economic performance, a quantitative, descriptive correlation design

was used for the study. Gall, Gall, and Borg (2007) confirmed that correlation

researchers seek to discover relationships between variables. Multivariate relationships

require more complex models and contain several interrelated variables (Brace et al.,

2009). ‘Complex’ in the context of the research study suggests that the phenomenon may

have several sources of variation (Kerlinger & Pedhazur, 1973). Qualitative research

designs are ill-suited for the research. Case-study researchers study a particular

individual, program, or event for a specific duration (Yin, 2009). Phenomenological

researchers seek to understand the lived experience through an exploration of

perspectives and perceptions (Giorgi, 1997). Entire groups sharing a common culture are

studied by ethnographic researchers (Leedy & Ormrod, 2010). Grounded-theory

researchers expand existing theory through the construction of an enhanced theoretical

model (Creswell, 2009). These qualitative research designs would not effectively

examine the statistical relationship between regulatory malfeasance or compliance and

financial performance.

Research Questions and Hypotheses

In quantitative correlation research, the research questions guide the data

collection and analysis toward the determination of whether a relationship exists between

two or more variables (Creswell, 2009; Leedy & Ormrod, 2010). The research questions

in a quantitative design are related to characteristics of the variables (Christensen et al.,

2011). The following research questions and hypotheses guided the study:

R1: “What is the relationship between the economic performance of 100 publicly

traded financial institutions coded under SIC 6211 and associated government regulatory

compliance or malfeasance?”

H01: No statistically significant relationship exists between the economic

performance of 100 publicly traded financial institutions under SIC 6211 and associated

government regulatory compliance or malfeasance.

HA1: A statistically significant relationship exists between the economic

performance of 100 publicly traded financial institutions under SIC 6211 and associated

government regulatory compliance or malfeasance.

R2: “Does a relationship exist between the economic performance of 100 publicly

traded financial institutions under SIC 6211 and the types of associated regulatory

violations?”

H02: No statistically significant relationship exists between the economic

performance of 100 publicly traded financial institutions under SIC 6211 and the type of

associated regulatory violations.

HA2: A statistically significant relationship exists between the economic

performance of 100 publicly traded financial institutions under SIC 6211 and the type of

associated government regulatory violations.

R3: “Does a relationship exist between the economic performance of 100 publicly

traded financial institutions under SIC 6211 and the number of associated regulatory

violations?”

H03: No statistically significant relationship exists between the economic

performance of 100 publicly traded financial institutions coded under SIC 6211 and the

number of associated regulatory violations.

HA3: A statistically significant relationship exists between the economic

performance of 100 publicly traded financial institutions coded under SIC 6211 and the

number of associated regulatory violations.

Table 1

Criterion and Predictor Variables Ordered by Hypothesis

Hypothesis

Criterion Variable and Level

Predictor Variables and Levels

Statistical Strategy

H1 Financial performance

Ordinal

Regulatory compliance or malfeasance Categorical

Logistic Regression

H2 Financial performance Ordinal

Type of regulatory violations Categorical

Logistic Regression

H3 Financial performance Ordinal

Volume of regulatory violations Ordinal

Poisson Regression

By employing an appropriate design and accurate data collection, researchers

prove that nothing could have induced the results of a study other than what was defined

in the hypotheses (Black, 1999). Null hypotheses are statements of insignificant

relationships among variables (Neuman, 2011). Affirmation of alternate hypotheses is

gained by disproving the null hypotheses (Steinberg, 2011). Indirect support for the

alternate hypotheses is obtained via rejection of the null hypotheses (Christensen et al.,

2011).

Operational Definition of Variables

Legislators enact the laws under which public financial institutions are governed.

SEC regulatory agents write the rules and policies under which the institutions are

monitored (SEC, 2008). Regulatory violations are defined in the following four broad

categories by the SEC: (a) misrepresentation or omission of material information

surrounding public securities, (b) irresponsible or unfair treatment of customers, (c)

stealing customer funds or securities, and (d) security price manipulation and insider

trading. The variable of regulatory violations was measured in the study using archived,

audited data from the publicly accessible SEC Web site. The SEC requires public

disclosure of regulatory malfeasance (SEC, 2008). The types and number of regulatory

violations were examined for the calendar years of 2005 through 2010 of 74 American,

publicly traded financial institutions.

The basic measures of a company’s economic performance include revenue, pre-

tax and net income. Revenue is the total amount of money earned by a company for its

goods and services. Pre-tax income is a company’s revenue minus expenses for a given

reporting period. After-tax income includes tax expense. The gross margin of a business,

or profit, is a profitability ratio that indicates the relationship between revenue and

operational costs. The after-tax profit margin is also a profitability ratio that includes the

cost of taxes.

Accountants report profits on company income statements, which are financial

statements that report how revenue is transformed into net income or profits (Ross et al.,

2009). Public companies are required to file audited income statements annually (SEC,

2008). The variable of economic performance in the study was measured using audited

financial statements from the publicly accessible EDGAR database maintained by the

SEC.

Operational definitions of constructs that vary are categorized as nominal, ordinal,

interval, or ratio (Black, 1999). The variables of financial performance and the volume

of regulatory violations are continuous variables and were measured at the ordinal level

(Steinberg, 2011). Regulatory malfeasance, a two-category response, and the type of

regulatory malfeasance, a four category response, are discrete variables and were

measured at the nominal level (Salkind, 2009). No order or ranking is intended (Black,

1999).

Sample and Setting

The sample of the study was comprised of 74 publicly traded financial institutions

categorized under SIC 6211. SIC 6211 includes 214 security brokers, dealers, and

flotation companies listed on a U.S. stock exchange (Federal Service Desk, 2010). The

SIC list is available to the public on several Web sites including that of the SEC.

Individual company listings under specific industry group codes are accessed publicly

through several other Web sites that can be located via a search on “company listing by

SIC code.” For purposes of the study, those companies listed on EDGAR under SIC

6211 were used.

Two-hundred and thirteen companies are listed under SIC 6211. The sample of

this study included 74 of the 214. The sample was inclusive of all companies listed under

SIC 6211 that met the study’s research criteria. Eleven of the listed companies were sold,

acquired, or merged during the study’s period. Fifty-six of the listed companies did not

submit financial filings between 2005 and 2010. One company became private and was

no longer required to post financial records to EDGAR. Six of the listed companies were

outside the United States and did not meet the sample criteria. Four of the companies

withdrew their registration and therefore had no archived records. Fifteen of the listed

companies were recorded in the EDGAR database under more than one name. Forty-six

of the SIC 6211 listed companies were corporate trusts, portfolios, affiliated stockholders,

or hedge funds, and were not required to post an annual report or other income statement

records.

Limiting the study sample to publicly listed companies was essential. All

companies offering stock to the public are required under the SEC to file annual financial

reports as well as to publicly disclose regulatory violations (SEC, 2008). The public data

is reported via the SEC-public access database known as EDGAR (Securities Law

Institute, 2010).

Sampling frame. The sampling frame of the study includes U.S. financial

institutions coded under SIC 6211. SIC 6211 is inclusive of 214 listed brokers, dealers,

and flotation companies (Federal Service Desk, 2010). The participating financial

institutions were listed on a U.S. stock exchange. Lenth (2001) opined that the adequate

size of a study, relative to the goals of the researcher, is critical. Creswell (2009)

contended that larger sample sizes contribute to less error variance and greater

representativeness.

Archived data sample. A prestudy calculation of the size of the sample is

important to understanding the number of elements needed to answer the research

questions (Noordzij, Dekker, Zoccali, & Jager, 2011). A sample-size calculator was used

to justify the appropriateness of the sample size in support of the analysis. To achieve a

confidence level of 95% with a confidence interval of 9.22%, a sample size of 74 out of a

target population of 214 was appropriate (Creative Research Systems, 2007).

Geographic location. As noted earlier, the study sample of the research was

limited to publicly traded companies listed on an American stock exchange. All such

companies have audit and public-disclosure requirements involving financial and other

data. The institutions analyzed in the quantitative correlative study were located in the

United. Forty of the companies were regionally concentrated within the northeast area of

the country and 34 were geographically dispersed.

Data Collection

Quantitative researchers collect data in numeric form relevant to testing the

formulated hypotheses (Gelo et al., 2008). Those collecting archival data locate a source

of previously collected information and organize the data in a new way (Neuman, 2011).

Archival was compiled via a review of official public documents and catalogued research

(Christensen et al., 2011).

Archival data. When research involves official public documents, such

documents and records are considered ‘fair game’ (Leedy & Ormrod, 2010). SEC

regulators require timely public posting of financial records and regulatory filings on the

EDGAR database (SEC, 2008). In the study, archival data of financial performance and

regulatory violations for the calendar years 2005 through 2010 was gathered from

EDGAR and the SEC on the sampled institutions. For the purpose of the descriptive

correlative study, archival data, refers to data that was prepared prior to and independent

of being used for this research study (Lucko & Mitchell, 2010).

Lucko and Mitchell (2010) confirmed that archival data represent a beneficial

pool of data for past time periods for researchers to explore. Existing data often are

reliable and offer high measurement validity (Christensen et al., 2011). The data

collection of archival data in the study involved two steps. The first involved the

collection of all regulatory violations and compliance for the six-year period under study.

The second step was to record the financial performance of the participating institutions

for the same six-year period. Data was recorded in spreadsheet form and constructed to

calculate six-year averages, gross and after-tax profit margin. A statistical software

package was used to support the statistical analysis. The level of measurement influences

the manner in which the construct is measured and the range of the statistical operations

(Neuman, 2011). The variables of financial performance as well as the volume of

regulatory violations are continuous variables and were measured at the interval level

(Steinberg, 2011). Regulatory malfeasance as well as the type of regulatory malfeasance

are discrete variables and were measured at the nominal level (Salkind, 2009).

Reliability and Validity

Reliability and validity are concerns with all types of measurement (Neuman,

2011). The term reliability is used interchangeably with dependability and consistency

(Christensen et al., 2011). The term validity reflects accountability and legitimacy (Gelo

et al., 2008). In quantitative research, the term reliability refers to consistent and

replicable results (Trochim, 2006b). In the study, reliability was analyzed using

Cronbach’s alpha, which is a reliability coefficient evidencing how effectively a variable

set measures a construct (Brown, 2002). Creswell (2009) ascertained respectable

coefficients to be in the range of 0.65 to 0.85.

Two specific measures were used in the descriptive correlation study to gather

empirical evidence on the variables of interest (Neuman, 2011). The archival financial

and regulatory data was gathered from the EDGAR and SEC databases, public sources of

financial and other data maintained by the SEC.

Investigators consider stability reliability to question whether the same results

would be drawn if the study traversed different time periods (Neuman, 2011). Future

researchers seeking to replicate the research design and methodology over different

periods of time will increase survey reliability. Representative reliability is used to

question whether the indicator yields the same results when a construct is applied to

different subpopulations (Neuman, 2011). The descriptive correlation study will be

replicable within unrelated industries (Black, 1999).

Researchers have referred to validity as the “true measure” (Neuman, 2011,

p. 212). Internal validity addresses if the study has adequate controls to confirm that the

drawn conclusions are justified by the data collected (Leedy & Ormrod, 2010). External

validity is a measure of whether the research results are applicable to events and settings

beyond the current study (Christensen et al., 2011). Cook and Campbell (1979) advanced

that the classical definition of internal validity is “the approximate validity with which we

infer that a relationship between two variables is causal or that the absence of a

relationship implies the absence of cause” (p. 37). In quantitative research, researchers

consider four types of validity (Neuman, 2011). Face validity is judged by questioning if

the indicator fits as a measure of the variable of interest (Christensen et al., 2011).

Content validity is a measure of the extent to which the instrument represents all facets of

the grounding construct (Neuman, 2011). Neuman (2011) commented that criterion

validity is a measure of whether a criterion accurately represents the construct. Construct

validity is useful when measuring multiple indicators (Smith, 2005).

Descriptive correlation designs present important limitations (Grimes & Schulz,

2002). One such weakness of correlative research is the inability to draw conclusions

about the cause and effect relationships among variables (Fawcett & Garity, 2009).

Correlative results should not be presented as indicating a change in one variable caused

a change in another variable. Internal validity, having to do with some “spurious event”

(Mitchell, 1985, p. 196) related to the treatment or the dependent variable, is also a

disadvantage (Christensen et al., 2011). Several threats to internal validity are imminent

in the study. Whether individuals believe the method of measurement is a fit may serve

as a threat. Content validity addresses whether the full definitional content represents the

measure (Neuman, 2011). In the study, the definition of regulatory malfeasance may

vary; therefore, validity may be questioned. Additionally, agreement regarding what

constitutes “successful economic performance” may vary. The 2008 financial crisis may

be considered a ‘spurious’ situational event. The convergence or divergence of different

constructs is descriptive of construct validity (Smith, 2005). The study may be

threatened by such validity.

External validity is “the approximate validity with which we can infer that the

presumed causal relationship can be generalized to and across alternate measures of the

cause and effect and across different types of persons, settings, and times” (Cook &

Campbell, 1979, p. 37). Researchers have identified threats to external validity as a

generalizability risk of a study (Gelo et al., 2008). Several threats to external validity

may be imminent in the study. Threats to external validity affect generalizability of the

data to other populations (Creswell, 2009).

Privately held institutions were excluded from the study because they are not

required to disclose to the public financial and other data. The research was focused

exclusively on SIC 6211 and excluded all other SIC codes. The study is adaptive to other

SIC codes comprised of publicly listed companies with government regulation and

oversight. The economic performance of the institutions participating in the study may

have been subject to external and internal influences other than regulatory compliance or

malfeasance. The financial executives may have changed institutions, or macroeconomic

and microeconomic conditions may have affected financial performance. Financial

efficiency may have been altered by institutional decisions such as mergers and

acquisitions (Joshua, 2011).

Data Analysis

Quantitative researchers organize and manipulate quantitative data to reveal their

findings (Neuman, 2011). Numeric archival data was collected in the study. Statistical

techniques were applied to methodize the collected quantitative data (Steinberg, 2011).

Because the descriptive correlation study involved more than one predictor and criterion

variable, multivariate statistical analysis was proposed for use to detail the data collected

(Christensen et al., 2011). Specifically, canonical correlation and multiple regression

were proposed as key analytical tools (Kerlinger & Pedhazur, 1973). Inferential statistics

were employed to generalize the findings to the target population from which the sample

was drawn (Trochim, 2006a).

Screening. The data collected in the study was screened prior to the statistical

analysis to ensure the data met the assumptions that guided the multivariate analysis

(Cruz, 2007). The data-screening process included an assessment for normality skewness

and kurtosis. The data was examined to detect univariate and multivariate outliers.

Missing data was analyzed to determine if missing values were a function of a random or

a systematic process. Screening for data linearity and homoscedasticity was completed.

Appropriate transformations were made, as necessary, to modify variables that violated

the statistical assumptions (Myers & Well, 2003).

Statistics. Various statistical strategies were used in the study to present

quantitative explanations of the data sample and measures in manageable summaries

(Trochim, 2006a). Multivariate statistics were proposed for use to consider more than

one response variable at a time. However, logistic regression was determined to be the

more appropriate statistical technique (Howell, 2007). Correlation and logistic regression

statistical techniques were appropriate to answer the research questions of whether a

relationship exists between economic performance and regulatory malfeasance or

compliance, between economic performance and types of associated violations, and

between economic performance and the number of associated regulatory violations.

Canonical correlation. Correlation, or covariation, is a statistical term used to

describe the simultaneous variation of two or more variates (Fisher, 2003). A correlation

is a relationship or an association (Kerlinger & Pedhazur, 1973). Canonical correlation

analysis was used to examine each pair of hypotheses. This statistical analysis technique

was appropriate in the study because resulting data from each hypothesis included

multiple variables and did not assume unidirectional causation from one to another.

Canonical correlation was used to analyze relationships and associations (Hotelling,

1936). Stevens (2002) confirmed canonical correlation is useful in examining

relationships where there is more than one predictor and more than one criterion variable.

Multiple regression. Regression is a statistical technique used to examine how

one or more variable may predict the value of another variable (Christensen et al., 2011).

According to Kerlinger and Pedhazur (1973), multiple regression may be more closely

related to scientific investigation than most other analytic techniques. The multiple-

regression data analysis was proposed for use to examine each pair of hypotheses.

Multiple-regression correlation analysis was thought to be appropriate in the study to

examine the relationship among the study’s multiple variables (Myers & Well, 2003) as

well as to identify the best set of predictor variables.

Using the multiple regression equation, Y 1 = b1X1 + b2X2 + … + bnXn + a,

researchers are able to predict from X and Y by knowing the values of the constants a and

b. In the quantitative correlation study, the multiple regression equation used was Y 1 –

b1X1 + b2X2 + b3X3 + b4X4 + a in the analysis of the four types of regulatory malfeasance

(predictor variable). Logistic regression was determined to be the more appropriate

statistical model (Howell, 2007). Logistic regression is suitable for testing hypotheses

about categorical outcome variables’ and one or more continuous predictor variables’

relationship (Peng, Lee, & Ingersoll, 2002). The logistic equation is stated in terms of

probability that Y = 1 or Y = 0 as follows:

In p = B0 + B1X 1 - p

Because the research tested hypotheses from research questions, degrees of

freedom were presented. The degrees of freedom term refers to a computation of how

many numbers are free to vary in a calculation sequence (Steinberg, 2011). The degrees

of freedom (df) equation in the research was df = n - (k + 1) = 100 - (3 + ) = 96 from the

t-values. The level of statistical significance, or the point at which the null hypothesis is

rejected and the alternative hypothesis is accepted (Christensen et al., 2011), was as

follows: @ 1% level of significance= ± 2.628, @ 5% level of significance = ± 1.985, or

@10% level of significance = ± 1.661 (Cooper & Schindler, 2010).

