Article Critique 1

T h e J o u r n a l o f D e v e l o p i n g A r e a s Volume 52 No. 3 Summer 2018

THE SPEED OF ADJUSTMENT OF

CORPORATE CASH HOLDINGS

K T Vigneswara Rao

Keyur Thaker*

Indian Institute of Management, India

ABSTRACT

Cash is an important and highly liquid asset held by a business firm to meet its liquidity needs

and other purposes. Cash holding level bear real consequences on the financial and operating

performance and policy of a firm in both short run and long run. In India, there has been a

dramatic increase in the corporate cash holdings over the last decade. For the last three years,

the percentage of assets that the listed firms are retaining as a cash has undergone an unusal

increase. The major purpose of this study is to explore the speed of adjustment of cash

balances of sampled firms towards target cash holding. The study sample consists of 849

manufacturing firms listed on National Stock Exchange of India for the period 2007-2012. The

present study used dynamic panel data regression analysis to address the dynamic nature of

cash holdings, where the generalized method of moments (GMM) technique was employed for

estimating the determinants and the speed of adjustment (SOA) of cash holdings with one-step

and two-step estimators of system GMM (SYS–GMM) The findings indicate net working

capital, leverage, capital expenditure, default spread and T-bills rates have negative association

with the cash to total asset ratios while the dividends, net debt issuance and net equity issuance

have positive association with cash holding levels of the firm. The estimated adjustment

coefficient (λ) is below 0.5 signifying that a typical firm in the sample closes more than half

the gap between actual and target cash holdings within one and half year and the entire gap

within two and half years. The study results supports trade-off theory of corporate cash

holdings. This study provides an insight to the dynamic nature and speed of adjustment

towards the target level of cash holdings of sample firms, which might help a finance manager

in the better management of cash holdings. Effective liquidity management leads to the

realization of long-term financial goals and objectives of a firm.

JEL Classifications: G32, G35, G39

Keywords: Cash holdings, Speed of Adjustment, Dynamic Panel data, Corporate Cash Levels

*Corresponding Author’s Email: [email protected]

INTRODUCTION

The extant literature in both theoretical and empirical finance discussed about

various motives, determinants and speed of adjustment of corporate cash holdings

towards target level of cash holdings. Though, the optimum level of cash holdings

that a firm needs to maintain is not clearly expounded and is much debated issue in

corporate finance literature for more than a decade. In the recent times, there has

been an increased interest from the financial press, practitioners, and academia

because of substantial increase in corporate cash holdings in the context of both the

developing countries and emerging markets (Opler et al., 1999; Ferreira & Vilela,

2004; Bates et al., 2009; Al–Najjar, 2013). In the case of India, cash and bank

balances of listed firms have increased nine-fold during the time period 2004-14

(Lokeshwari, 2014). Corporate cash holding levels bear real consequences on the

financial and operating performance of firm as observed by Mikkelson and Partch

(2003), thereby making it clear that cash constitutes a key dimension of firms’

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financial policies. Hence, understanding speed of adjustment of corporate cash

holdings is helpful in formulating and implementing the various financial policies of

a firm.

LITERATURE REVIEW

The earliest studies focused on cash holdings of a firm include Keynes (1936),

Jensen and Meckling (1976), Myers and Majluf (1984). Later on Kim et al. (1998)

developed a theory of corporate liquidity by considering the marginal costs and

benefits associated with holding liquid assets of a firm. Build on this study, there are

number of extended studies including (Opler et al., 1999; Ferreira and Vilela, 2004;

D’Mello et al., 2008; and Bates et al., 2009). If the markets are perfect, then there is

no significance for cash holdings as firms can source the funds with no transaction

costs as and when required (Modigliani and Miller, 1958). However in reality,

markets are imperfect and transaction costs and information asymmetry makes cash

holdings of a firm more relevant and challenging. There are two major strands in the

literature of corporate cash holding, one dealing with the determinants of cash ratios

and another with the estimation of marginal value of cash (Kim et al., 1998; Opler et

al., 1999; Ferreira and Vilela, 2004; Ozkan and Ozkan, 2004; Bates et al., 2009;

Faulkender and Wang, 2006; Pinkowitz and Williamson, 2007). The following table

shows a summary of model predictions provided by three theories of cash holdings

discussed in the literature.