Summary

The purpose of this descriptive correlation study was to examine the relationship

between established SEC regulatory compliance or malfeasance and economic

performance. The public records of 74 American publicly traded financial institutions

were analyzed from 2005 through 2010. Opposing ideological arguments exist regarding

the effect of regulation on economic performance (Rassier & Earnhart, 2011). A limited

number of quantitative studies exist that statistically affirm or negate a relationship

between financial regulation and economic performance.

The structured, quantitative data-collection technique of archival data over a six-

year period was implemented to answer the research questions and test the hypotheses

(O’Leary-Shuler, 2009). The study used correlation and multiple-regression statistical

techniques to examine the relationship between regulatory compliance or malfeasance

and the economic performance of the financial institutions researched. The different

types and volumes of regulatory malfeasance and economic performance were also

analyzed using correlation and multiple-regression statistics (Steinberg, 2011). The

findings are presented along with a detailed analysis of the archival data in the following

chapter. Each hypothesis is either accepted or rejected (Simon, 2006). A summary of

key findings that either affirm or negate a relationship between SEC regulatory

malfeasance, including the volume and type of violation, and the economic performance

of publicly traded American financial institutions is presented.

Chapter 4: Presentation and Analysis of Data

Financial executives anticipate increased legal and regulatory scrutiny in response

to the 2008 financial crisis (Blundell-Wignall et al., 2008). Investors expect financial

executives to meet earnings projections (Berrone et al., 2007). Regulators demand

adherence to regulations (O’Brien, 2010).

Overlooked by the researchers of existing empirical investigations is evidence of

the influence of regulatory malfeasance or compliance on economic performance (Choi

& Jung, 2008). The aim of this descriptive correlation study with a logistic regression

analysis was to examine the relationship between established SEC regulatory compliance

or malfeasance and the economic performance of American, publicly traded financial

institutions from 2005 through 2010. The study’s sample included 74 of the 214

financial institutions coded under SIC 6211.

The analysis of the archival data gathered from 74 financial institutions coded

under SIC 6211 is presented in Chapter 4. The analytical results of the relationship

between the predictor variables of regulatory malfeasance, including volume and type,

and the criterion variable of economic performance are included in this chapter. The

study’s three hypotheses are tested. Canonical correlations and logistic regressions are

revealed.

Data Collection Process

The data collection process is the ‘trigger’ that advances the research study from

the planning stage to the execution stage (Black, 1999). Six years of historical data from

2005 through 2010 were collected for the sample of 74 publicly traded financial

institutions coded under SIC 6211. The company’s financial performance records and

litigated regulatory infractions were amassed via the SEC’s Web sites.

To analyze financial performance, form 10-K was pulled from EDGAR. Form 10-

K, or 10-KA, is the EDGAR alpha/numeric submission type referencing a company’s

annual report (SEC, 2012). SEC filers, such as the 74 publicly traded financial

institutions researched in this study, are required to make an annual filing of form 10-K.

To pull the 10-K for all 74 companies in the study’s sample, the SEC’s EDGAR Web site

was accessed. 10-Ks were gathered for fiscal years 2005 through 2010.

The proposed sample included 100 financial institutions. The sample was reduced

to 74 because of mergers and acquisitions as well as bankruptcies. Companies privatized

and were no longer subject to public reporting requirements of their financial records.

Several companies initially registered with the SEC and subsequently withdrew their

registrations; no annual reports were filed. International companies were also listed

under SIC 6211 and required exclusion from the study’s sample. Corporate trusts,

portfolios, affiliated stakeholders filings, and hedge funds are categorized under SIC

6211. These entities are not required to post form 10-K (SEC, 2010).

A geographic sample of companies in New York and its contiguous states was

suggested in the proposal. The geographic sampling was not appropriate because of the

reduced number of financial institutions. All of the companies included in the sample

were publicly traded American financial institution categorized under SIC 6211. Annual

report filings were posted by the sample companies during the study’s 2005-2010 time

period. The resulting 74 sample companies are bolded in Appendix A.

The Figure 1 data is a graphical representation of the annual decline in annual

statements filed by the 74 companies researched over the study’s six-year time period. A

total of 401 annual reports were accessed and reviewed from 2005 through 2010. The

number of annual reports filed decreased annually.

Figure 1

Accessed Financial Statements by Year

To examine regulatory malfeasance, SEC litigation documents in the date range

of January 1, 2005 through December 31, 2010, were extracted from the SEC.gov Web

site’s advanced search tool. A total of 1,412 litigations were revealed for the 74

companies in the study’s sample from 2005 through 2010. The litigation matters

included administrative proceedings and court cases as well as distribution plans (SEC,

2011b). Briefs and complaints were also totaled. The 1,412 litigations were read to

discern those that affirmed malfeasance by the company versus an individual employed

by the company. Duplicates and misfiles were dismissed.

Table 2 is a tabular presentation of the type and volume of regulatory infractions

breached by the study’s 74 companies from 2005 through 2010. One hundred and fifteen

violations were identified. The largest volume of infractions, 43 of the 115 violations or

73 72

2005 2006 2007 2008 2009 2010

A c c e s s e d

F in

a n

c ia

l S

ta te

m e n

Year

37% was the result of irresponsible or unfair treatment of customers. Only five

infractions were identified during the study’s time period for companies’ stealing

customer funds or securities.

Table 2

Types of Regulatory Infractions

Type of Regulatory Infraction

Volume

Percentage

1 Misrepresentation or omission of material information surrounding public securities

34 30%

2 Irresponsible or unfair treatment of customers 43 37% 3 Stealing customer funds or securities 5 4% 4 Security price manipulation and insider trading

33 29%

Total Infractions 115 100%

SEC regulatory documents were retrieved from the SEC’s Web site. More than

1,400 regulatory filings were reviewed for the study’s 74 companies from 2005 through

2010. Of the 1,412 documents, 115 fell into the specific regulatory infraction categories

under investigation.

The percentages of regulatory violations were not highly significant. The study’s

restricted sample of companies listed under SIC 6211 excluded the potential to capture

companies with regulatory violations in other SIC codes. Company subsidiaries may

have been cited for regulatory malfeasance during the study’s time period. Though the

subsidiary may have qualified under SIC 6211, the parent company was listed under a

different SIC code. Timing may have played a factor. The situation may exist in which a

company was accused of a regulatory violation in 2008. Investigation and trials

proceeded through 2011 and the resulting regulatory violation would have fallen outside

the study’s timeframe.

Appropriateness of Statistical Tests

Inferential statistics were used to understand characteristics of the population

based on the sample data. The IBM ®

Statistical Package for the Social Sciences version

20 (SPSS) was employed to perform a number of statistical tests (IBM Corporation,

2012). The Pearson correlation coefficient was applied to test the correlations between

the financial performance variables and companies’ regulatory malfeasance or

compliance.

Descriptive correlation with multiple regression analysis was the proposed

statistical procedure to be used in the study (Kerlinger & Pedhazur, 1973). Regression is

a mathematical procedure used to examine how one or more variable may predict the

value of another variable (Christensen et al., 2011). The study’s intent to show a

‘significant or no significant’ relationship between variables is supportive of the

descriptive correlation with a regression technique (Kerlinger & Pedhazur, 1973).

The proposed multiple regression statistical procedure was determined not to be

the most effective method to examine the study’s research questions. Binary responses

were presented by the study’s criterion variable. Multiple regression analysis yields

predicted values that follow a normal distribution and are less than zero and greater than

one (Black, 1999). In this study, the probability values could be only zero or one. The

variables are binary (Black, 1999).

Logistic regression was a more appropriate statistical model than multiple

regression (Howell, 2007). Logistic regression is suitable for testing hypotheses about

relationships between categorical outcome variables, and one or more continuous

predictor variables (Peng et al., 2002). The logistic equation is stated in terms of

probability that Y = 1 or Y = 0 as follows:

In p = B0 + B1X 1 - p

Because the presence or absence of regulatory malfeasance was a categorical

variable, repeated ANOVA was performed to test malfeasance or compliance to the mean

financial performance (Steinberg, 2011). Mauchly’s Test of Sphericity was performed to

examine outliers (IDRE Research Technology Group, 2013). Greenhouse-Geisser

correction was used to show the homogeneity of profit margin over the study’s six-year

period (IDRE Research Technology Group, 2013).

Logistic regression was used to analyze the relationship between mean profit

margin and the presence or absence of malfeasance. To test the second hypothesis, Chi-

squared analysis was used to examine mean profit margin and the types of regulatory

malfeasance. Chi-squared analysis was also utilized to test the third hypothesis (Fisher,

2003). Logistic regression analysis was run to determine if any relationships existed

between variables of interest (Christensen et al., 2011).

Data Analysis Procedure

The SEC’s archival database, EDGAR was used to collect revenue and net

income from the study’s 74 sample companies. Gross income margin, or gross profit

margin, was calculated by dividing pre-tax income by revenue. Gross profit margin is

expressed as a percentage (Kalluru & Bhat, 2009). The SEC’s litigation database was

utilized to access the sample’s regulatory violations from 2005 through 2010. Six

sequential arithmetic steps were followed to test the study’s three hypotheses (IDRE

Research Technology Group, 2013).

Step 1: Correlation between gross profit margin and after-tax profit margin.

Gross profit margin and after-tax profit margin are calculated by dividing pre-tax income

by revenue and after-tax income by revenue, respectively (Bishop, 2004). Step 1

required a determination be made regarding whether gross profit margin and after-tax

profit margin or either of the variables would be used in the statistical model. The

correlations between gross profit margin and after-tax profit margin were tested to

determine if they were linearly correlated (Fisher, 2003). Additionally, because data was

collected for a six-year period, testing was required to conclude if there was a linear

correlation across years for both gross profit margin and after-tax profit margin (Field,

2009).

The Pearson correlation showed that gross profit margin and after-tax profit

margin correlated within each year (Steinberg, 2011; see Appendix D). Gross profit

margin, except for a few pairs of observations, was not linearly correlated across years.

Based on the statistical correlations, gross profit margin was used as the predictor in the

study’s statistical models (Brace et al., 2009).

Step 2: Repeated ANOVA. Repeated ANOVA was used to collapse the gross

profit margin across the six years of the study (IDRE Research Technology Group,

2013). Two approaches were executed (see Appendix E). The first analysis was

completed by employing a logistic model. The model was applied to the six-year gross

profit margin data collected to learn whether the presence or absence of malfeasance bore

a relationship to economic performance. The second examination dropped the

insignificant predictors from the model. Neither statistical exploration was useful to

examine malfeasance (Field, 2009).

A repeated ANOVA test was performed to understand if variances of gross profit

margin were similar across the six years of the study (IDRE Research Technology Group,

2013). Two conditions were examined – normal distributions of each group and equal

variances across groups. Consistent with Mauchley’s Test of Sphericity, neither

condition was met because of extreme outliers (Field, 2009).

The Greenhouse-Geisser correction was used to determine if sphericity had been

violated (Denis, 2011). The closer the Greenhouse-Geisser correction is to one, the more

homogeneous the variances of differences (Field, 2009). Statistical evidence did not exist

to reject that the co-variances between the pairs of years were equal. The arithmetical

results did not support the collapse of the gross profit margin across the study’s six-year

time period (Denis, 2011). The analysis proceeded with mean gross profit margin.

Step 3: RQ1 logistic regression. Step 3 explored Research Question 1 and

Hypothesis 1. Research Question 1 asked, “What is the relationship between the

economic performance of 74 publicly traded financial institutions coded under SIC 6211

and associated government regulatory compliance or malfeasance?” Hypothesis 1 was

put forth as follows:

H01: No statistically significant relationship exists between the economic

performance of 74 publicly traded financial institutions under SIC 6211 and associated

government regulatory compliance or malfeasance.

HA1: A statistically significant relationship exists between the economic

performance of 74 publicly traded financial institutions under SIC 6211 and associated

government regulatory compliance or malfeasance.

Ordered logistic regression was applied to test Hypothesis 1, using the presence or

absence of regulatory malfeasance to predict mean gross profit margin. The model was

not useful (p-value=0.195) (Steinberg, 2011; see Appendix F). The null hypothesis was

accepted. No statistical evidence exists (p-value=0.830) that mean regulatory compliance

or malfeasance is a significant predictor of mean gross profit margin of the 74 companies

studied.

Step 4: RQ2 logistic regression. Step 4 examined Research Question 2 and

Hypothesis 2. Research Question 2 inquired, “Does a relationship exist between the

economic performance of 74 publicly traded financial institutions under SIC 6211 and the

types of associated regulatory violations?” Hypothesis 2 was presented as follows:

H02: No statistically significant relationship exists between the economic

performance of 74 publicly traded financial institutions under SIC 6211 and the type of

associated regulatory violations.

HA2: A statistically significant relationship exists between the economic

performance of 74 publicly traded financial institutions under SIC 6211 and the type of

associated government regulatory violations.

Logistic regression was used to analyze the relationship between the companies’

economic performance as measured by gross profit margin, and the different types of

publicly reported regulatory violations (Denis, 2011; see Appendix G). Type 1

regulatory malfeasance included reported violations related to the misrepresentation or

omission of material information surrounding public securities. The logistic regression

analysis was not useful at 0.05 significant level, p-value=0.068 (Steinberg, 2011). Mean

gross profit margin was not a significant predictor of a company’s regulatory violations

for misrepresenting or omitting material information about public securities (p-

value=0.633).

Regulatory violations categorized under Type 2 include irresponsible or unfair

treatment of customers. Statistical evidence exists (p-value=0.010) that the logistic

regression model is useful to predict a relationship between the economic performance of

the companies’ researched and their publicly reported Type 2 regulatory violations

(Kerlinger & Pedhazur, 1973). Using a 0.05 significance level, the mean gross profit

margin is significant (p-value=0.054) to predict the volume of Type 2 regulatory

violations as depicted in Table 3.

Table 3

Parameter Estimates: Violation Type 2

Parameter

Std. Error

95% Wald Confidence Interval

Hypothesis Test

Lower

Upper Wald Chi-Square df Sig.

(Intercept) 1.179 .3061 .579 1.779 14.846 1 .000 Meangpm -.019 .0100 -.039 .000 3.724 1 .054 (Scale) 1

Type 3 regulatory violations involve the stealing of companies’ funds or

securities. The logistic model used to predict Type 3 violations on the economic

performance of the companies included in the study is not useful at a 0.05 significant

level, p-value=0.613 (Kerlinger & Pedhazur, 1973). The mean gross profit margin is not

a significant predictor, p-value=0.753, for Type 3 regulatory violations.

The fourth type of regulatory violation comprises security price manipulation and

insider trading. Statistical evidence exists, p-value=0.037, that the logistic regression

model is useful to predict the volume of Type 4 violations (Denis, 2011). However, the

mean gross profit margin is not a significant predictor, p-value=0.137, of the study’s

sample companies’ charges for violating security pricing and insider trading regulations.

Step 5: Poisson regression. The third Research Questions posed the question,

“Does a relationship exist between the economic performance of 74 publicly traded

financial institutions under SIC 6211 and the number of associated regulatory

violations?” The associated hypothesis was issued as follows:

H03: No statistically significant relationship exists between the economic

performance of 74 publicly traded financial institutions coded under SIC 6211 and the

number of associated regulatory violations.

HA3: A statistically significant relationship exists between the economic

performance of 174 publicly traded financial institutions coded under SIC 6211 and the

number of associated regulatory violations.

Poisson regression is a form of regression analysis (Christensen, 2011). The

statistical technique uses generalized linear models to count data and contingency tables.

Poisson regression was used to predict the relationship between economic performance as

measured by mean gross profit margin, and the volume of regulatory violations (IDRE

Research Technology Group, 2013).

More than half of the responses are zero counts, and the distribution of total

volume is skewed strongly to the right (see Appendix H). The model to predict the total

volume was not useful, p-value=0.631. No statistical evidence exists, p-value=0.656, that

the mean gross profit margin is a useful predictor for the volume of regulatory violations

(Denis, 2011).

Step 6: Additional Chi-squared analyses. An additional test was completed

to more thoroughly explore the relationship between Type 2 regulatory violations,

irresponsible and unfair treatment of customers, and the violating companies profit or

loss (see Appendix I). Mean gross profit margin was categorized (Christensen et al.,

2011). The new categorical variable was used to complete a Chi-squared analysis that

examined the difference in the binary response between profit and loss (Steinberg, 2011).

All the Chi-square tests failed to reject the null hypotheses with the exception

of Type 2 violations. Statistically significant evidence exists, p-value=0.045, that there

are differences in the profits and losses in companies with publicly reported Type 2

regulatory violations.

A second additional test was run to examine whether a relationship existed

between the regulatory compliance or malfeasance of the study’s top 10 most profitable

companies, and the other companies that reported profits or losses. Mean gross profit

margin was further categorized into three levels. The three groupings included the top-10

most profitable as well as the companies profitable, but not in the top-10 and the

companies that experienced a loss.

Chi-squared tests were used to find the differences between regulatory

compliance or malfeasance and the type of violations in the top most profitable

companies as compared to the other companies in the study (Steinberg, 2011). The

assumptions that at least 20% of the expected count have a cell count larger than or equal

to five and none of the expected cell count is zero are not met for the dependent variables

of malfeasance and Type 3 violations (Tukey, 1977). None of the Chi-squared tests

presented significant results.

For the pairs that did not meet the Chi-squared test, dependent variables of

malfeasance and Type 3 violations, Fisher’s exact test was used (IDRE Research

Technology Group, 2013). The Fisher’s exact test does not have the assumption that

each cell’s expected frequency is five or more and can be used regardless of how small

the expected frequency is (Field, 2009). No significant results were presented using

Fisher.

Critical values. Several statistical analyses were used to test the study’s

hypotheses. Both statistical significance and insignificance were found. Hypotheses

were accepted and rejected. Table 5 presents a synopsis of the study’s critical values.

Table 4

Critical Values Synopsis

Hypothesis

Omnibus Test

Parameter Test

Table(s)

Results

0.195 0.830 F2 No statistical significance

H2 Type 1 0.068 0.633 G1, 3 No statistical significance Type 2 0.010 0.054 G2, 4 Marginal statistical significance Type 3 0.613 0.753 G3, G4 No statistical significance Type 4

0.037 0.137 G5, G6 No statistical significance

H3 0.631 0.656 H2, H3 No statistical significance

The logistic regression model is useful to predict a statistically significant

relationship between Type 2 regulatory violations and economic performance. Type 2

regulatory infractions are defined as a company’s irresponsible or unfair treatment of

customers. A diversified, financial services holding company, for example, was charged

in June 2011, with irresponsibly telling customers that Auction Rate Securities were

‘cash equivalents’ and ‘highly liquid’ short-term investments.

Summary

The data gathered from 401 annual reports, and 115 regulatory infractions over

the time period of 2005 through 2010 were presented in Chapter 4. The study’s three

research questions addressed the relationship between financial performance and

regulatory compliance or malfeasance. The type and volume of regulatory malfeasance

were explored. A data analysis associated with each of the three research questions and

their respective hypotheses was provided.

The relationship between established SEC regulatory compliance or malfeasance

and the economic performance of American publicly traded financial institutions from

2005 through 2010 was examined. The outcomes were described for each of the study’s

variables. A statistically significant relationship was revealed between the irresponsible

or unfair treatment of customers and financial performance. Significant relationships

were not found between the predictor variables of regulatory compliance or malfeasance

or the type or volume of violations and the criterion variable of financial performance.

Insights gained from the study are discussed in Chapter 5. An assimilation and

understanding of the study’s results is offered. Responses are offered to several

interpretive questions relating to how and why the results may be beneficial and to

whom. Possible alternative explanations for the study’s results are included. Suggestions

are offered as to the form of future research. The significance of the study’s findings to

financial executives and regulators is discussed as well as relevant recommendations to

financial executives and regulators.

Chapter 5: Conclusions and Recommendations

The failure of American financial institutions following the 2008 financial crisis

spurred Congress to pass laws and regulatory agencies to write new regulations

(Blundell-Wignall et al., 2008). Financial executives continue to argue the increased

regulation abates their financial performance (Berrone et al., 2007). Lawmakers and

regulators contend enhanced regulation generates greater profits because it builds the

trust and confidence of investors to invest (O’Brien, 2010).

The purpose of this descriptive correlation study with a regression analysis was to

examine the relationship between established SEC regulatory compliance or malfeasance

and the economic performance of American, publicly traded financial institutions from

2005 through 2010. Overlooked by published researchers is empirical evidence

demonstrating a statistically significant or insignificant relationship between regulatory

compliance or malfeasance and the economic performance of U.S. companies in the

financial services sector (O’Brien, 2010). Limited consistent evidence exists in

published research associating the volume or type of SEC regulatory violations with the

financial performance of financial services companies (Lin & Hwang, 2010).

Seventy-four financial institutions comprised the study’s sample. The 74

companies were all coded under SIC 6211, which includes security brokers, dealers, and

flotation companies. The 74 companies were all publicly traded and located in the

United States. All 74 companies publicly reported their audited income statements for

one, two, three, four, five, or six years during the study’s time period of 2005 through

2010.

A total of 115 regulatory infractions were associated with the 74 companies over

the study’s six-year timeframe. Thirty-four violations were the result of

misrepresentation or omission of material information surrounding public securities.

Forty-three infractions were reported for irresponsible or unfair treatment of customers.

Stealing customer funds or securities was breached by five of the companies. Security

price manipulation and insider trading laws were violated by 33 companies.

Future researchers in the fields of regulatory reform and ethical leadership may

expand on the study’s findings. Regulatory reform researchers may use this study as a

springboard for a qualitative study. Qualitative regulatory reform researchers may focus

on financial executives’ perceptions of the impending Dodd-Frank legislation. A

limitation of this research was the study’s 2005 through 2010 timeframe. Future

researchers may lengthen the study to explore why financial executives comply or

disobey regulatory requirements. The study’s data collection and analysis processes may

be duplicated to analyze further the types and volume of regulatory violations over a

longer time period.

Leadership researchers in the field may use the results to add to the field business

ethics focused on executive decision-making. Descriptive-correlation researchers are

limited in their ability to infer causality (Christensen, 2011). To expand this study, future

researchers may complete a qualitative research study to explore the research question

related to the relationship between a company’s economic performance and associated

regulatory compliance or malfeasance.

Harshman and Harshman (2008) suggested that research focused on the

discernment of why executives breach the ethical and legal standards of business is

inefficient. The study’s results may be used by financial executives to affirm their

decisions related to regulatory compliance. Companies’ chief financial officers may use

the findings and conclusions of this study to budget for the cost of regulatory reform.

Compliance executives may apply the study’s results to their regulatory cost benefit

analyses. Company compliance managers may use the results of this study in discussions

with business managers to dispel the negativism of approaching regulations. SEC

regulators may use the results of this study during discussions with financial executives

to allay the argument that increased regulation influences negatively a company’s

profitability. SEC regulatory trainers may find the results supportive in investor training

sessions on how to improve investor certitude.

A summary of the study’s findings is presented in Chapter 5. An expanded

discussion of inferences and interpretations is reported. Each significant or insignificant

finding is further analyzed. Substantive recommendations for application to other

populations and further related research are discussed. Each hypothesis result is

compared to the literature findings. Major findings are compared and contrasted to the

theoretical frames. Recommendations and proposed leadership actions are stated.

Summary of Findings

The study’s ideological framework was grounded by economic and public-choice

theories as well as leadership theories. Economic theorists advance the view of free

markets and limited regulatory interference (Smith, 1776). Public-choice theorists

suggest individuals are motivated solely to serve their own narrow interests (Mashaw,

1997). Legislators cannot put forth regulation that serves a broad constituency and

therefore, government intervention is ineffective (Powell & Stringham, 2009).

Agency cost theorists purport that the potential for malfeasance exists when the

interests of stakeholders diverge. Financial executives may take irresponsible risks and

manipulate information to satisfy one agency at the detriment of another (Williams &

Ryan, 2007). Ethics theorists have described the deterioration of public confidence in

leaders as a result of their breach of leadership ethics (Burton & Goldsby, 2010).

Several themes emerged from the empirical results of this research study. The

themes are supported by the conceptual framework that shaped this research.

Opportunities to explore new theories are discussed.

Theme 1 (RQ2): Type of regulatory violations. Research Question 2 sought to

examine the relationship between the economic performance of 74 publicly traded

financial institutions under SIC 6211 and the types of regulatory violations. A total of

115 regulatory violations were reported during the study’s time period and were

categorized by type.

Forty-three of the 115 violations were related to irresponsible or unfair treatment

of customers. A marginally significant relationship between this type of regulatory

infraction and the violating companies’ economic performance was identified. No

significant relationships were found between the misrepresentation or omission of

material information, stealing of customer funds, or security price manipulation and

insider trading and the financial performance of the companies sampled. Logistic

regression was used to examine the influence of the type of regulatory violation on the

companies’ financial performance (IDRE Research Technology Group, 2013). The

results suggest that the irresponsible treatment of customers’ financial matters leads to

deterioration of the company’s financial results.

Published research was put forth in the literature review related to laws and

regulations written to protect the public. The focus was to find prior empirical evidence

of an arithmetically proven relationship between financial performance and regulatory

adherence. Neither the literature review nor the study’s theoretical constructs presented a

specific analysis of the relationship between customer mistreatment and financial

performance. Future researchers may determine this to be an opportunity for further

exploration.

Theme 2 (RQ1): Regulatory compliance or malfeasance. Research Question 1

used logistic regression to examine the relationship between financial performance of 74

American publicly traded financial institutions coded under SIC 6211 and regulatory

compliance or malfeasance. No significant relationship was revealed by the data

analysis.

A confirmatory statistical investigation that regulation was effective was

conducted by Kedia and Rajgopal (2011). Dobbin and Zorn (2005) argued regulatory

interventions were the problem, not the solution. The results of Research Question 2’s

inquiry may support the claim purported by regulatory advocates.

Increased financial regulation such as the Consumer Protection Act (Anderson et

al., 2011) will likely bear no consequence on the financial performance of the affected

companies. Public-choice theorists who suggest regulators will serve their narrow

interests and affirm that businesses will figure out how to make money, regardless of

perceived obstacles (Buchanan & Tullock, 1962). Public-choice theorists may be

negated by Research Question 2’s results.

Theme 3 (RQ3): Volume of regulatory violations. The aim of the third research

question was to analyze the relationship between the volume of regulatory infractions and

the financial performance of 74 publicly traded American financial institutions coded

under SIC 6211. Poisson regression was used to examine the predictor variable in

relation to financial performance as the criterion variable (Steinberg, 2011). No

significant relationship between financial performance and the number of regulatory

infractions was confirmed by the data analysis.

Despite the payment of $1.1 billion in penalties through June 22, 2011 as a result

of the financial crisis (SEC, 2011b), a significant relationship between the volume of

infractions and the violating companies’ financial performance was not confirmed by the

study’s statistical analysis. A beneficial and compelling consideration for further study

may be to analyze volume by dollars versus number. Financial managers may extend the

research to explore the relationship between the financial penalties paid for the violation

and economic performance.

Theme 4: Top-performing companies. Additional statistical tests were

conducted to expand the inquiry concerning the top-performing companies and their

regulatory malfeasance or compliance. The mean gross profit margin was further

categorized into three groupings of companies including the top-10 performing

companies as well as companies with a profit or loss. The three groupings were used to

examine whether the level of financial performance was related to regulatory compliance

or malfeasance. No significant relationship was present.

Agency theorists’ quest for the convergence of stakeholders’ interests may be

supported by the results of this study. After reviewing the study, business ethicists may

experience greater confidence in financial executives’ decisions not to violate a

regulation regardless of whether the fine will have a de minimis effect on the companies’

financial results.

Restatement of Limitations

The study’s timeframe of years 2005 through 2010 may have influenced the

statistical results. The financial-service sector’s performance during the study’s time

period presents a stark contrast. The 2008 financial crisis was succeeded by the ‘lows’ of

years 2008 through 2010 and preceded by the ‘highs’ of the years 2005 through 2007

(Levin, 2010). The financial crisis spurred heightened scrutiny of financial services

companies related to regulatory compliance (Raidy, 2011). Therefore, the 2008 financial

crisis was a catalyst for increased regulation (Anderson et al., 2011). The study’s

timeframe was selected to cover the period three years before and two years after the

financial crisis, limiting the study to an event.

The accuracy of the archival financial records for the company’s sampled was

identified as a limitation of the study. Though tabbed as a limitation, the study’s data

collection process was restricted to SEC-reported 10-Ks and annual reports with auditor

confirmation. Annual report preparers and auditors may make mistakes, and the mistakes

may require a restatement of the financial reports. For the purposes of this study, the

archived, audited, and restated, if applicable, annual report or 10-K data was pulled for

analysis.

The correlative statistical method used in the study was disclosed as a potential

limitation because the statistical technique cannot show causality (Christensen et al.,

2011). The study’s aim was to examine the co-variations of two or more variables.

Correlation researchers cannot make statements concerning cause and effect on the basis

of this type of research. In correlation research, researchers do not know the direction of

the cause. A second or third unknown variable may be involved.

The study’s intent to show a ‘significant or no significant’ relationship between

variables is confirmation of the descriptive correlation with regression analysis (Kerlinger

& Pedhazur, 1973). Multiple regression was the statistical procedure proposed to analyze

the study’s three research questions. Because the study’s criterion variable presented a

binary response, logistic regression was used to analyze the data set (Christensen et al.,

2011). Multiple regression analysis often yields predicted values less than zero and

greater than one (Black, 1999). In this study, the probability values could only be zero or

one. Because the study’s probability values were binary, logistic regression was the more

appropriate statistical model (Howell, 2007). Techniques like logistic regression are

extensions of multiple regression (Howell, 2007).

The study’s sample of 74 companies was inclusive of all American, publicly

traded financial institutions in SIC 6211 that filed annual reports or 10-Ks on EDGAR.

Restricting the sample to SIC 6211 may have limited the accuracy of the results. The

companies in SIC 6211 were stressed significantly by the financial crisis (Dolmetsch,

2008). Regulatory researchers may apply the study’s research method to the ‘dot-com

bubble’ in the technology sector. With the technology bust in 2000, the time period of

years 1998 through 2003 of one of the technology SIC codes may be of interest.

Other limitations. The study’s scope of a specific SIC code may have limited

the study. Many of the largest financial services companies are organized in such a way

that though they have a division that should be reported under SIC 6211, their parent

100

company is categorized under another SIC code. An American multinational diversified

financial services company is categorized under SIC 6021, National Commercial Banks.

The companies’ broker/dealer division was cited with multiple regulatory violations

during the study’s time period (SEC, 2011b), but these results fall outside the study’s SIC

6211 sample limitation.

Companies that may be central to the 2008 financial crisis are not coded under

SIC 6211. Two companies viewed as key contributors to the 2008 financial crisis are

coded under SIC 6111, Federal and Federally Funded Sponsored Credit Agencies (SEC,

2011a). The SEC takes enforcement action against both of these government-sponsored

entities for regulatory misconduct during the study’s time period (SEC, 2011b).

The rationality assumption embedded in the agency problem may have limited the

study. The premise that financial executives act rationally may be false. The supposition

that government regulators do not intentionally make decisions that create a

demonstrably worse result may be inaccurate.

Conclusions and Implications

Several conclusions may be drawn from this study. First, no significant

relationship exists between a company’s financial performance and its regulatory

malfeasance or compliance. Second, a marginally significant relationship exists between

a company’s financial performance and its treatment of customers. Third, no statistically

significant relationship exists between a company’s financial performance and the

number of its financial violations. Statistical significance, or lack thereof, should not be

interpreted absent of the context within which it occurs (Salkind, 2009).

101

Implications to leaders. The study’s aim was to examine the relationship

between established SEC regulatory compliance or malfeasance and the economic

performance of American, publicly traded financial institutions from 2005 through 2010.

The debate of leadership ethics was exposed in the published literature review (Moore &

Flynn, 2008; Zhong, 2011). Quantifiable leadership guidance about the financial benefit

or detriment of regulatory compliance or malfeasance was not a result of the present

study. Financial leaders are entrusted to ‘do the right thing’ and comply with regulations

(O’Brien, 2010). As a result of the study, financial leaders may better understand the

mathematically proven statement that financial performance will be impaired if

customers are mistreated or if material information surrounding public securities is

misrepresented or omitted.

Optimistic regulators and financial executives in support of expanded regulation

may interpret these results to support their views. Financial executives with the belief

that regulation impedes their business results may view the results of this study as

limited. A noted limitation is the brevity of the study’s six-year duration.

Implications to the study of leadership. The ideology of business ethics

continues to present a quandary to leadership researchers. The purpose of this study was

to examine the relationship, if any, between financial performance and regulatory

malfeasance and compliance. No significant relationships were confirmed. Two thematic

findings are noted from the findings. First, financial executives need not violate

regulations in their quest for improved financial performance. Second, because no

relationship was found to exist between regulation and financial performance, financial

executives should honor the approaching regulatory expansion.

102

Recommendations for Further Study

No matter how complete a research study’s methodology is designed, the

investigation can be extended be extended or improved (Black, 1999). Financial

regulation will continue to be a widely discussed and publicized topic with the

forthcoming Dodd-Frank regulations (Blundell-Wignall et al., 2008). The argument

presented by financial executives that the expansive regulation negatively impacts their

financial results will persist (Heneghan, 2010). The response from regulators will support

broadened regulation.

Timeframe. A similar research study with fewer companies and expanded over a

longer time period may provide new insights. Financial performance patterns may

emerge over a longer study period. Economic performance highs and lows may emerge in

response to the expansion or lessening of financial regulation.

Sector. Other quantitative researchers may want to test the study’s methodology

on a different financial services sector SIC code. The private equity sector has been less

regulated than companies in SIC 6211 (Dealbook, 2010). As the private equity

regulatory landscape evolves, regulatory researchers may have an interest in studying the

relationship of new regulation on the economic performance of these traditionally high-

performing companies.

Research design. A mixed-method approach to this research study may provide

the opportunity to view the research problem from multiple perspectives (Tashakkori &

Teddue, 2003). If researchers can investigate the problem quantitatively and

qualitatively, leaders may develop a more complete understanding of the problem.

Qualitative researchers could conduct open-ended interviews with financial executives to

103

gain financial executives’ perceptions of the relationship between financial regulation and

economic performance

Research Reflections. The present study was pursued in response to the

combative posturing related to increased regulation of the financial services sector in the

wake of the 2008 financial crisis. No significant relationships were disclosed through the

research process. Statistical significance should not be the goal of research (Black, 1999).

In this research study, finding no significant relationship between financial performance

and regulatory malfeasance adds to the study of ethical leadership.

Additional SIC codes could have been included in the study to expand the number

of companies researched. Forty-one SIC codes exist in the 6000-series that represent

different types of financial companies (SEC, 2011a). Broadening beyond SIC 6211 may

have skewed the results because of the differences in the companies’ current regulations

and prospective reform.

The findings in a mixed-method or qualitative study method may have been

different. Alternative methods could have provided an understanding of financial

executives’ perceptions related to governmental regulation and the impending Dodd-

Frank reforms. Exploring the paradigm under which financial executives make

regulatory decisions qualitatively would be an alternative to the statistical examination

that was conducted in this study.

A broader historical analysis may include additional insights on the relationship

between economic performance and regulatory compliance or malfeasance. An

expansive time period of a decade could have correlated statistical trending to regulatory

introductions. A specific point could have been selected with a before and after

104

comparative analysis. 2008, the year of the financial crisis could have been the study’s

focal point. Data collection and analysis could have compared the five-year’s prior and

after.

Summary

Prior to this study, the relationship between regulatory malfeasance and economic

performance had not been quantified or examined. In this study, no significant

relationship was found between the financial performance of the 74 companies sampled

under SIC 6211 and the companies’ respective regulatory malfeasance or compliance

over a six-year period. No significant relationship exists between the sampled

companies’ financial performance and the corresponding volume of the companies’

regulatory malfeasance.