TABLE L: SUMMARY OF MODEL PREDICTIONS

Variable Trade-off theory Pecking order

theory

Free cash flow

theory

Dividends Negative

Investment opportunity

set

Positive Positive Negative

Liquid asset substitutes Negative

Leverage Un known Negative Negative

Size of firm Negative Positive Positive

Cash flow uncertainty Positive

Cash flow Negative Positive

Debt maturity Un known

(Adopted: Ferreira and Vilela, 2004)

Kim et al. (1998) have developed a model to define a firm’s ‘optimal

liquidity’ and appraise the trade-off between costs and benefits of possessing liquid

assets. They concluded that cash holding of any firm is an increase function that

results in the reduction of the opportunity costs with the increase in liquid assets.

While, increase function comprises of the external finance cost, cash flow riskiness

and investment opportunities in future. Extending their study, Opler et al. (1999)

have used both cross-sectional and time series regression methods to examine the

determinants and implications of cash holdings for a sample of US listed firms over a

period of 1971-94. Their study concluded that cash holdings of a firm are directly

and positively related to its small size, investment opportunities, risk and low access

to external funding.

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Almeida et al. (2004) developed a model to identify a relationship between

cash saving behavior of a firm and its financial constraints by using a sample of US

manufacturing firms over a period of 1971-2000. He found constrained firms had

higher and significant sensitivity of cash flow to cash holdings, while not so for

unconstrained firms. Acharya et al. (2007) examined the debt and cash policies for a

sample of US firms during 1971-2001 by using 3SLS system and devised a model for

firm’s decision to apportion additional cash flows to cash holdings versus debt

payments. They concluded that cash acts as a negative debt for unconstrained firms

and also for constrained firms possessing low hedging needs. On the other hand, this

is not true in case of constrained firms, which have high hedging needs. Bates et al.

(2009) have used both the fixed effects and Fama-Macbeth methodologies to

examine the augment in cash balances in U.S. listed firms from 1980-2006. They

have reported more than a double increase in corporate cash balances from 1980 to

2006, supporting the precautionary motive of cash holding, as the increase in cash

was associated with cash flow volatility and spending on R&D.

Size of the firm

Miller and Orr (1966) argued that large companies have a higher scale in cash

management and holdings as compared to smaller firms. Larger firms eventually

hold lower cash levels in comparison with smaller firms. Smaller firms are more

oppressive than larger ones due to higher transaction and fixed costs attributed to

raising debt and external funding. Kim et al. (1998), Ozkan and Ozkan (2004) have

supported the view that larger firms are less likely to encounter financial constraints

compared to small counterparts. This is because larger firms have ease of access to

external funding with reduced transaction costs. The diversification advantages of

larger firm helps to maintain lower cash levels when compared to smaller one

(Titman and Wessels, 1988; Rajan and Zingales, 1995). The negative relationship

between size and cash holdings held by the firm has been documented in the earlier

studies (Kim et al., 1998; Opler et al., 1999; Bruinshoofd and kool, 2004; Ferreira

and Vilela, 2004; D’Mello et al., 2008; Bates et al. 2009; Kim, Kim and Woods,

2011; Al-Najjar, 2013).

Leverage

Ferreira and Vilela (2004) asserted a positive relationship between cash holdings and

high leverage of a firm due to increased risk of default and financial distress. Hardin

et al. (2009) have contradicted that leverage leads to negative impact on cash

holdings as it is used for reducing agency related problems. However, the earlier

empirical studies documented in literature established a negative relationship

between leverage and cash, as leverage can be viewed as a substitute for cash (Kim et

al., 1998; Opler et al., 1999; Ozkan and Ozkan, 2004; Ferreira and Vilela, 2004;

D’Mello et al., 2008; and Bates et al., 2009).