Other than a marginally significant relationship between the mistreatment of

customers and financial performance, no significant relationships exist between the types

of regulatory infractions and the sample companies’ financial performance. The study’s

analysis and results are significant to leadership. The quantitative research results are

relevant to financial executives and governmental regulators.

Inferences are developed through the examination of the study’s findings. No

statistically significant relationship was found between economic performance and

regulatory malfeasance or compliance in the study’s research sample of 74 companies.

The volume of regulatory infractions was not found to have a statistically significant

relationship to economic performance. A marginally significant relationship was found

to exist between one type of regulatory malfeasance, the mistreatment of customers and

economic performance.

105

The study’s themes and sub-themes were discussed in Chapter 5. The themes and

sub-themes were supported by the study’s conceptual framework. Economic theorists

defend free markets and limited regulatory interference. Public-choice theorists view

individuals as motivated only by their own narrow interests. The results of this study

showing no significant relationship between economic performance and regulatory

compliance may support the public-choice theory.

Agency cost theorists purport that the opportunity for corporate malfeasance

exists as stakeholders’ interests diverge. Ethics theorists describe the deterioration of

public confidence in corporate leaders a result of their ethical breaches. The results of

this study showing no relationship between regulatory malfeasance and corporate

financial performance may devalue leadership ethical decision making and support

agency cost theorists.

Finding no statistical relationship between financial performance and regulatory

malfeasance or compliance adds to the study of ethical leadership. Concluding a

marginal statistical relationship between economic performance and the mistreatment of

customers extends the study of regulatory compliance. Future researchers may use the

results of the study to augment and improve the investigation.

106

References

Ahrens, T. (2008, June). The hidden ethics of corporate governances and the practical

uses of corporate governance codes: A commentary on Bhimani. Journal of

Management & Governance, 12, 149–152. doi:10.1007/s10997-00809062-9

Alvarez, R. I., & Astarita, M. J. (2001). Introduction to the blue sky laws. Retrieved from

http://www.seclaw.com/bluesky.htm

Alvesson, M., & Sandberg, J. (2011). Generating research questions through

problematization. Academy of Management Review, 36(2), 247–271.

Alzola, M. (2011). The reconciliation project: Separation and integration of business

ethics research. Journal of Business Ethics, 99, 19–36. doi:10.1007/s10551-011-

0746-8

An economic analysis of Section 16(b) of the Securities Act of 1934. (1976). William and

Mary Law Review, 18(2), 389–428. Retrieved from http://scholarship.law.wm.

edu/wmlr/vol18/iss2/8

Anderson, D., Buehler, K., Ceske, R., Ellis, B., Samandari, H., & Wilson, G. (2011,

March). Assessing and addressing the implications of new financial regulations

for the U.S. banking industry. McKinsey Working Papers on Risk, 25, 1–25.

Ang, J. S., & Richardson, T. (1994, January). The underwriting experience of commercial

bank affiliates to the Glass-Seagull Act: A re-examination of evidence for passage

of the Act. Journal of Banking and Finance, 18, 351–395.

Arnold, T., Hersch, P., Mulherin, H. J., & Netter, J. (1999, June). Merging markets.

Journal of Finance, 54(3), 1083–1107.

Asher, J. (1981). Glass-Seagull: A fresh look. ABA Banking Journal, 73(2), 62–70.

107

Association of Certified Fraud Examiners. (2011). About the ACFE. Retrieved from

http://www.acfe.com/about-the-acfe.aspx

Badertscher, B., & Burks, J. J. (2010, January 8). Accounting restatements and the

timeliness of disclosures. Retrieved from http://ssrn.com/abstract=1407233

Barnett, G. E. (1934, October). The Securities Act of 1933 and the British Companies

Act. Harvard Business Review, XIII(I), 1–18.

Barth, M. E. & Clinch, G. (2009, April/May). Scale effects in capital markets-based

accounting research. Journal of Business Finance & Accounting, 36(3&4), 253–

288. doi:10.111/j.1468-5957.2009.02143.x

Bartlett, D. (2003). Management and business ethics: A critique and integration of ethical

decision-making models. British Journal of Management, 14, 223–235.

Bartov, E., & Cohen, D. A. (2009, Fall). The numbers game in the pre- and post-

Sarbanes-Oxley eras. Journal of Accounting, Auditing & Finance, 24(4), 505–

534.

Baruch, S., & Saar, G. (2009, June). Asset returns and the listing choice of firms. Review

of Financial Studies, 22(6), 2239–2274.

Battalio, R. H., Hatch, B. C., & Loughran, T. (2011, May 16). Who benefited from the

disclosure mandates of the 1964 Securities Act Amendments? Publication of the

Center for the Study of Financial Regulation, 1–51.

Bauer, R., & Braun, R. (2010, November/December). Misdeeds matter: Long-term stock

price performance after the filing of class-action lawsuits. Financial Analysts

Journal, 66(6), 1 – 19.

108

Benmelech, E., & Moskowitz, T. J. (2010, June). The political economy of financial

regulation: Evidence from U.S. state usury laws in the 19 th

century. Journal of

Finance, LXV(3), 1029–1073.

Benson, C., & Mattingly, P. (2011, July 11). Dodd-Frank Act forcing banks to slim down,

reshape swaps: One year later. Retrieved from http://www.bloomberg.com/news/

2011-07-11/dodd-frank-act-forcing-banks-to-slim-down-reshape-swaps-one-year-

later.html

Benston, G. J. (1973, March). Required disclosure and the stock market: An evaluation of

the Securities Exchange Act of 1934. American Economic Review, 63(1), 133–

155.

Benston, G. J., & Kaufman, G. G. (1996, May). The appropriate role of bank regulation.

Economic Journal, 106, 688–697. Oxford, United Kingdom: Blackwell.

Bernstein, R., Binns, D., Hyman, M., & Staubus, M. (2002). The entrepreneur’s guide to

equity compensation (3 rd

ed.). Iowa City, IA: Foundation for Enterprise.

Berrone, P., Surroca, J., & Tribo, J. A. (2007). Corporate ethical identity as a determinant

of firm performance: A test of the mediating role of stakeholder satisfaction.

Journal of Business Ethics, 76, 35–53. doi:10.1007/s10551-006-9276-1

Bishop, M. (2004). Essential economics. New York, NY: Bloomberg Press.

Black, T. R. (1999). Doing quantitative research in the social sciences: An integrated

approach to research design, measurement and statistics. Thousand Oaks, CA:

Sage.

Blundell-Wignall, A., Atkinson, P., & Lee, S. H. (2008). The current financial crisis:

Causes and policy issues. OECD Journal: Financial Market Trends, 2, 11–31.

109

Borgers, T. (2009, November/December). Financial crisis inquiry commission is biggest

financial investigation since 1930s’ Pecora Commission. Fraud Magazine, 2(9),

para. 1–15. Retrieved from http://www.fraud-magazine.com/article.aspx?id-184

Brace, N., Kemp, R., & Snelgar, R. (2009). SPSS for psychologists (4 th

ed.). Houndmills,

Basingstoke, Hampshire, England: Palgrave Macmillan.

Brady, S. (2007, September). A-Z of financial terms. Euromoney, 38, 1–28.

Braga-Alves, M. V., & Shastri, K. (2011, Spring). Corporate governance, valuation, and

performance: Evidence from a voluntary market reform in Brazil. Financial

Management, 40(1), 139–157.

Braithwaite, J., Coglianese, C., & Levi-Faur, D. (2007). Can regulation and governance

make a difference? Regulation & Governance, 1, 1–7. doi:10.1111/j.1748-5991.2

007.000006.x

Brenkert, G. G. (2010, October). The limits and prospects of business ethics. Business

Ethics Quarterly, 20(4), 703–709.

Brommer, M., Gratto, C., Gravender, J., & Tuttle, M. (1987). A behavioral model of

ethical and unethical decision making. Journal of Business Ethics, 6(4), 265–280.

doi:10.1007/BF00382936

Brown, J. D. (2002). Statistics corner questions and answers about language testing

statistics: The Cronbach alpha reliability estimate. Shiken: JALT Testing &

Evaluation SIG Newsletter, 6(1), 16–18. Retrieved from http://www.jalt.org/test/

bro_13.htm

110

Brown, M. E., & Mitchell, M. S. (2010, October). Ethical and unethical leadership:

Exploring new avenues for future research. Business Ethics Quarterly, 20(4),

583–616.

Buchanan, J. M., & Tullock, G. (1962). The calculus of consent: Logical foundations of

constitutional democracy. Ann Arbor, MI: University of Michigan Press

Burr, S. I. (2010, April). The new FINRA Rule 5122 – a new approach to market

regulation? Real Estate Finance, 26(6), 13–16.

Burton, B. K., & Goldsby, M. G. (2010). The moral floor: A philosophical examination

of the connection between ethics and business. Journal of Business Ethics, 91,

145–154. doi:10.1007/s10551-009-0074-4

Caldari, K. (2010, Summer). Institutional economics and the concept of equilibrium.

Journal of Post Keynesian Economics, 32(4), 601–622. doi:10.2753/PKE0610-

3477320407

Chambers, D., Hermanson, D. R., & Payne, J. L. (2010, September). Did Sarbanes-Oxley

lead to better financial reporting? CPA Journal, 80(9), 24–27.

Cheffers, M., Whalen, D., & Usvyatsky, O. (2011, May). 2010 financial restatements: A

ten year comparison. Audit Analytics, 1–36. Retrieved from www.media.

compliance week. com/ documents/23/2010restatement_5709.pdf

Choi, T. H., & Jung, J. (2008). Ethical commitment, financial performance, and

valuation: An empirical investigation of Korean companies. Journal of Business

Ethics, 81, 447–463. doi:10.107/s10551-007-9506-1

Chonko, L. B., & Hunt, S. D. (1985). Ethics and marketing management: An empirical

examination. Journal of Business Research, 13(4), 339–359.

111

Christensen, L. B., Johnson, R. B., & Turner, L. A. (2011). Research methods, design,

and analysis (11 th

ed.). Boston, MA: Pearson Education.

Christensen, R. (1997). Log-linear models and logistic regression. (2 nd

ed.). New York,

NY: Springer-Verlag.

Christensen, S. L. (2008). The role of law in models of ethical behavior. Journal of

Business Ethics, 77, 451–461. doi:10.1007/s10551-007-9359-7

Chua, F., & Rahman, A. (2011). Institutional pressures and ethical reckoning by business

corporations. Journal of Business Ethics, 98, 307–329. doi:10.1007/s10551-010-

0551-9

Coco, G., & DeMeza, D. (2009, December). In defense of usury laws. Journal of Money,

Credit and Banking, 41(8), 1691–1703. doi:10.1111/j.1538-4616.2009.00274.x

Cohen, D. A., Dey, A., & Lys, T. Z. (2008). Real and accrual-based earnings

management in the pre- and post-Sarbanes-Oxley periods. Accounting Review,

83(3), 757–765. doi:10.2308/accr.2008.83.3.757

Commons, J. (1924). Legal foundations of capitalism, New York, NY: Macmillan Press.

Cook, T. D., & Campbell, D. T. (1979). Quasi-experimentation: Design & analysis issues

for field settings. Boston, MA: Houghton Mifflin.

Cooper, D. R., & Schindler, P. S. (2010). Business Research Methods (10 th

ed.). New

York: McGraw-Hill/Irwin.

Cosans, C. (2009). Does Milton Friedman support a vigorous business ethics? Journal of

Business Ethics, 87, 391–399. doi:10.1007/s10551-008-9927-5

Creative Research Systems. (2007). Sample size calculator. Retrieved from http://www.

survey system.com/sscalc.htm#one

112

Creswell, J. W. (2009). Research design: Qualitative, quantitative, and mixed methods

approaches (3 rd

ed.). Thousand Oaks, CA: Sage.

Croley, S. P. (2008). Regulation and public interests: The possibility of good regulatory

government. Princeton, NJ: Princeton University Press.

Cruz, D. (2007). Application of data screening procedures in stress research. New School

Psychology Bulletin, 5(2), 41–45.

Cumming, D., & Johan, S. (2008, September). Global market surveillance. American

Law and Economics Review, 2, 454-506. doi:10.1093/aler/ahn009

Day-by-day chronology of 1933. (1933, January 1). Wall Street Journal, p. 22. Retrieved

from ProQuest Historical Newspapers database at http://search.proquest.com/ez

proxy.apollolibrary.com/docview/128634395?accountid=35812

Dealbook. (2010, February 4). The regulatory landscape for private equity. The New York

Times. Retrieved from http://dealbook.nytimes.com/2010/02/04/the-regulatory-

landscape-for-private-equity/

Dechow, P. M., Ge, W., Larson, C. R., & Sloan, R. G. (2011, Spring). Predicting material

accounting misstatements. Contemporary Accounting Research, 28(1), 17–82.

doi:10.1111/j.1911-3846.2010.01041.x

DeConto, J. J. (2010, January 12). A new battle against usury: The people’s interest.

Christian Century, 27(1), 20–25. Retrieved from http>//www.christiancentury.

org/article/2010-01/peoples-interest

Deloitte Audit Group. (2011). Finishing in figures: Surveying financial statements in

annual reports. Retrieved from http://www.deloitte.com/assets/Dcom-United

113

Kingdom/Local%20Assets/Documents/Services/Audit/Corporate%20Governance

/UK_Audit_FinishingFigures.pdf

Denis, D. J., (2011). One-way repeated measures analysis of variance using SPSS.

Retrieved from

http://psychweb.psy.umt.edu/denis/datadecision/front/stat_II_2011/psyx_521_one

_way_repeated_measures.pdf

Dobbin, F., & Zorn, D. (2005). Corporate malfeasance and the myth of shareholder value.

Political Power and Social Theory, 17, 179–198. doi:10.1016/S0198-8719(04)17

006-3

Dodd-Frank Wall Street Reform and Consumer Protection Act. (2010). Retrieved from

http://www.sec.gov/about/laws/wallstreetreform-cpa.pdf

Dolmetsch, C. (2008, October 13). Subprime collapse to global financial meltdown:

Timeline. Retrieved from http://www.bloomberg.com/apps/news?pid=21070001

&sid=aleqkSjAAw10

Dombalagian, O. H. (2010). Requiem for the bulge bracker? Revisiting investment bank

regulation. Indiana Law Journal, 85, 777–796.

Donaldson, L. (2008). Ethics problems and problems with ethics: Toward a pro-

management theory. Journal of Business Ethics, 78, 299–311. doi:10.1007/s1055

1-006-9336-6

Dubinsky, A. J., & Loken, B. (1989). Analyzing ethical decision making in marketing.

Journal of Business Research, 19(2), 83–107.

Durnev, A., Fox, M. B., Morck, R., & Yeung, B. (2009). The effectiveness of mandatory

disclosure: An empirical test of the line of business regulations. Retrieved from

114

http://www.tilburguniversity.edu/research/institutes-and-research-groups/tilec/

org/weekly/MerrittFox.pdf

Edgar, R. J. (2009). The future of financial regulation: Lessons from the global financial

crisis. Australian Economic Review, 42(4), 470–476.

Enderle, G. (2010, October). Clarifying the terms of business ethics and CSR. Business

Ethics Quarterly, 20(4), 730–732.

Ethics Resource Center. (2009). Resources: Organizational ethics. Retrieved from http://

www. ethics.org/resource/organizational-ethics

Ezirim, C. B., Nwibere, B. M., & Emecheta, B. C. (2010, Summer). Organizational

culture and performance: The Nigerian experience. International Journal of

Business and Public Administration, 7(1), 40–56.

Fard, H. D., & Noruzi, M. R. (2011, January). A snap shot on business ethics and ethic in

business. Interdisciplinary Journal of Contemporary Research in Business, 2(9),

496–500.

Fawcett, J., & Garity, J. (2009). Evaluating research for evidence-based nursing.

Philadelphia, PA: F.A. Davis.

Federal Service Desk. (2010). Answer Center. Retrieved from https://www.fsd.gov/app/

answers/detail/a_id/183

Fehr, S. C. (2010, April 2). The Great Recession of ’74, ’82, ’92, and ’07. Retrieved from

http://www.stateline.org/live/details/story?contentId-473390

Ferrell, A. (2007). Mandated disclosure and stock returns: Evidence from the over-the-

counter market. Journal of Legal Studies, 36, 213–251.

115

Ferrell, O. C., & Gresham, L. G. (1985). A contingency framework for understanding

ethical decision making in marketing. Journal of Marketing, 49, 87–96.

Field, A. (2009). Discovering statistics using SPSS. (3 rd

ed.). Thousand Oaks, CA: Sage.

Fieser, J. (2010). Business ethics. Retrieved from http://www.utm.edu/staff/jfieser/fita/

research/Busbook.htm

Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention, and behavior: An

introduction to theory and research. Reading, MA: Addison-Wesley.

Fisher, R. A. (2003). Statistical methods experimental design and scientific inference.

New York, NY: Oxford University.

Fox, M. B., Durnev, A., Morck, R., & Yeung, B. (2003, December). Law, share price

accuracy and economic performance: The new evidence. Michigan Law Review,

102(3), 331–386.

Francis, T. (2009, June 26). Financial regulation: Industry objections increasing.

Bloomberg BusinessWeek Online, para. 14. Retrieved from http://www.business

week.com/print/bwdaily/dnflash/content/jun2009/db20090626_446404.html

Frankel, T., & Cunningham, L. A. (2007, January). The mysterious ways of mutual funds:

Market timing. Retrieved from http://lawdigitalcommons.bc.edu/lsfp/187/

Freeland, C. (2010, July/August). Bubble bath. Foreign Policy, 180, 17–18. Retrieved

from EBSCOHOST database at https://ehis.ebscohost.com/eds/delivery?sid=8b36

436d-f5ac-40e9-981f-f74760ac346a%40.htm

Friedman, H. M., & Friedman, L. W. (2010). Lessons from the twin mega-crises: The

financial meltdown and the BP oil spill. Journal of Business Systems,

Governance, and Ethics, 5(4), 34–45.

116

Friedman, M. (Ed.). (1953). Essays in positive economics. Chicago, IL: University of

Chicago Press.

Friedman, M. (1978, October). What is America? The Saturday Evening Post, 16–18,

104.

Fritzsche, D. J., & Becker, H. (1984). Linking management behavior to ethical

philosophy – an empirical investigation. Academy of Management Journal, 27(1),

166–175.

Fung, M. V. (2010, Winter). Comments on “Can post Keynesians make better use of

behavioral economics?” Journal of Post Keynesian Economics, 33(2), 235–249.

doi:10.2753/PKE0160-3477330202

Gall, M. D., Gall, J. P., & Borg, W. R. (2007). Educational research: An introduction (8 th

ed.). Boston, MA: Allyn & Bacon.

Gaudine, A., & Thorne, L. (2001). Emotion and ethical decision-making in organizations.

Journal of Business Ethics, 31(2), 175–187.

Gelo, O., Braakmann, D., & Benetka, G. (2008, September 16). Quantitative and

qualitative research: Beyond the debate. Integrative Psychological and

Behavioral Science, 42, 266–290. doi:10.1007/s12124-008-9078-3

Geva, A. (2006). A typology of moral problems in business: A framework for ethical

management. Journal of Business Ethics, 69, 133–147.

Giorgi, A. (1997). The theory, practice, and evaluation of the phenomenological method

as a qualitative research procedure. Journal of Phenomenological Psychology,

28(2), 235–260.

117

Giroux, G. (2008, Winter). What went wrong? Accounting fraud and lessons from the

recent scandals. Social Research, 75(4), 1205–1238.

Gordon, R. K. (2010). On the use and abuse of standards for law: Global governance and

offshore financial centers. North Carolina Law Review, 88, 503–588.

Graham, J. R., Harvey, C. R., & Rajgopal, S. (2005, January 11). The economic

implications of corporate financial reporting. Retrieved from http://ssrn.com/

abstract=491627

Grande, A., Puri, M., Saunders, A., & Walter, I. (1997, Winter). Bank underwriting of

debt securities: Modern evidence. Review of Financial Studies, 10(4), 1175–1202.

Greenstone, M., Oyer, P., & Vissing-Jorgensen, A. (2006, May). Mandated disclosure,

stock returns, and the 1964 Securities Acts amendments. Quarterly Journal of

Economics, 121, 399–460.

Greve, H. R., Palmer, D., & Pozner, J.-E. (2010). Organizations gone wild: The causes,

processes, and consequences of organizational misconduct. Academy of

Management Annals, 4(1), 53–107. doi:10.1080/19416521003654186

Grimes, D. A., & Schulz, K. F. (2002, January 12). Descriptive studies: What they can

and cannot do. Lancet, 359, 145–149. Retrieved from https://research.chm.msu.

edu/Resources/2/20descriptive/20studies.pdf

Groves, R. M. (2006). Nonresponse rates and nonresponse bias in household surveys.

Public Opinion Quarterly, 70(5), 646–675. doi:10.1093/poq/nf1033

Hall v. Geiger-Jones Co., 242 U.S. 539 (1917).

Harned, P. J. (2010). Letter from ERC President Patricia J. Harned, Ph.D. Retrieved

from http://www.ethics.org/page/about-erc

118

Harshman, C. L., & Harshman, E. F. (2008). The Gordian knot of ethics: Understanding

leadership effectiveness and ethical behavior. Journal of Business Ethics, 78,

175–192. doi:10.1007/s10551-006-9318-8

He, L., & Ho, S.-J. K. (2011). Monitoring costs, managerial ethics and corporate

governance: A modeling approach. Journal of Business Ethics, 99, 623–635. doi:

10.1007/s10551-010-0672-1

Healy, P. M., & Palepu, K. (2003, Spring). The fall of Enron. Journal of Economic

Perspectives, 17(2), 1–46. doi:10.2139/ssrn.417840

Heath, J. (2009, October). The uses and abuses of agency theory. Business Ethics

Quarterly, 19(4), 497–528.

Hegarty, W. H., & Sims, H. P. (1978). Some determinants of unethical decision behavior:

An experiment. Journal of Applied Psychology, 63(4), 451–457.

Helin, S., & Sandstrom, J. (2007). An inquiry into the study of corporate codes of ethics.

Journal of Business Ethics, 75, 253–271. doi:10.1007/s10551-006-9251-x

Hendrickson, J. M. (2001). The long and bumpy road to Glass-Steagall Reform: A

historical and evolutionary analysis of banking legislation. American Journal of

Economics and Sociology, 60(4), 949–879.

Heneghan, T. (2010, March 4). Ethics angle missing in financial crisis debate. Reuters.

Retrieved from

http://www.reuters.com/assets/print?aid=USTRE6231H320100304

Holmes, G. (1892). Usury in law, in practice and in psychology. Political Science

Quarterly, 7(3), 431–467.

119

Hopkins, W. G. (2008, July). Quantitative research design. Sportscience, 4(1). Retrieved

from http://www.sportsci.org/jour/0001/wghdesign.html

Hotelling, H. (1936). Relations between two sets of variants. Biometrika, 28, 321–377.

Howell, D. (2007). Statistical methods for psychology. (6 th

ed.). Thomson, Australia:

Cengage.

Hughes, T., Bence, D., Grisoni, L., O’Regan, N., & Wornham, D. (2011). Scholarship

that matters: Academic-practitioner engagement in business and management.

Academy of Management Learning & Education, 10(1), 40–57.

Hunt, S. D., & Vitell, S. (1986). A general theory of marketing ethics. Journal of

Marketing, 6, 5–16.

IASB forms an expert advisory panel to discuss the valuation of financial instruments in

inactive markets. (2008, June 3). Retrieved from http://www.ifrs.org/News/

Announcements+and+Speeches/IASB+forms+an+expert+advisory+panel.htm

IBM Corporation. (2012). IBM SPSS Statistical Editions. Retrieved from Federal Service

Desk. (2010). Answer Center. Retrieved from https://www.fsd.gov/app/

answers/detail/a_id/183

IDRE Research Technology Group. (2013). What statistical test should I use? Statistical

analyses using SPSS. Retrieved from

http://www.ats.ucla.edu/stat/spss/whatstat/whatstat.htm

Industry profile: Finance & insurance sector. (2011). Retrieved from http://universityof

phoenix.firstresearch-

learn.com.ezproxy.apollolibrary.com/industry.aspx?pid=368& chapter=0

120

Jackson, K. E. (2008, Fall). De-biasing scale compatibility effects when investors use

nonfinancial measures to screen potential investments. Contemporary Accounting

Research, 25(3), 803–826. doi:1506/car.25.3.6

James, R. (2006, January/February). Corporate ethics are in the eye of the beholder, says

Ron James. Across the Board, 12, 45.

Jarrell, G. A. (1981). The economic effects of federal regulation of the market for new

securities issues. Journal of Law and Economics, 24, 613–675.

Johnson, S. A., Ryan, H. E., Jr., & Tian, Y. S. (2009). Managerial incentives and

corporate fraud: The sources of incentives matter. Review of Finance, 13, 115–

145. doi:10.1093/rof/rfn014

Jones, T. M. (1991). Ethical decision making by individuals in organizations: An issue-

contingent model. Academy of Management Review, 16(2), 366–395.

Joshua, O. (2011, May). Comparative analysis of the impact of mergers and acquisitions

on financial efficiency of banks in Nigeria. Journal of Accounting and Taxation,

3(1), 1–7.

JP Morgan’s Dimon lashes out at Dodd-Frank. (2011, April 6). Financial News, para. 1–

13. Retrieved from http://www.efinancialnews.com/story/2011-04-06/dimon-

lashes-dodd-frank

Kalish, I. (2011, July 26). Global economic outlook: Policymakers confront reality.

Retrieved from http://www.deloitte.com/view/en_GX/global/62d16b8c08c21310

VgnVCM2000001b56f00aRCRD.htm

121

Kalluru, S., & Bhat, S. (2009). Does foreign bank entry affect operations of domestic

banks? A study on Indian public sector banks. ICFAI Journal of Managerial

Economics, 7(3/4), 40–51.

Kang, S. A., & Chun, S. B. (2010). Characteristics of firms engaged in real activity-based

earnings management: Evidence from Korea. Korean Accounting Review, 35(2),

35–70.

Kang, S.-A., & Kim, Y.-S. (2011, February). Does earnings management amplify the

association between corporate governance and firm performance? Evidence from

Korea. International Business & Economics Research Journal, 10(2), 53–66.

Kaufman, B. E. (2007). The institutional economics of John R. Cummons: Complement

and substitute for neoclassical economic theory. Socio-Economic Review, 5, 3–45.

doi:10.1093/ser/mwl061

Kavousy, E., Fard, H. R. V., Kangarluei, S. J., & Bayazidi, A. (2010). The relationship

between ethics criteria and earnings management in accepted companies in

Tehran stock exchange. European Journal of Social Science, 14(3) 455–463.

Kedia, S., & Rajgopal, S. (2011). Do the SEC’s enforcement preferences affect corporate

misconduct? Retrieved from http://ssrn.com/abstract-1750371

Kelly, E. P., Bramhandkar, A., & Movassaghi, H. (2009). A case study of ethics and

mutual funds mismanagement at Putnam. Ethics & Behavior, 19(1), 25–35. doi:

10.1080/10508420802623658

Kerlinger, F. N. (1986). Foundations of behavioral research (3 rd

ed.). New York, NY:

Holt, Rinehart and Winston.

122

Kerlinger, F. N., & Pedhazur, E. J. (1973). Multiple regression in behavioral research.

New York, NY: Holt, Rinehart and Winston.

Kim, K. (2009, March). Adam Smith’s theory of economic history and economic

development. European Journal of the History of Economic Thought, 16(1), 41–

64. doi:10.1080/09672560802707407

Kravet, T., & Shevlin, T. (2009, May 12). Accounting restatement and information risk.

Review of Accounting Studies, 15(2), 264–294. doi:10.1007/s11142-009-9103-x

Kroszner, R. S., & Rajan, R. G. (1993, July). Is the Glass-Seagull Act justified? A study

of the U.S. experience with universal banking before 1933. American Economic

Review, 84(4), 810–832.

Kujala, J., Lamsa, A.-M., & Penttila, K. (2011). Managers’ moral decision-making

patterns over time: A multidimensional approach. Journal of Business Ethics,

100, 191–207. doi:10.1007/s10551-010-0467-4

Kyle, A. S., & Viswanathan, S. (2008, January 14). Price manipulation in financial

markets: How to define illegal price manipulation. American Economic Review:

Papers & Proceedings, 98(2), 274–279. doi:10.1257/aer.98.2.274

Laczniak, G., & Inderrieden, E. J. (1987). The influence of stated organizational concern

upon ethical decision making. Journal of Business Ethics, 6, 297–307.

Lamiell, J. T. (1998). Nomothetic and idiographic: Contrasting Windelband’s

understanding with contemporary usage. Theory & Psychology, 8(1), 23–38.

Lan, L. L., & Heracleous, L. (2010). Rethinking agency theory: The view from law.

Academy of Management Review, 35(2), 294–314.

123

Landis, J. M. (1934, May). Should the Federal Securities Act of 1933 be modified?

Congressional Digest, 13(4), 137–145.

Larsen, J., Buckberg, E., Overdahl, J.A. (2011, June 23). SEC settlements trends: 1H11

update. Insight in Economics. Retrieved from http://www.nera.com/67_7346.htm

Leedy, P. D., & Ormrod, J. E. (2010). Practical research: Planning and design (9 th

ed.).

Upper Saddle River, NJ: Pearson Education.

Lenth, R. V. (2001, March). Some practical guidelines for effective sample-size

determination. Unpublished manuscript, Department of Statistics, University of

Iowa, Iowa City. Retrieved from http://www.stat.uiowa.edu/techrep/tr303.pdf

Lev, B. (1989). On the usefulness of earnings and earnings research: Lessons and

directions from two decades of empirical research. Journal of Accounting

Research, 27, 153–201.

Levin, Y. (2010, Spring). Recovering the case for capitalism. National Affairs, 3, 121–

136. Retrieved from www.NationalAffairs.com/doclib/20100317_Levin.pdf

Lewis, S., & Ricciulli, F. (1990). Restructuring the Italian banking industry. New York,

NY: Salomon Brothers.

Li, S., & Xu, C., (2008, September). Does financial regulation matter? Market volatility

and the US 1933/34 Acts. Unpublished manuscript, London School of Economics,

England.

Lin, J. W., & Hwang, M. I. (2010). Audit quality, corporate governance, and earnings

management: A meta-analysis. International Journal of Accounting, 14, 57–77.

doi:10.1111/j.1099-1123-2009-00403.x

124

Linck, J. S., Lopez, T. J., & Rees, L. (2007, April 18). The valuation consequences of

voluntary accounting changes. Review of Quantitative Financial Accounting, 28,

327–352. doi:10.1007/s11156-007-0016-0

Lodico, M. G., Spaulding, D. T., & Voegtle, K. H. (2010). Methods in educational

research from theory to practice (2 nd

ed.). San Francisco, CA: Jossey-Bass.

Lopez, J. (2010, July). Re-enactment of the Glass-Seagull Act 1933: Is this a step in the

right direction for global financial reform? Law and Financial Markets Review,

4(4), 428–432.

Loughran, T., & McDonald, B. (2011, August 31). Measuring reliability in financial

disclosures. Retrieved from http://ssrn.com/abstract=1920411

Luban, D. (2003). Making sense of moral meltdowns. In D. L. Rhode (Ed.), Moral

leadership: The theory and practice of power, judgment, and policy (pp. 57–75).

San Francisco, CA: Jossey-Bass.

Lucko, G., & Mitchell, Z. W. (2010, January). Quantitative research: Preparation of

incongruous economic data sets for archival data analysis. Journal of

Construction Engineering and Management, 136(1), 49 – 57.

Luke, M. (2006, March). The impact of Sarbanes-Oxley as perceived by executive

leadership: An exploratory case study (Unpublished doctoral dissertation).

University of Phoenix, AZ.

Ma, Z. (2009). The status of contemporary business ethics research: Present and future.

Journal of Business Ethics, 90, 255–265. doi:10.1007/s10551-010-0420-6

MacDonald, R. F., III, & Lettow, P. V. (2010, August). More than just financial reform:

Analysis and observations on the Dodd-Frank Wall Street Reform and Consumer

125

Protection Act [White paper]. Venulex Legal Summaries 2010: Q3 Special

Section, 2, 1–93.

MacLean, T. (2008). Framing and organizational misconduct: A symbiotic interactionist

study. Journal of Business Ethics, 78, 3–16.

Mahoney, P. G., & Mei, J. (2008, January). Mandatory vs. contractual disclosure in

securities markets: Evidence from the 1930s. Beijing, China: Cheung Kong

Graduate School of Business.

Marciukaityte, D., Szewczyk, S. H., Uzun, H., & Varma, R. (2006). Governance and

performance changes after accusations of corporate fraud. Financial Analysts

Journal, 62(3), 32–41.

Mashaw, J. L. (1997). Greed, chaos, and governance: Using public choice to improve

public law. New Haven, CT: Yale University Press.

Mathews, A. F. (1972). Criminal prosecutions under the Investment Company Act of

1940 and the Investment Advisers Act. Boston College Law Review, 13(5), 1257–

1273. Retrieved from http://lawdigialcommons.bc.edu/bclr/vol13/iss5/11

Mayring, P. (2007, September). On generalization in qualitatively oriented research.

Forum: Qualitative Social Research, 8(3), 26–33. Retrieved from http://www.

qualitative-research.net/index.php/fqs/article/viewArticle/291/641

Mester, L. J. (1996, July-August). Repealing Glass-Seagull: The past points the way to

the future. Federal Reserve Bank of Philadelphia Business Review, 3–18.

Retrieved from http://www.philadelphiafed.org/research-and-data/publications/

business-review/1996/july-august/glass-steagall.cfm

126

Mill, J. S. (1891). Principles of political economy: People’s edition. London, England:

Longmans, Greens.

Miller, D., & Sardais, C. (2011, May). Angel agents: Agency theory reconsidered.

Academy of Management Perspectives, 25(2), 6–13. doi:10.5465/AMP.2001.6102

0798

Mishina, Y., Dykes, B. J., Block, E. S., & Pollock, T. G. (2010). Why good firms do bad

things: The effects of high aspirations, high expectations, and prominence on the

incidence of corporate illegality. Academy of Management Journal, 53(4), 701–

722.

Mitchell, T. R. (1985). An evaluation of the validity of correlational research conducted

in organizations. Academy of Management Review, 10(2), 192–205.

Moore, D. A., & Flynn, F. J. (2008). The case for behavioral decision research in

organizational behavior. Academy of Management Annals, 2(1), 399–431. doi:10.

1080/19416520802211636

Myers, J. L., & Well, A. D. (2003). Research design and statistical analysis (2 nd

ed.).

Mahwah, NJ: Lawrence Erlbaum.

National Association of Securities Dealers Automated Quotations. (2011). Company list.

Retrieved from http://www.nasdaq.com/screening/company-list.aspx

Neuman, W. L. (2006). Social research methods: Qualitative and quantitative

approaches (6 th

ed.). Boston, MA: Pearson Education.

Neuman, W. L. (2011). Social research methods: Qualitative and quantitative

approaches (7 th

ed.). Boston, MA: Pearson Education.

127

Noordzij, M., Dekker, F. D., Zoccali, C., & Jager, K. J. (2011). Sample size calculations.

Nephron Clinical Practice, 118(4), 319–323.

North, D. C. (1990). Institutions, institutional change, and economic performance.

Cambridge, United Kingdom: Cambridge UP.

Novak, W. J. (2010). Law and the social control of American capitalism. Emory Law

Journal, 60, 377–405.