Investment opportunities

Bates et al., (2009) stated that with increase in future investment opportunities, firms

are induced to hold more cash in order to avoid external macro-economic shock and

risks. Hardin et al. (2009) supported this view and argued that firms contain and hold

cash and liquid assets to take positive NPV projects and reduce the enormous cost

due to external funding and their related market frictions. Increased investment

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opportunities induces a firm to pile up the cash due to its precautionary motive as

discussed in the previous studies (Harris and Raviv, 1990; Kim et al. 1998; Ozkan

and Ozkan 2004; Ferreira and Vilela 2004; Bates et al., 2009).

Liquid asset substitutes or Networking capital

In general, there is an inverse relationship between cash holdings and non-cash liquid

assets such as accounts receivables, marketable securities and trade debtors etc., held

by a firm as these can be substituted for cash, if needed. Ferreira and Vilela (2004)

have agreed to this viewpoint and also argue that non-cash liquid assets are typically

substitutes for cash as can be easily liquidated cash.

Capital expenditure

Opler et al. (1999) argued a direct and positive association between cash holdings

and capital expenditures as firms have a tendency to pile up cash to meet the capex

demands. On the other hand, Bates et al., (2009) argued that new assets are generated

due to capital expenditures and this enhances the “borrowing capacity” and earnings

capabilities of any firm, which has negative impact on the requirements of cash by a

firm due to increased cash flows. Their study observed a negative correlation

between cash holdings and capital expenditures of a firm.

Cash flow

Kim et al. (1998) emphasized a negative relationship between cash flow and cash

holdings of a firm. This is because cash flows generated from operations can be used

as a substitute for cash. On the contrary, Opler et al. (1999) have argued that a

positive and direct relationship exists between cash flow and cash holdings. This is

because an increase in cash flow can further strengthen cash build-up of a firm and

enable them to carry out future investments with reduced financial risk. Ferreira and

Vilela (2004) have supported this view and stated a significant and positive

relationship between flow of cash and cash holdings.

Dividends

A dividend paying firm can cut-back their dividends to raise required funds quickly

and easily in comparison with non-dividend paying firm (Ferreira and Vilela, 2004;

Opler et al., 1999). On the contrary, the firms which pay dividends regularly have to

maintain higher cash levels in order to ensure smooth dividend payments. Opler et al.

(1999) identified a positive relationship between the same. Bates et al. (2009) have

asserted a negative relationship between cash holdings and dividend payment.

Cash flow uncertainty or Risk

Opler et al. (1999) suggested a direct and positive relationship between cash holdings

and volatility of cash flows. Their study supports precautionary cash holdings and

indicates that firms pile up cash balances due to increased riskiness in cash flows.

Ozkan and Ozkan (2004) argued that, “Firms which have higher riskiness in cash

flows are estimated to hold more cash to alleviate the expected costs due to

illiquidity”.

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Research and development expenses

Schroth and Szalay (2010) proposed that firms practicing increased innovation may

withhold cash to invest later in a phased manner in order to outperform their

competitors. A firm needs to hold cash in order invest in technology and sustain its

success in innovation.

DATA, SAMPLE & METHODOLOGY

The sample of study includes firms listed on National stock exchange of India, for a

period of 2007-2012, excluding financial and banking firms, investment and holdings

companies, utilities and firms with missing data. The final sample includs 849

manufacaturing firms covering 5094 firm years of balanced panel data.The data was

sourced from the CMIE Prowess database.

Dynamic panel data estimation

The empirical results of Opler et al. (1999) largely supports the trade-off model of

cash holdings and provides evidence consistent with the idea of firms adjusting to

target level of cash holdings. The static panel data analysis of cash holdings not only

assumes that firms adjust their deviations from optimal or targeted cash holding

levels in a static framework, but also does not allow for a time varying target level of

cash. Therefore an appropriate econometric estimation technique should allow for

lags in adjustment and systematic changes in the determinants of optimal cash levels.