Nyberg, A. J., Fulmer, I. S., Gerhart, B., & Carpenter, M. A. (2010). Agency theory

revisited: CEO return and shareholder interest alignment. Academy of

Management Journal, 53(5), 1029–1049.

O’Brien, J. (2010). The future of financial regulation: Enhancing integrity through

design. Sydney Law Review, 32, 63–85.

O’Leary-Shuler, L. (2009, July 15). Guide to data collection methods. Retrieved from

http://web.mit.edu/tll/assessment-evaluation/ae-datacollection-methods-lols.pdf

Organisation for Economic Cooperation & Development. (2010). Policy framework for

effective and efficient financial regulation: General guidance and high-level

checklist. OECD Journal: Financial Market Trends, 2, 267–321.

Owens, H. F. (1964, October 16). The Securities Acts Amendment of 1964. Retrieved

from http://www.sec.gov/news/speech/1964/101664owens.pdf

Pajares, F. (2007). The elements of a proposal. Retrieved from http://des.emory.edu/mfp/

proposal.html

Pankoff, L. D., & Virgil, R. L. (1970, April). On the usefulness of financial statement

information: A suggested research approach. Accounting Review, 45(2), 269–279.

128

Park, J. J. (2009, October). Assessing the materiality of financial misstatements. Brooklyn

Law School Legal Studies Research Papers, 109, 514–546. Retrieved from http://

ssrn.com/abstract-1158566

Paulson, H. M. (2010). Inside the race to stop the collapse of the global financial system.

London, England: Headline Book.

Peach, T. (2009). Adam Smith and the labor theory of (real) value: A reconsideration.

History of Political Economy, 41(2), 383–406. doi:10.1215/00182702-2009-007

Pear, A. M. (2011, June). Understanding the information contained in EDGAR filings.

Los Angeles Lawyer, 34(4), 8.

Pecora denounces stock manipulation. (1933, February 19). The New York Times, p. 33.

Retrieved from http://search.proquest.com/ezproxy.apollolibrary.com/docview/

100757790?accountid=35812

Pellerin, S. R., Walter, J. R., & Wescott, P. E. (2009, Spring). The consolidation of

financial regulation: Pros, cons, and implications for the United States. Economic

Quarterly, 95(2), 121–160.

Peng, C-Y. J., Lee, K. L., & Ingersoll, G. M. (2002, September/October). An introduction

to logistic regression analysis and reporting. The Journal of Educational

Research, 96(1), 3–14.

Perols, J. L. & Lougee, B. A. (2011, June). The relation between earnings management

and financial statement fraud. Advances in Accounting, 27(1), 39–53.

Petrick, J. A., & Quinn, J. F. (2000) The integrity capacity construct and moral progress

in business. Journal of Business Ethics, 23, 3–18.

129

Pohl, N. (2007). Where is Wall Street? Financial geography after 09/11. Industrial

Geographer, 2(1), 72–93.

Polit, D. F., & Tatano Beck, C. (2010, November). Generalization in quantitative and

qualitative research: Myths and strategies. International Journal of Nursing

Studies, 47(11), 1451–1458. doi:10.1016/j.ijnurstu.2010.06.004

Powell, B., & Stringham, E. P. (2009). Public choice and the economic analysis of

anarchy: A survey. Public Choice, 140, 503–538. doi:10.1007/s11127-009-9407-1

Pratch, L. (2009, Winter). Integrity in business executives. Journal of Private Equity,

12(1), 1–9.

Prentice, R. A., & Donelson, D. C. (2010, Spring). Insider trading as a signaling device.

American Business Law Journal, 47(1), 1–73.

Price, M. E., Verhulst, S., & Morgan, L. (2007). The speculative influence of academic

research on the making of communications policy: Reflections, recollections and

informal perspectives. Unpublished manuscript, Annenberg School for

Communication, University of Pennsylvania, Philadelphia.

Puri, M. (1994, January). Commercial banks in investment banking: Conflict of interest

or certification role? Journal of Financial Economics, 40, 397–418.

Raidy, C. S. (2011, June). Regulatory update: Selected topic summary overview of the

Dodd-Frank and Consumer Protection Act. Real Estate Finance, 28(1), 14–16.

Randolph, J. J. (2009, June). A guide to writing the dissertation literature review.

Practical Assessment, Research & Evaluation, 14(13), 1–13. Retrieved from

http://pareonline.net/getvn.asp?v+14&n=13

130

Rashkover, B. W., & Winter, C. B. (2005, October 7). The impact of Sarbanes-Oxley on

SEC enforcement in public company disclosure cases – Part 1. International

Disclosure and Governance, 2(4), 312–324.

Rassier, D. G., & Earnhart, D. (2011). Short-run and long-run implications of

environmental regulation on financial performance. Contemporary Economic

Policy, 29(3), 357–373.

Rest, J. R. (1986). Moral development: Advances in research and theory. New York, NY:

Praeger.

Rhee, R. J. (2009, November 18). The Madoff scandal, market regulatory failure and the

business education of lawyers. Journal of Corporation Law, 35(2), 363–392.

Rhode, D. L. (Ed.). (2006). Moral leadership: The theory and practice of power,

judgment, and policy. San Francisco, CA: Jossey-Bass.

Rivlin, A. M. (2009, July 21). Reducing systematic risk in the financial sector: Hearing

before the House Committee on Financial Services & Senate Committee on

Banking, Housing and Urban Affairs (testimony of Alice M. Rivlin).

Roche, C. (1999). Impact assessment for development agencies: Learning to value

change. Oxford, England: Oxfam.

Romano, R. (2005, June). The Sarbanes-Oxley Act and the making of quack corporate

governance (Yale Law Journal ICF Working Paper No. 05-23). Retrieved from

SSRN:http://ssrn.com/abstract-749524

Roosevelt, F. D. (1932). Only thing we have to fear is fear itself. Retrieved from http://

historymatters.gmu.edu/d/5057/

131

Ross, S. A., Westerfield, R. W., Jaffe, J. F., & Jordan, B. D. (2009). Corporate finance:

Core principles & applications (2 nd

ed.). New York, NY: McGraw Hill/Irwin.

Ross, S. A., Westerfield, R. W., & Jordan, B. D. (2008). Fundamentals of corporate

finance (8 th

ed.). Columbus, OH: McGraw-Hill Higher Education.

Rubin, A. (2008). Practitioner’s guide to using research for evidence-based practice.

Hoboken, NJ: John Wiley & Sons.

Ryan, L. V., Buchholtz, A. K., & Kolb, R. W. (2010, October). New directions in

corporate governance and finance: Implications for business ethics research.

Business Ethics Quarterly, 20(4), 673–694.

S. Res. 74, 73d Cong. (1933) (enacted).

S. Res. 162, 73d Cong. (1933) (enacted).

S. Res. 565, 88th Cong. (1964) (enacted).

S. Res. 745, 107th Cong. (2002) (enacted).

S. Res. 789, 76th Cong. (1940) (enacted).

S. Res. 881, 73d Cong. (1934) (enacted).

S. Res. 4173, 111 th

Cong. (2010) (enacted).

Salkind, N. J. (2009). Exploring research (7 th

ed.). Upper Saddle River, NJ: Pearson

Education.

Schweitzer, G. (2009). Quantitative versus qualitative research. Retrieved from http://

www.articlesbase.com/business-articles/quantitative-versus-qualitative-research-

994628.html

Schweitzer, M. E., Ordonez, L., & Douma, B. (2004). Goal setting as a motivator of

unethical behavior. Academy of Management Journal, 47(3) 422–432.

132

Securities Act of 1933. (1933). Retrieved from http://www.sec.gov/about/laws/sa33.pdf

Securities Exchange Act of 1934. (1934). Retrieved from http://www.sec.gov/about/laws.

sea 34. pdf

Securities and Exchange Commission. (2008). What we do. Retrieved from http://www.

sec.gov/about/whatwedo. shtml

Securities and Exchange Commission. (2010). Laws and regulations. Retrieved from

http://www.sec.gov/about/laws.shtml

Securities and Exchange Commission. (2011a). Division of corporate finance: Standard

industrial classification (SIC) code list. Retrieved from

http://www.sec.gov/info/edgar/siccodes.htm

Securities and Exchange Commission. (2011b). Enforcement actions. Retrieved from

http://www. sec.gov/spotlight/enf-actions-fc.shtml

Securities and Exchange Commission. (2012). EDGAR filer manual (Volume II).

Retrieved from http://www.sec.gov/info/edgar/edgarfm-vo12-v5.pdf

Securities Law Advisory. (2011, January 7). 2010 SEC regulatory update. Retrieved

from http://www.alston.com/files/Publication/967e983f-ad9a-4bf4-9bdf-53a955

174eba/Presentation/PublicationAttachment/4dc939c6-6c09-4403-85e9-5c8dab24

3745/SEC_RegulatoryUpdate2010.pdf

Securities Law Institute. (2010). EDGAR. Retrieved from http://www.securitieslaw

institute.com/edgar.html

Seligman, J. (2003, December 5). Cautious evolution or perennial irresolution: Self-

regulation and market structure during the first 70 years of the Securities and

133

Exchange Commission. Retrieved from http://business.nd.edu/center_for_the_

study_of_financial_regulation/

Shadnam, M., & Lawrence, T. B. (2011, July). Understanding widespread misconduct in

organizations: An institutional theory of moral collapse. Business Ethics

Quarterly, 21(3), 379–407.

Sherman, M. (2009, July). A short history of financial deregulation in the United States.

Washington, DC: Center for Economic and Policy Research. Retrieved from

http://www.openthegovernment.org/sites/default/files/otg/dereg-timeline-2009-

07.pdf

Simon, C. J. (1989, June). The effect of the 1933 Securities Act on investor information

and the performance of new issues. American Economic Review, 79(3), 295–318.

Simon, M. (2006). Dissertation and scholarly research: Recipes for success. Dubuque,

IA: Kendall Hunt.

Simon, M. K. (2010). Dissertation & scholarly research. Seattle, WA: CreateSpace.

Sims, R. R., & Brinkman, J. (2003). Enron ethics (or: Culture matters more than codes).

Journal of Business Ethics, 45(3), 243–256.

Sinha, P., & Gadarowski, C. (2010). The efficacy of regulation fair disclosure. Financial

Review, 45, 331–354.

Smith, A. (1776). An inquiry into the nature and causes of the wealth of nations. New

York, NY: Oxford University Press.

Smith, G. T. (2005). On construct validity: Issues of method and measurement.

Psychological Assessment, 17(4), 396–408. doi:10.1037/1040-3390.17.4.396

134

Smith, R. T., Bradley, M., & Jarrell, G. (1986, Winter). Studying firm-specific effects of

regulation with stock market data: An application to oil price regulation. Rand

Journal of Economics, 17(4), 467–489.

State of Wisconsin Department of Financial Institutions. (2010). A brief history of

securities regulation. Retrieved from http://www.wdfi.org/fi/securities/

regexempt/history.htm

Steinberg, W. J. (2011). Statistics alive! (2 nd

ed.). Thousand Oaks, CA: Sage.

Stevens, J. P. (2002). Applied multivariate statistics for the social sciences (4 th

ed.).

Mahwah, NJ: Lawrence Erlbaum Associates.

Stigler, G. J. (1960, May). The influence of events and policies on economic theory.

American Economic Review, 50(2), 36–45. Retrieved from http://www.jstor.org/

pss/1815008

Stigler, G. J. (1965, March). The economist and the state. American Economic Review,

LV(1), 1–18.

Streeck, W. (2009, September). Industrial relations today: Reining in flexibility.

Economics, Management & Financial Markets, 4(3), 15–36.

Stuart, D. M., & Wilson, D. A. (2009, August). Disclosure obligations under the federal

securities laws in government investigations. Business Lawyer, 64(4), 973–998.

Sturn, R. (2010). On making full sense of Adam Smith. Homo Oeconomicus, 27(3), 263–

287.

Svensson, G., & Wood, G. (2008). A model of business ethics. Journal of Business

Ethics, 77, 303–322. doi:10.1007/s10551-007-9351-2

135

Symonds, J. E., & Gorard, S. (2010). Death of mixed methods? Or the rebirth of research

as a craft. Evaluation & Research in Education, 23(2), 121–136. doi:10.1080/095

00790.2010.483514

Tabarrok, A. (1998). The separation of commercial and investment banking: The

Morgans vs. the Rockefellers. Quarterly Journal of Austrian Economics, 1(1), 1–

18.

Tamny, J. (2010, February 22). The naïve conceit of bank regulations. Investors Business

Daily Online. Retrieved from http://news.investors.com/Article/521655/2010021

91758/The-Naive-Conceit-Of-Bank-Regulations.htm

Tashakkori, A., & Teddue, C. (Eds.) (2003). Handbook of mixed methods in social and

behavioral research. Thousand Oaks, CA: Sage.

Tenbrunsel, A. E., & Smith-Crowe, K. (2008). Ethical decision making: Where we’ve

been and where we’re going. Academy of Management Annals, 2(1), 545–607.

doi:10.1080/19416520802211677

Thomadakis, S. B. (2007, November 14). What makes good regulation? Paper presented

at the meeting of the IFAC Council, Mexico City.

Tran, B. (2008). Paradigms in corporate ethics: The legality and values of corporate

ethics. Social Responsibility Journal, 4(1/2), 158–171. doi:10.1108/174711108

10856929

Trevino, L. K. (1992). Moral reasoning and business ethics: Implications for research,

education and management. Journal of Business Ethics, 11, 445–459.

Trevino, L. K., Weaver, G. R., & Reynolds, S. J. (2006). Behavioral ethics in

organizations: A review. Journal of Management, 32(6), 951–990.

136

Trochim, W. M. K. (2006a). Analysis. Retrieved from http://www.socialresearchmethods.

net/kb/statinf.php

Trochim, W. M. K. (2006b). Types of reliability. Retrieved from http://www.social

researchmethods.net/kb/reltypes.php

Trochim, W. M. K. (2006c). Unit of analysis. Retrieved from http://www.socialresearch

methods.net/kb/unitanal.htm

Tsang, E. W. K. (1997). Organizational learning and the learning organization: A

dichotomy between descriptive and prescriptive research. Human Relations,

50(1), 73–89. doi:10.1023/A:1016905516867

Tukey, J. W. (1977). Exploratory data analysis. Menlo Park, CA: Addison-Wesley.

U.S. General Accounting Office. (2002). Financial statement restatements: Trends,

market impacts, regulatory responses, and remaining challenges. Retrieved from

http://www.gao.gov/new.items/d03138.pdf

U.S. Government Accountability Office. (2009, January). A framework for crafting and

assessing proposals to modernize the outdated U.S. financial regulatory system

(GAO Publication Report No. 09-216). Washington, DC: Author.

U.S. Securities and Exchange Commission. (2010). FY 2010 performance and

accountability report (Report No. USPTOSY2010PAR). Retrieved from http://

www.sec.gov/about/secpar/secpar2010.pdf

Vito, B. (2011). Revisiting the inadvertent investment company. Fordham Journal of

Corporate & Financial Law, 16(1), 125–176.

137

Vogel, F. (2011, July 4). Nowhere to hide: The challenge of managing corporate ethics.

Retrieved from http://www.ethicsworld.org/ethicsandemployees/fvvalmontspeech

.php

Wardrope, W. J. (2008). Investment valuation & analysis. In Research starters business

online (pp. 1–11). Toledo, OH: Great Neck.

Waring, K. (2007, February). Beyond the myth. Accountancy Magazine Online,

139(1362), 111–112. Retrieved from http://www.icaew.com/en/technical/

corporate-governance/dialogue-in-corporate-governance/beyond-the-myth/~/

media/Files/Technical/Corporate-governance/dialogue-in-corporate-governance/

beyond-the-myth-accountancy-article.ashx

Weber, J. (1990). Managers’ moral reasoning: Assessing their responses in three moral

dilemmas. Human Relations, 43(7), 687–702.

Weil, D., Fung, A., Graham, M., & Fagotto, E. (2006). The effectiveness of regulatory

disclosure policies. Journal of Public Analysis and Management, 25(1), 155–181.

doi:10.1002/pam.20160

White, E. N. (1990, Spring). The stock market boom and crash of 1929 revisited. Journal

of Economic Perspectives, 4(2), 67–83.

Whittier, N. C., Williams, S., & Dewett, T. C. (2006). Evaluating ethical decision-making

models: A review and application. Society and Business Review, 1(3), 235–247.

doi:10.1108/17465680610706319

Williams, C. C., & Ryan, L. V. (2007). Courting shareholders: The ethical implications

of altering corporate ownership structures. Business Ethics Quarterly, 17(4), 669–

688.

138

Willis, J. W. (2007). Foundations of qualitative research: Interpretive and critical

approaches. Thousand Oaks, CA: Sage.

Woolley, J., & Peters, G. (2011). The Reagan legacy on the 100 th

anniversary of his birth

(February 6, 1911). Retrieved from http://www.presidency.ucsb.edu/ws/index.

php?pid =29638

Wright, C. W. (1949). Economic history of the United States. New York, NY: McGraw-

Hill.

Yeung, A. Y., & Freeman, K. J. (2010). Gartenberg, Jones, and the meaning of fiduciary:

A legislative investigation of Section 36(B). Delaware Journal of Corporate Law,

35, 483–495.

Yin, R. K. (2009). Case study research design and methods (4th ed.). Thousand Oaks,

CA: Sage. Retrieved from http://www.sagepub.com/book.aspx?pid=9322

Yuan, J., Yuan, C., Deng, X., & Yuan, C. (2008, June). The effects of manager

compensation and market competition on financial fraud in public companies: An

empirical study in China. International Journal of Management, 25(2), 322–335.

Zhang, W., Zhou, J., & Lobo, G. J. (2006, August). The impact of corporate governance

on discretionary accrual changes around the Sarbanes-Oxley Act. Retrieved from

SSRN:http://ssrn.com/abstract=1011844

Zhang, X., Bartol, K. M., Smith, K. G., Pfarrer, M. D., & Khanin, D. M. (2008). CEOs on

the edge: Earnings manipulation and stock-based incentive misalignment.