Hence, we estimate a dynamic panel data model using a generalized method of

moments (GMM) procedure that not only allows us to account for the dynamic

nature of cash level, but also allows us to control for the endogeneity problem in firm

level specific variables. With regards to the econometric methodology, we allow cash

holdings to not adjust immediately to changes in the cash holdings’ explanatory

variables. Because of the adjustment process to take place, warranted by the

transaction and adjustment costs existing in the case of imperfect markets. Following

Ozkan and Ozkan (2004), we first assume that each ith firm has an optimal cash level

at year t, function of the explanatory variables xk, and an error term µ, i.e.,:

*

,i t k kit it

k

Cash x    

Where,

*

,i t Cash 

Target cash ratio of firm i in year t

Xit = K*1 vector of explanatory variables

k 

= K*1 vector of constants

it 

= Error term

With i = 1,2,3,…………,N and t =1,2,3…………….T

If companies do not adjust immediately to their optimal cash holding levels, the

difference between the actual cash and it’s previous year’s cash is given by a

proportion of the difference between optimal cash and the previous year’s cash

holdings:

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*

1 , 1 Cash ( )

it it i t it Cash Cash Cash

    

, 0<  <1

Where, Cashit and Cashit-1 are the actual cash ratios in the current and previous

period respectively.  represents the proportion of the adjustment to the optimal

level. Non-existence of adjustment cost implies that the value of  equal to 1 signifying that the company adjusts to target cash ratio immediately; while a value

equal to zero means that the costs of adjustment are so high that it is inefficient for

the company to change its cash level. Hence, in reality, this value ranges between 0

and 1. The following empirical model used to estimate dynamic nature of cash

holdings.

Cashit TAit

= β0 + β1 Sizeit + β2 NWCit

TAit + β3

CFit TAit

+ β4MBRit + β5 CEit TAit

+ β6 R&Dit

Sit + β7 LEVit + β8 DIVit + β9 NDIit + β10 NEIit

+ β11 TBYit + β12 DFSit

+ β13 AGEit+β14 RISKit + i i it

   

Opler et al. (1999) argues that Cash balances are mean-reverting. It seems that firms

have an implicit and an unobservable cash holdings target, but the adjustment of real

cash holdings to targeted cash holdings is only partial. In other terms, a delay can

exist in the adjustment process because of positive costs of adjustment due to

imperfections in the market. Even if firms have cash holdings targets, the right-hand

variables used in the static estimations have to be taken into account. To deal with

the potential dynamic nature of cash holdings, a dynamic panel data model was

employed. This study used Arellano and Bond (1991) and Arellano and Bover (1995)

estimators. At first, we applied Arellano and Bond tests for first and second order

serial autocorrelation of residuals. If εit is not serially correlated, the difference

residuals should be characterized by a negative first-order serial correlation and the

absence of a second-order serial correlation. The Sargan test for the validity of over-

identifying restrictions and the quality of the instruments is implemented for each

regression. It conducts test for the null hypothesis that the remaining theoretical

orthogonality restrictions are equal to zero Sargan (1958). Failure to reject the null

hypothesis of this test indicates that the instruments are valid and supports the

validity of the dynamic panel model specification.

We implemented "system GMM", outlined in Arellano and Bover (1995)

and Blundell and Bond (1998). This estimator was designed to deal with the fact that

lagged levels are often poor instruments for first differences. One can add moment

conditions to increase efficiency of the model. This technique adds to the system of

estimated differenced equations the original equations in levels. In these equations

predetermined and endogenous variables in levels are instrumented with suitable lags

of their own first differences, in order to control for firm-specific effects. These

lagged differences are appropriate instruments as long as the correlation between the

explanatory variables and the firm-specific effect is time-invariant.These two

estimators have one- and two-step variants. Theoretically, the two-step estimators are

Where 𝛽0 = 𝛼𝜆, 𝛽1 = 1 − 𝜆, 𝛽𝑘 = 𝜆𝛽𝑘 and 𝜀 𝑖𝑡 = λ𝜇𝑖𝑡

145

asymptotically more efficient, but their estimates of the standard errors are biased. To

take into account this fact, we use the finite- sample correction to the covariance

matrix following Windmeijer (2005). In this study, generalized methods of moments

(GMM) are used with system GMM method of one step and two step (Blundell and

Bond (1998), SYS-GMM).

RESULTS & DISCUSSIONS

Univariate analysis

Descriptive statistics and the correlation matrix of the variables employed for the

model are presented in the Table 2 and 3 respectively. As per Table 2, we observe

that the minimum and maximum cash ratio of listed firms are 0 and 1 respectively.

The mean cash ratio of observed sample is 10.4 per cent with a standard deviation of

13.2 per cent, which is higher than the average cash ratios reported for US listed

firms (Kim et al., 1998; Opler et al., 1999;Bates et al. 2009; Kim, Kim and Woods,

2011; Al-Najjar, 2013). The median cash ratio of sampled firm equals to 4.8 per cent

which is lower as compared to the median value of 5.9 per cent reported by (Ferreira

and Vilela, 2004; Ozkan and Ozkan 2004). The mean value of leverage is 0.516 with

a standard deviation of 0.25, which indicates that on an average depend on more on

equity financing than debt. Table 3 reports the results for Pearson pair wise

correlations among the variables used for estimation.The correlation between

dependent variable of cash ratio and capital expenditure, leverage, networking capital

is significantly negative, suggesting that the amount of cash held by the firm will be

decreased due to the increase in the capital expenditure, leverage and networking

capital. On the contrary, there is a significant positive correlation between net equity

issued, risk, size, dividends, cash flow, age and cash holdings of the sampled firms.

The leverage of a firm has significant negative correlation with the size and

networking capital of the firm.

Empirical results

The one-step estimation results for SYS-DIFF are reported under the Table 4. As per

empirical results, the null hypothesis of Sargan test was rejected in the presence of

heteroskedasticity due to lower p-value. Hence, we estimated the equation with two-

step model and the results are reported under Table 5. We find an evidence of

negative first-order serial correlation and no evidence of second-order serial

correlation in the first difference of errors. The null hypothesis of Sargan test of over-

identifying restriction is not rejected, indicating that the instruments and residuals are

independent. The Sargan test also indicates that the instruments used in the GMM 2

estimation are valid, which means that the instruments used are not correlated with

the error term. The Wald test of the joint significance of regressors is also satisfied.

We have used robust standard errors in order to eliminate the impact of any

econometric issues.

As per the results reported under Table 5, we found that the coefficient of

lagged cash ratio is positive and significantly different from zero. The adjustment

coefficient is 0.406 (λ) given by (1-0.594), Our estimated adjustment coefficient is

below 0.5 signifying that a typical firm in our sample closes more than half the gap

between actual and target cash holdings within one and half year and the entire gap

within two and half years.

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The significant negative coefficients associated with variables NWC/TA,

LEV, CE/TA, DFS and TBY specify that networking capital, leverage, capital

expenditure, default spread and T-bill yield has a negative association with the cash

ratios of sampled firms. All these variables are significant at 1 per cent level of

significance. The regression coefficient of networking capital has a negative sign and

it is statistically significant, it implies networking capital can be regarded as a

substitute for holding larger amounts of cash and marketable securities. This also

suggests that firms with more liquid assets can exchange those assets into cash and in

turn hold lesser levels of cash, as their liquidity needs are supported by the

networking capital (Opler et al., 1999; Dittmar et al., 2003; Ozkan and Ozkan, 2004;

Bates et al., 2009)