Academy of Management Journal, 51, 241–258.

Zhong, C.-B. (2011, March). The ethical dangers of deliberative decision making.

Administrative Science Quarterly, 56, 1–25.

139

Appendix A: Companies for 6211 – Security Brokers, Dealers, & Flotation

Companies

Company

State/Country

1 AB Watley Group Inc NY 2 Advest Group Inc CT 3 Affiliate Stakeholders of Morgan Stanley Dean Witter Discover & Company NY 4 AGF Management LTD A6 5 America First Associated Corporation NY 6 America First Associates Corporation NY 7 American Corporate Accruals Inc NJ 8 American Corporate Receipts Inc NJ 9 Ameritrade Online Holdings Corporation NE 10 Bear Stearns Capital Trust I NY 11 Bear Stearns Capital Trust II NY 12 Bear Stearns Capital Trust III NY 13 Bear Stearns Capital Trust IV NY 14 Bear Stearns Capital Trust V NY 15 Bear Stearns Companies Inc NY 16 Bear Stearns Finance LLC NY 17 BlackRock Inc NY 18 Brown Alex Inc MD 19 Calamos Asset Management Inc IL 20 Cantor Fitzgerald & Company NY 21 Capital Financial Holdings Inc ND 22 Carlton Companies Inc NY 23 Casey Edward J NY 24 CBNY Investment Services Corporation NY 25 Chapman Holdings Inc MD 26 CIBC Oppenheimer Corporation NY 27 Citigroup Global Markets Holdings Inc NY 28 Citizens Bancorp IN 29 Clark Melvin Securities Corporation MD 30 Claymore Securities Defined Portfolios Series 117 KS 31 Claymore Securities Defined Portfolios Series 118 KS 32 Claymore Securities Defined Portfolios Series 122 KS 33 Claymore Securities Defined Portfolios Series 123 KS 34 Claymore Securities Defined Portfolios Series 124 IL 35 Claymore Securities Defined Portfolios Series 125 KS 36 Claymore Securities Defined Portfolios Series 127 KS 37 Claymore Securities Defined Portfolios Series 128 KS 38 Colonial Direct Financial Group Inc FL 39 Core Holdings Inc CA 40 Cowen Group Inc NY 41 Cowen Holdings Inc NY 42 Credit Suisse AG V8 43 Credit Suisse Group AG V8 44 Credit Suisse Holdings (USA) Inc NY 45 Crown Financial Group Inc NJ 46 Crown Financial Holdings Inc NJ 47 Daiwa Capital Markets America, Inc 48 DE Frey Group Inc CO 49 Davis & Associates GA

140

Company

State/Country

50 Dean Witter Global Perspective Portfolio LP NY 51 Dias Holdling Inc MI 52 Direct Markets Holding Corporation NY 53 Diversified Biotech Holdings Corporation NY 54 Duff & Phelps Corporation NY 55 Dupont Direct Financial Holdings Inc NY 56 Edwards AG Inc MO 57 Enron Corporation TX 58 Evision International Inc CO 59 FAS Wealth Management Services Inc FL 60 FBR & Company VA 61 Federal Mortgage Management II Inc FL 62 Firebrand Financial Group Inc NY 63 First Montauk Financial Corporation NJ 64 First of Michigan Capital Corporation MI 65 Friedman Billings Ramsey Group Inc VA 66 GAMCO Investors Inc NY 67 Gilman Ciocia Inc NY 68 Gleacher & Company Inc NY 69 Global Arena Holding Inc NY 70 Global Capital Partners Inc NC 71 Globalnet Financial Company Inc FL 72 Goldman Sachs Bank AG NY 73 Goldman Sachs Group Inc NY 74 Goldman Sachs Hedge Fund Partner II LLC NY 75 Goldman Sachs Hedge Fund Partners LLC NY 76 Greenhill & Company Inc NY 77 GSL Holdings Inc CA 78 Hambrecht & Quist Group CA 79 Harter Financial Inc NJ 80 Hoenig Group Inc NY 81 Hudson Holding Corporation NJ 82 Hull Group Inc IL 83 Imperial Capital Group Inc CA 84 Incam AG I8 85 Instinet Group Inc NY 86 Institutional Financial Markets Inc PA 87 Interactive Brokers Group Inc CT 88 Interstate Johnson Lane Inc NC 89 International FC Stone Inc NY 90 Investment Technology Group Inc NY 91 Investors Group Inc A2 92 IPO Connection Company MO 93 Jefferies Group Inc CA 94 Jefferies Group Inc/DE NY 95 Jesup & Lamont Inc FL 96 JJFN Holdings Inc NY 97 JMP Group Inc CA 98 Jones Financial Companies LLLP MO 99 JW Charles Financial Services Inc FL 100 JW Genesis Financial Corporation FL 101 KBW Inc NY 102 Kemper Financial Companies Inc IL

141

Company

State/Country

103 Kent Financial Services Inc TX 104 Kirlin Holding Corporation NY 105 Knight Capital Group Inc NY 106 LaBranche & Company Inc NY 107 LaBranche George ML IV NY 108 Lehman Brothers Holdings NY 109 Lehman Brothers Holdings Capital Trust I NY 110 Lehman Brothers Holdings Capital Trust II NY 111 Lehman Brothers Holdings Capital Trust III NY 112 Lehman Brothers Holdings Capital Trust IV NY 113 Lehman Brothers Holdings E-Capital LLC I NY 114 Lehman Brothers Holdings E-Capital Trust I NY 115 Lehman Brothers Holdings Inc Plan Trust NY 116 Lehman Brothers Inc NY 117 Liquidnet Holdings Inc NY 118 Mackenzie Investment Management Inc FL 119 Marketaxess Holdings Inc NY 120 MAS Capital Inc IN 121 Maxcor Financial Group Inc NY 122 McDonald & Company Investments Inc OH 123 Merrill Lynch & Company Inc NY 124 Merrill Lynch Canada Inc A1 125 Merrill Lynch Capital Trust I NY 126 Merrill Lynch, Pierce, Fenner & Smith Inc NY 127 Merriman Holdings Inc CA 128 Momentum Distribution Inc WA 129 Morgan Keegan Inc TN 130 Morgan Stanley NY 131 Morgan Stanley Finance PLC NY 132 Morgan Stanley Group Inc/DE NY 133 National Discount Brokers Group Inc NJ 134 National Securities Corporation/WA WA 135 Network I Financial Group Inc NJ 136 New York Broker Deutschland AG I8 137 Nomura America Finance LLC NY 138 Nomura Holdings Inc MO 139 North Shore Capital Advisors Corporation NY 140 OLDE Financial Corporation MI 141 OnlineTradingInc Company Corporation FL 142 Oppenheimer Holdings Inc A6 143 optionsXpress Holdings Inc IL 144 Paine Weber Group Inc NY 145 Paulson Capital Corporation OR 146 Peak6 Corporation IL 147 Penson Worldwide Inc TX 148 Pharmaceutical Holders Trust NY 149 Piper Jaffray Companies MN 150 Plainfield Direct Inc CT 151 Point Center Mortgage Fund I, LLC CA 152 Progressive Asset Management Inc CA 153 PWG Capital Trust I CT 154 PWG Capital Trust II CT 155 PWG Capital Trust III NY

142

Company

State/Country

156 PWG Capital Trust IV NY 157 PWG Capital Trust V NY 158 Quick & Reilly Group Inc/DE FL 159 Ragen Mackenzie Group Inc WA 160 Raymond James Financial Inc FL 161 RBC Capital Markets LLC MN 162 RealAmerica Company/NEW TX 163 Rodman & Renshaw Capital Group Inc IL 164 Rubicon Financial Inc CA 165 Ryan Beck & Company Inc NJ 166 Ryan Beck Holdings Inc NJ 167 S3 Investment Company Inc CA 168 Schwab Charles Corporation CA 169 Scott & Stringfellow LLC VA 170 SEI Investments Company PA 171 Shapiro Barry NY 172 Siebert Financial Corporation NY

173 SIG Indices LLLP 174 Smith Barney Holdings Inc NY 175 Smith Tim NY 176 Soundview Technology Group Inc CT 177 Stewart WP & Company LTD NY 178 Stifel Financial Corporation MO 179 Stocktrade Network Inc NY 180 Stockwalk Group Inc/MN MN 181 Summit Brokerage Services Inc/FL FL 182 Summit Financial Services Group Inc FL 183 SW Acquisition Inc NJ 184 SWS Group Inc TX 185 Targets Trust XII NY 186 Targets Trust XIII NY 187 Targets Trust XVI NY 188 Targets Trust XVII NY 189 Targets Trust XXII NY 190 Targets Trust XXIII NY 191 Targets Trust XXIV NY 192 Targets Trust XXVI NY 193 Targets Trusts V NY 194 Targets Trusts VI NY 195 TD Ameritrade Holding Corporation NE 196 TD Ameritrade Online Holdings Corporation MD 197 Telecom Holders Trust NY 198 ThinkorSwim Group Inc NY 199 Thomas Weisel Partners Group Inc CA 200 Tiers Asset Backed Securities Ser Champt 1997-7 DE 201 TNFG Corporation TX 202 Traderight Corporation FL 203 Tradestation Group Inc FL 204 Treasure Financial Corporation TX 205 Tucker Anthony Sutro MA 206 US Bancorp Investments Inc MN 207 Van Der Moolen Holding NV P7

143

Company

State/Country

208 Wadell & Reed Financial Inc KS 209 Waterhouse Investor Services Inc NY 210 WestAmerica Corporation FL 211 Westech Capital Corporation TX 212 Win Global Markets Inc DE 213 Woodstock Holdings Inc GA 214 Ziegler Companies Inc WI

Note: From “Companies for SIC 6211 – Security Brokers, Dealers & Flotation Companies,” by The Securities and Exchange Commission EDGAR system. Retrieved from http://www.sec.gov/cgi-bin/browse- edgar?company=&match=&CIK=&filenum=&State=&Country=&SIC=6211&owner=ex clude&Find=Find+Companies&action=getcompany; the 214 companies listed are the study’s population and the 74 bolded companies represent the study’s sample.

144

Appendix B: Sample of Securities and Exchange Commission Enforcement Actions

Company

Enforcement Action Result

Goldman Sachs

In April 2010, the SEC regulators charged Goldman Sachs and one of its vice presidents with defrauding investors by misstating and omitting key facts regarding a financial product tied to subprime mortgages. Goldman Sachs associates failed to disclose to investors that Paulson & Co, a major hedge fund player, had taken a significant role in assembling a synthetic collateralized debt obligation tied to the performance of subprime residential mortgage-backed securities, and had taken a short position against it.

Settlement $550 million in penalties and disgorgement and agreeing to reform its business practices

Citigroup In July 2010, Citigroup and two senior executives agreed to settle charges that it had misled investors about the company’s exposure to subprime mortgage- related assets, making misleading statements in earnings calls and public filings about the extent to its holdings of assets backed by subprime mortgages. Between July and mid-October 2007, Citigroup represented that subprime exposure in its investment banking unit was $13 billion or less when, in fact, it was more than $50 billion.

Settlement $75 million and the executives also paid penalties

New Century

In December 2009, the SEC regulators alleged that three former officers of New Century Financial Corporation had falsely assured New Century investors that all was well, while failing to disclose increase in loan defaults, loan repurchases and loan repurchase requests.

Settlement Executives paid more than $1.5 million and agreed to five-year officer and director bars

Morgan Keegan

In April 2010, the SEC brought administrative proceedings against Morgan Keegan & Company, Morgan Asset Management and two employees for allegedly overstating the value of securities backed by subprime mortgages. The SEC alleged that Morgan Keegan failed to employ reasonable procedures to internally price the portfolio securities in five funds and sold shares to investors based on the inflated prices.

Settlement $100 million and the two employees agreed to pay penalties, including one who agreed to be barred from the securities industry

Bank of America

In February 2010, the SEC charged Bank of America with misleading investors about billions of dollars in bonuses being paid to Merrill Lynch executives at the time of its acquisition of the firm, and failing to disclose extraordinary losses that Merrill sustained.

Settlement $150 million

Evergreen In June 2009, the SEC charged Evergreen Securities with overstating the value of a mutual fund invested primarily in mortgage-backed securities and only selectively telling shareholders about the fund’s valuation problems.

Settlement $40 million

TD Ameritrade

In February 2011, the SEC charged TD Ameritrade with the failing to supervise representatives who mischaracterized the Reserve Fund as safe as cash and failed to disclose risks when offering the investment to customers.

Settlement $10 million

145

Company

Enforcement Action Result

JP Morgan In July 2011, the SEC charged JP Morgan Securities with fraudulent bidding practices involving investment of municipal bond proceeds. JMP associates improperly won bids by entering into secret arrangements with bidding agents to get an illegal last look at competitors’ bids.

Settlement $228 million

Raymond James

In June 2011, the SEC charged Raymond James Financial Inc, with misleading disclosures. Raymond James representatives and financial advisors told customers that Auction Rate Securities were ‘cash equivalents’ and ‘highly liquid’ short-term investments that sported a higher yield than money market accounts.

Settlement $300 million

Note: From “FY 2010 Performance and Accountability Report,” by the United States Securities and Exchange Commission, Securities and Exchange Commission, 2011. Reprinted pending permission.

146

Appendix C: Twenty-First Century Corporate Scandals

Company Year

Description

Enron 2001 Declared bankruptcy on December 2, 2001 after restating earnings in the 3

rd -quarter 10-Q, indicating major problems with special-purpose

entities. Investigations by the SEC, Justice Department, and others; executives indicted and class-action lawsuits filed.

Global Crossing 2002 Overstated revenues and earnings over network capacity swaps and then declared bankruptcy; investigated by SEC and FBI.

WorldCom 2002 Recorded improper expenses of $3.8 billion and then declared bankruptcy; under investigation for accounting fraud and other violations; the amount of improper expenses uncovered approached $10 billion.

Tyco 2002 Conglomerate with questionable practices on accounting for acquisitions and other issues. Restated 1999-2001 financials based on merger-related restructurings plus other problems with reserves. CEO and CFO indicted.

Qwest 2002 Subject to criminal investigation by U.S. Justice Department and accounting practice probe by the SEC; associated with ‘hollow swaps’.

Adelphia 2002 Cable TV operation charged with overstating earnings; former CEO charged with looting the company, which went bankrupt.

Imclone 2002 Insider trading charges against former CEO for selling stock after FDA rejected a new drug; alleged to have tipped off Martha Steward and other friends and relatives.

Merrill Lynch, Salomon, Smith Barney, Credit Suisse, Goldman Sachs, JP Morgan, and others

2002 Major investment banks settled with the New York Attorney General, SEC, and other regulators on deceptive stock analysis and other brokerage-related practices. The total fine was a combined $2 billion plus other sanctions and agreement to correct deceptive practices.

HealthSouth 2003 Accused of accounting fraud involving $1.4 billion in earnings and $800 million in overstated assets. Former CFO and others pleaded guilty to fraud charges.

The Mutual Funds Scandal 2003 The mutual funds have limited SEC regulation requirements and often poor corporate governance. New York Attorney General Eliot Spitzer sued them on several counts related to market timing.

Fannie Mae 2004 Fraudulent accounting practices totaling $16 billion and extensive payouts to ousted executives; earnings restated and executives fired.

AIG 2005 Internal control weaknesses and poor corporate governance; CEO fired.

Stock Option Backdating 2006 Backdating options grant date to lower stock price and higher earnings; dozens of companies investigated by SEC.

Subprime Loans 2007 Massive number of mortgage loans to subprime borrowers, often without documentation. Mortgages then repackaged through mortgage-backed securities and sold as bonds. Huge losses taken at major financial institutions and many executives fired.

Note: From “What Went Wrong? Accounting Fraud and Lessons from the Recent Scandals,” by G. Giroux, 2008. Social Research, 75(4), p. 1206. Copyright of Social Research. Reprinted pending permission.

147

Appendix D: Data Analysis Procedure Step 1 Results

Table D1

Correlations between Gross Profit Margin and After-tax Profit Margin

Note. GMP = gross profit margin; ATPM = after-tax profit margin. * p < .05, two-tailed. ** p < .01, two-tailed.

GPM2010 ATPM2010 GPM2009 ATPM2009 GPM2008 ATPM2008 GPM2007 ATPM2007 GPM2006 ATPM2006 GPM2005 ATPM2005

Pearson Correlation 1.000 0.994** 0.865** 0.751** 0.251 0.281* 0.054 0.055 0.539** 0.495** 0.602** 0.475**

GPM2010 Sig. (2-tailed) 0.000 0.000 0.000 0.057 0.033 0.690 0.683 0.000 0.000 0.000 0.000

N 58 58 58 58 58 58 57 57 57 57 56 57

Pearson Correlation 0.994** 1.000 0.865** 0.778** 0.237 0.332* 0.043 0.046 0.512** 0.457** 0.576** 0.411**

ATPM2010 Sig. (2-tailed) 0.000 0.000 0.000 0.073 0.011 0.752 0.735 0.000 0.000 0.000 0.001

N 58 58 58 58 58 58 57 57 57 57 56 57

Pearson Correlation 0.865** 0.865** 1.000 0.951** 0.135 0.095 0.011 0.006 0.126 0.077 0.190 0.124

GPM2009 Sig. (2-tailed) 0.000 0.000 0.000 0.300 0.466 0.935 0.963 0.336 0.558 0.150 0.344

N 58 58 61 61 61 61 60 60 60 60 59 60

Pearson Correlation 0.751** 0.778** 0.951** 1.000 0.051 0.177 -0.019 -0.022 -0.068 -0.150 0.011 -0.146

ATPM2009 Sig. (2-tailed) 0.000 0.000 0.000 0.694 0.173 0.883 0.866 0.608 0.254 0.932 0.267

N 58 58 61 61 61 61 60 60 60 60 59 60

Pearson Correlation 0.251 0.237 0.135 0.051 1.000 0.692** 0.070 0.070 0.275* 0.264* 0.155 0.155

GPM2008 Sig. (2-tailed) 0.057 0.073 0.300 0.694 0.000 0.584 0.584 0.028 0.035 0.225 0.221

N 58 58 61 61 65 65 64 64 64 64 63 64

Pearson Correlation 0.281* 0.332* 0.095 0.177 0.692** 1.000 0.032 0.032 0.247* 0.159 0.112 0.033

ATPM2008 Sig. (2-tailed) 0.033 0.011 0.466 0.173 0.000 0.804 0.804 0.049 0.210 0.383 0.793

N 58 58 61 61 65 65 64 64 64 64 63 64

Pearson Correlation 0.054 0.043 0.011 -0.019 0.070 0.032 1.000 1.000** -0.023 -0.013 .973** .967**

GPM2007 Sig. (2-tailed) 0.690 0.752 0.935 0.883 0.584 0.804 0.000 0.850 0.918 0.000 0.000

N 57 57 60 60 64 64 70 70 69 69 68 68

Pearson Correlation 0.055 0.046 0.006 -0.022 0.070 0.032 1.000** 1.000 -0.023 -0.013 .973** .967**

ATPM2007 Sig. (2-tailed) 0.683 0.735 0.963 0.866 0.584 0.804 0.000 0.850 0.918 0.000 0.000

N 57 57 60 60 64 64 70 70 69 69 68 69

Pearson Correlation .539** .512** 0.126 -0.068 .275* .247* -0.023 -0.023 1.000 .862** 0.175 0.164

GPM2006 Sig. (2-tailed) 0.000 0.000 0.336 0.608 0.028 0.049 0.850 0.850 0.000 0.147 0.172

N 57 57 60 60 64 64 69 69 71 71 70 71

Pearson Correlation .495** .457** 0.077 -0.150 .264* 0.159 -0.013 -0.013 .862** 1.000 0.144 0.191

ATPM2006 Sig. (2-tailed) 0.000 0.000 0.558 0.254 0.035 0.210 0.918 0.918 0.000 0.233 0.111

N 57 57 60 60 64 64 69 69 71 71 70 71

Pearson Correlation .602** .576** 0.190 0.011 0.155 0.112 .973** .973** 0.175 0.144 1.000 .980**

GPM2005 Sig. (2-tailed) 0.000 0.000 0.150 0.932 0.225 0.383 0.000 0.000 0.147 0.233 0.000

N 56 56 59 59 63 63 68 68 70 70 72 72

Pearson Correlation .475** .411** 0.124 -0.146 0.155 0.033 .967** .967** 0.164 0.191 .980** 1.000

ATPM2005 Sig. (2-tailed) 0.000 0.001 0.344 0.267 0.221 0.793 0.000 0.000 0.172 0.111 0.000

N 57 57 60 60 64 64 69 69 71 71 72 73

148

Appendix E: Data Analysis Procedure Step 2 Results

Table E1

Test of Model Effects

Type III Type III

Source Wald Chi- Square

df Sig. Source Wald Chi-Square

df Sig.

(intercept) 1.460 1 .227 (intercept) 1.978 1 .160 GPM2010 .503 1 .478 GPM2010 .730 1 .393 GPM2009 1.372 1 .241 GPM2009 1.556 1 .212 GPM2008 .011 1 .916 GPM2007 .032 1 .857 GPM2007 .046 1 .830 GPM2006 1.853 1 .173 GPM2006 .962 1 .327 GPM2005 .018 1 .893

Note. GPM = gross profit margin. Dependent variable = Malfeasance.

Table E2 Omnibus Test

Likelihood Ratio Chi-Square

df Sig. Likelihood Ratio Chi-Square

df Sig.

5.549 6 .476 5.947 4 .203

Note. GPM = gross profit margin. Dependent variable = Malfeasance.

Figure E1

Variance Boxplot of the Sample Companies’ Gross Profit Margins

149

Table E3 Mauchley’s Test of Sphericity

Within Subjects

Effect

Mauchley’s W

Approximate Chi-Square

df Sig.

Epsilon

Greenhouse- Geisser

Huynh- Feldt

Lower- bound

yeargpm .001 391.280 14 .000 .318 .326 .200

Table E4

Tests of Within-subjects Effects

Source

Type III Sum of

Squares

df Mean

Square F Sig.

yeargpm

Sphericity Assumed 153527.634 5 30705.527 1.650 .147 Greenhouse-Geisser 153527.634 1.590 96567.004 1.650 .202 Huynh-Feldt 153527.634 1.630 94173.799 1.650 .202 Lower-bound 153527.634 1.000 153527.634 1.650 .204

Error (yeargpm)

Sphericity Assumed 5025659.374 270 18613.553 Greenhouse-Geisser 5025659.374 85.852 58538.487 Huynh-Feldt 5025659.374 88.034 57087.736

150

Appendix F: Data Analysis Procedure Step 3 Results

Table F1

Omnibus Test: Regulatory Compliance or Malfeasance

Likelihood Ratio Chi-squared

df Sig.

1.681 1 .195

Table F2

Parameter Estimates: Regulatory Compliance or Malfeasance

Parameter

Std. Error

95% Wald Confidence

Interval

Hypothesis Test

Lower

Upper Wald Chi- Square

df Sig.

(Intercept) .304 .2368 -.160 .768 1.648 1 .199 Meangpm 2.254E-

005 .0001 .000 .000 .046 1 .830

(Scale) 1 a

151

Appendix G: Data Analysis Procedure Step 4 Results

Table G1

Omnibus Test: Violation Type 1

Likelihood Ratio Chi-squared

df Sig.

3.326 1 .068

Table G2

Parameter Estimates: Violation Type 1

Parameter

Std. Error

95% Wald Confidence Interval

Hypothesis Test

Lower

Upper Wald Chi-Square df Sig.

(Intercept) 1.374 .2951 .796 1.953 21.680 1 .000 Meangpm .001 .0022 -.003 .005 .229 1 .633 (Scale) 1

Table G3

Omnibus Test: Violation Type 2

Likelihood Ratio Chi-squared

df Sig.

6.692 1 .010

Table G4

Omnibus Test: Violation Type 3

Likelihood Ratio Chi-squared

df Sig.

.255 1 .613

152

Table G5

Parameter Estimates: Violation Type 3

Parameter

Std. Error

95% Wald Confidence Interval

Hypothesis Test

Lower

Upper Wald Chi-Square df Sig.

(Intercept) 3.139 .5929 1.977 4.301 28.037 1 .000 Meangpm -.003 .0108 -.025 .018 .099 1 .753 (Scale) 1

Table G6

Omnibus Test: Violation Type 4

Likelihood Ratio Chi-squared

df Sig.

4.352 1 .037

Table G7

Parameter Estimates: Violation Type 4

Parameter

Std. Error

95% Wald Confidence Interval

Hypothesis Test

Lower

Upper Wald Chi-Square df Sig.

(Intercept) 1.360 .3165 .740 1.980 18.461 1 .000 Meangpm -.015 .0099 -.034 .005 2.212 1 .137 (Scale) 1

153

Appendix H: Data Analysis Procedure Step 5 Results

Table H1

Frequency Table of Total Volume

Frequency

Percent Valid Percent Cumulative Percent

0 42 56.8% 56.8% 56.8% 1 15 20.3% 20.3% 77.0% 2 4 5.4% 5.4% 82.4% 3 1 1.4% 1.4% 83.8% 4 2 2.7% 2.7% 86.5% 5 1 1.4% 1.4% 87.8% Valid 6 2 2.7% 2.7% 90.5% 7 2 2.7% 2.7% 93.2% 8 1 1.4% 1.4% 94.6% 9 2 2.7% 2.7% 97.3% 10 1 1.4% 1.4% 98.6% 14 1 1.4% 1.4% 100.0% Total 74 100.0% 100.0%

Figure H1

Total Volume Distribution

154

Table H2

Omnibus Test: Volume

Likelihood Ratio Chi-squared

df Sig.

.231 1 .631

Table H3

Parameter Estimates: Volume

Parameter

Std. Error

95% Wald Confidence

Interval

Hypothesis Test

Lower

Upper Wald Chi- Square

df Sig.

(Intercept) .446 .0937 .262 .629 22.652 1 .000 Meangpm 1.366E-

006 3.0692E-

006 -4.649E-006 7.382E-006 .198 1 .656

(Scale) 1 a

155

Appendix I: Data Analysis Procedure Step 6 Results

Table I1

Mean Gross Profit Margin Percentiles

10% 25% 50% 75% 90% 95%

-444.1000 -43.1750 -15.0075 3.9208 23.1250 43.2667 53.4583

Note. Weighted average=mean gross profit margin for each company across six years. Tukey’s Hinges=mean gross profit margin for each company across six years. Table I2 Descriptive Crosstabs: Gross Profit or Loss to Malfeasance

Malfeasance

Total No Violation Violation(s)

Loss Count 18 12 30 % within gpm profit and loss 60.0% 40.0% 100.0%

Profit Count 24 20 44 % within gpm profit or loss 54.5% 45.5% 100.0%

Total Count 42 32 74 % within gpm profit or loss 56.8% 43.2% 100.0%

Table I3

Chi-squared Tests: Malfeasance

Value

Asymp. Sig. (two-sided)

Exact Sig. (two-sided)

Exact Sig. (one-sided)

Pearson Chi-squared .216 a 1 .642

Continuity Correction b .051 1 .821

Likelihood Ratio .217 1 .642 Fisher’s Exact Test .811 .411 Linear-by-linear Association .213 1 .644 N of Valid Cases 74

156

Table I4 Descriptive Crosstabs: Gross Profit or Loss to Type 1 Violations

Binary Volume Type 1

Total No Violation Violation(s)

Loss Count 23 7 30 % within gpm profit and loss 76.7% 23.3% 100.0%

Profit Count 35 9 44 % within gpm profit or loss 79.5% 20.5% 100.0%

Total Count 58 16 74 % within gpm profit or loss 78.4% 21.6% 100.0%

Table I5

Chi-squared Tests: Type 1 Violations

Value

Asymp. Sig. (two-sided)

Exact Sig. (two-sided)

Exact Sig. (one-sided)

Pearson Chi-squared .087 a 1 .768

Continuity Correction b .000 1 .994

Likelihood Ratio .087 1 .768 Fisher’s Exact Test .781 .493 Linear-by-linear Association 086 1 .769 N of Valid Cases 74

157

Table I6 Descriptive Crosstabs: Gross Profit or Loss to Type 2 Violations

Binary Volume Type 2

Total No Violation Violation(s)

Loss Count 26 4 30 % within gpm profit and loss 86.7% 13.3% 100.0%

Profit Count 29 15 44 % within gpm profit or loss 65.9% 34.1% 100.0%

Total Count 55 19 74 % within gpm profit or loss 74.3% 25.7% 100.0%

Table I7

Chi-squared Tests: Type 2 Violations

Value

Asymp. Sig. (two-sided)

Exact Sig. (two-sided)

Exact Sig. (one-sided)

Pearson Chi-squared 4.028 a 1 .045

Continuity Correction b 3.013 1 .083

Likelihood Ratio 4.282 1 .039 Fisher’s Exact Test .059 .039 Linear-by-linear Association 3.973 1 .046 N of Valid Cases 74

158

Table I8 Descriptive Crosstabs: Gross Profit or Loss to Type 3 Violations

Binary Volume Type 3

Total No Violation Violation(s)

Loss Count 30 0 30 % within gpm profit and loss 100.0% 0.0% 100.0%

Profit Count 41 3 44 % within gpm profit or loss 93.2% 6.8% 100.0%

Total Count 71 3 74 % within gpm profit or loss 95.9% 4.1% 100.0%

Table I9

Chi-squared Tests: Type 3 Violations

Value

Asymp. Sig. (two-sided)

Exact Sig. (two-sided)

Exact Sig. (one-sided)

Pearson Chi-squared 2.132 a 1 .144

Continuity Correction b .739 1 .390

Likelihood Ratio 3.205 1 .073 Fisher’s Exact Test .267 .204 Linear-by-linear Association 2.103 1 .147 N of Valid Cases 74

159

Table I10 Descriptive Crosstabs: Gross Profit or Loss to Type 4 Violations

Binary Volume Type 4

Total No Violation Violation(s)

Loss Count 26 4 30 % within gpm profit and loss 86.7% 13.3% 100.0%

Profit Count 32 12 44 % within gpm profit or loss 72.7% 27.3% 100.0%

Total Count 58 16 74 % within gpm profit or loss 78.4% 21.6% 100.0%

Table I11

Chi-squared Tests: Type 4 Violations

Value

Asymp. Sig. (two-sided)

Exact Sig. (two-sided)

Exact Sig. (one-sided)

Pearson Chi-squared 2.045 a 1 .153

Continuity Correction b 1.305 1 .253

Likelihood Ratio 2.143 1 .143 Fisher’s Exact Test .250 .126 Linear-by-linear Association 2.018 1 .156 N of Valid Cases 74

Table I12

Frequency Table: Gross Profit Margin to Top-10, Profit, and Loss Companies

Frequency

Percent Valid Percent

Cumulative Percent

Valid

Loss 30 40.5% 40.5% 40.5% Profit 37 50.0% 40.0% 90.5% Top-10 7 9.5% 9.5% 100.0% Total 74 100.0% 100.0%

160

Table I13

Descriptive Crosstabs: Top-10, Profit, and Loss to Malfeasance

Malfeasance

Total

No Violation Violation(s)

Loss Count 18 12 30 Expected Count 17.0 13.0 30.0 % within gpm top-10, profit, and loss 60.0% 40.0% 100.0%

Profit Count 20 17 37 Expected Count 21.0 16.0 37.0 % within gpm top-10, profit, and loss 54.1% 45.9% 100.0%

Top-10 Count 4 3 7 Expected Count 4.0 3.0 7.0 % within gpm top-10, profit, and loss 57.1% 42.9% 100.0%

Total Count 42 32 74 Expected Count 42.0 32.0 74.0 % within gpm top-10, profit, and loss 56.8% 43.2% 100.0%

Note. Two cells (33.3%) have expected counts of less than 5. The minimum expected count is 3.03.

161

Table I14

Descriptive Crosstabs: Top-10, Profit, and Loss to Type 1 Violations

Binary Volume Type 1

Total

No Violation Violation(s)

Loss Count 23 7 30 Expected Count 23.5 6.5 30.0 % within gpm top-10, profit, and loss 76.7% 23.3% 100.0%

Profit Count 29 8 37 Expected Count 29.0 8.0 37.0 % within gpm top-10, profit, and loss 78.4% 21.6% 100.0%

Top-10 Count 6 1 7 Expected Count 5.5 1.5 7.0 % within gpm top-10, profit, and loss 85.7% 14.3% 100.0%

Total Count 58 16 74 Expected Count 58.0 16.0 74.0 % within gpm top-10, profit, and loss 78.4% 21.6% 100.0%

Table I15

Chi-squared Tests: Type 1 Violations

Value

Asymp. Sig. (two-sided)

Pearson Chi-squared .274 a 2 .872

Likelihood Ratio .296 2 .863 Linear-by-linear Association .206 1 .650 N of Valid Cases 74

162

Table I16

Descriptive Crosstabs: Top-10, Profit, and Loss to Type 2 Violations

Binary Volume Type 2

Total

No Violation Violation(s)

Loss Count 26 4 30 Expected Count 22.3 7.7 30.0 % within gpm top-10, profit, and loss 86.7% 13.3% 100.0%

Profit Count 25 12 37 Expected Count 27.5 9.5 37.0 % within gpm top-10, profit, and loss 67.6% 32.4% 100.0%

Top-10 Count 4 3 7 Expected Count 5.2 1.8 7.0 % within gpm top-10, profit, and loss 47.1% 42.9% 100.0%

Total Count 55 19 74 Expected Count 55.0 19.0 74.0 % within gpm top-10, profit, and loss 74.3% 25.7% 100.0%

Table I17

Chi-squared Tests: Type 2 Violations

Value

Asymp. Sig. (two-sided)

Pearson Chi-squared 4.363 a 2 .113

Likelihood Ratio 4.559 2 .102 Linear-by-linear Association 4.167 1 .041 N of Valid Cases 74

163

Table I18

Descriptive Crosstabs: Top-10, Profit, and Loss to Type 3 Violations

Binary Volume Type 3

Total

No Violation Violation(s)

Loss Count 30 0 30 Expected Count 28.8 1.2 30.0 % within gpm top-10, profit, and loss 100.0% 0.0% 100.0%

Profit Count 34 3 37 Expected Count 35.5 1.5 37.0 % within gpm top-10, profit, and loss 91.9% 8.1% 100.0%

Top-10 Count 7 0 7 Expected Count 6.7 .3 7.0 % within gpm top-10, profit, and loss 100.0% 0.0% 100.0%

Total Count 71 3 74 Expected Count 71.0 3.0 74.0 % within gpm top-10, profit, and loss 95.9% 4.1% 100.0%

Note. Three cells (50.0%) have expected counts less than five. The minimum expected count is .28.

164

Table I19

Descriptive Crosstabs: Top-10, Profit, and Loss to Type 4 Violations

Binary Volume Type 4

Total

No Violation Violation(s)

Loss Count 26 4 30 Expected Count 23.5 6.5 30.0 % within gpm top-10, profit, and loss 86.7% 13.3% 100.0%

Profit Count 26 11 37 Expected Count 29.0 8.0 37.0 % within gpm top-10, profit, and loss 70.3% 29.7% 100.0%

Top-10 Count 6 1 7 Expected Count 5.5 1.5 7.0 % within gpm top-10, profit, and loss 85.7% 14.3% 100.0%

Total Count 58 16 74 Expected Count 58.0 16.0 74.0 % within gpm top-10, profit, and loss 78.4% 21.6% 100.0%

Table I20

Fisher’s Exact Test: Malfeasance and Type 3 Violations

Malfeasance

Type 3 Violations

p-value Alternative Hypothesis p-value Alternative Hypothesis 0.8815 Two-sided 0.4435 Two-sided