The negative effect of cash flows is consistent with the view that firms that

have higher cash flows are expected to hold the lesser amount of cash as a result of

higher profitability and cash flows. The significant positive coefficients for variables

DIV, NDI and NEI indicate that cash-holding levels of sampled firms are positively

affected by the dividends, net debt issuance and net equity issuance. Except dividend

all these variables are significant at 1 per cent level. We also found a significant

positive relationship between cash holdings and dividends suggesting that dividend

paying firms maintain larger cash holdings in order to assure the payment of dividend

to their shareholders. The estimated coefficients of both net equity and net debt

issuance are positive, suggesting that firms who raised the finance either through the

issue of equity or debt or both will have larger cash holdings. The positive impact of

net equity issuance on cash holdings is in line with the findings of the study by David

McLean (2011).

CONCULSIONS

Corporate cash holdings is an important liquid asset affecting liquidity and

profitability of a firm. Over the period there has been a dramatic increase in the level

of cash holdings across the companies in India. Cash holding level bear real

consequences on the financial and operating performance and policy of a firm in both

short run and long run. The present study investigated the speed of adjustment of

corporate cash holdings of Indian listed firms for a sample of 5094 firm years during

the period 2007-2012. This study employed dynamic panel data regression by using

SYS-GMM method. As per empirical results of one-step analysis, the null hypothesis

of Sargan test was rejected in the presence of heteroskedasticity due to lower p-value.

Hence, we estimated the dynamic panel data method with two-step model using

SYS-GMM method.

The findings indicate net working capital, leverage, capital expenditure,

default spread and T-bills rates have negative association with the cash to total asset

ratios while the dividends, net debt issuance and net equity issuance have positive

association with cash holding levels of the firm. The negative relationship with net

working capital and capital expenditure indicate the firms increasingly hold cash for

precautionary motive. The decrease in inventories and the greater importance of

R&D relative to capital expenditures might have effect on the cash holdings.

Probably lower asset tangibility of R&D investment opportunities would be costlier

to finance than capital using external capital expenditures. However we do not have

significant relationship for R&D in this study.

The empirical results revel that the coefficient of lagged cash ratio variable

is significantly positive and lesser than 1. This result validates the target adjustment

model for the cash holding of our sampled firms. The speed of adjustment of

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corporate cash holdings was estimated using the SYS-GMM method. We found that

the coefficient of lagged cash ratio is positive and significantly different from zero.

The adjustment coefficient is 0.406 (λ), the estimated adjustment coefficient is below

0.5 signifying that a typical firm in the sample closes more than half the gap between

actual and target cash holdings within one and half year and the entire gap within two

and half years.

This study concludes that sampled firms make a trade-off between the

positive costs of cash holdings adjustments and the costs of being far from the fixed

target. This study provides an insight to the dynamic nature and speed of adjustment

towards the target level of cash holdings of sample firms, which might help a finance

manager for management of cash holdings. Effective liquidity management leads to

the realization of long-term financial goals and objectives of a firm. Alternation in

level of debts, assets, dividends, buy back of shares, and management of working

capital are some of the possible ways for financial managers of firms to adjust the

cash holdings to optimal level.

TABLE 2: DESCRIPTIVE STATISTICS

CASH/TA CE/TA CF/TA LEV NDI NEI NWC/TA

Mean 0.104 0.066 0.079 0.516 0.016 0.022 -0.016

Median 0.048 0.046 0.072 0.529 0.000 0.000 -0.013

Maximum 1.000 0.762 1.687 2.606 0.572 1.000 0.720

Minimum 0.000 -2.164 -0.902 0.000 -2.310 -13.808 -1.238

Std. Dev. 0.132 0.121 0.089 0.247 0.080 0.211 0.174

Skewness 2.249 -4.102 1.146 1.052 -4.787 -55.139 -0.771

Kurtosis 9.069 74.894 37.331 9.042 172.180 3638.329 7.346

Observations 5094 5094 5094 5094 5094 5094 5094

TABLE 3: PEARSON CORRELATION MATRIX BETWEEN VARIABLES

RD/S RISK SIZE TBY DIV DFS AGE

Mean -0.025 0.034 9.469 0.066 0.741 0.025 33.903

Median 0.000 0.022 9.201 0.069 1.000 0.026 26.000

Maximum 2.569 0.976 14.898 0.084 1.000 0.031 149.000

Minimum -149.800 0.000 -0.693 0.034 0.000 0.017 0.000

Std. Dev. 2.099 0.049 1.178 0.017 0.438 0.004 22.100

Skewness -71.313 6.946 0.984 -0.828 -1.098 -0.635 1.376

Kurtosis 5088.436 80.325 5.506 2.614 2.206 2.559 5.138

Observations 5094 5094 5094 5094 5094 5094 5094

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TABLE 4 :ONE-STEP DYNAMIC PANEL, USING 4245 OBSERVATIONS

INCLUDED 849 CROSS-SECTIONAL UNITS- (SYS-GMM)

Variabl

e Cash/TA CE/TA CF/TA LEV NDI NEI

NWC/T

A

Cash/TA 1.000

CE/TA

-

0.109**

* 1.000

CF/TA

0.285**

*

0.086**

* 1.000

LEV

-

0.365**

* 0.023

-

0.460**

* 1.000

NDI -0.022

0.311**

*

-

0.047**

*

0.107**

* 1.000

NEI 0.031** 0.027 -0.030** -0.025 -0.008 1.000

NWC/T

A

-

0.118**

* -0.014

0.123**

*

-

0.256**

* 0.024 0.030** 1.000

RD/S 0.004 0.007 0.012 0.026 0.003 0.001 -0.001

RISK

0.047**

*

-

0.090**

* 0.025

0.127**

*

-

0.037**

* 0.000

-

0.137**

*

SIZE

0.097**

* 0.012 0.029**

-

0.057**

* 0.026 -0.010

-

0.106**

*

TBY -0.008

-

0.065**

* -0.006 -0.005 -0.020 0.003 0.024

DIV

0.172**

*

0.091**

*

0.405**

* -0.312 0.046 -0.025 0.120

DFS

-

0.057**

*

-

0.049**

*

-

0.079**

* 0.005 -0.043 -0.056 -0.066

AGE

0.078**

*

-

0.078**

*

0.076**

* -0.031

-

0.059**

*

-

0.047**

*

-

0.057**

*

Variable RD/S RISK SIZE TBY DIV DFS AGE

RD/S 1.000

RISK 0.009 1.000

SG 0.003** -0.017

SIZE -0.038 -0.111 1.000

TBY -0.013 -0.011** 0.072*** 1.000

DIV 0.023 -0.215 0.122 -0.010 1.000

DFS -0.002 -0.010*** 0.109 -0.369 -0.034 1.000

AGE 0.017 -0.002 0.098*** 0.031 0.122*** 0.025 1.000

Variable Coefficient Std. Error z p-value

Cash/TA(-1) 0.628 0.048 13.174 0.000 ***

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const 0.119 0.021 5.638 0.000 ***

CE/TA -0.126 0.029 -4.261 0.000 ***

LEV -0.054 0.011 -4.991 0.000 ***

CF/TA 0.097 0.033 2.960 0.003 ***

RD/S 0.000 0.000 0.327 0.744

NWC/TA -0.040 0.011 -3.649 0.000 ***

NEI 0.047 0.022 2.099 0.036 **

NDI 0.084 0.028 3.024 0.003 ***

DIV 0.002 0.004 0.445 0.656

SIZE 0.002 0.002 1.129 0.259

RISK -0.003 0.049 -0.067 0.947

AGE 0.000 0.000 2.770 0.006 ***

DFS -2.816 0.461 -6.114 0.000 ***

TBY -0.298 0.061 -4.862 0.000 ***

Sum squared resid 20.167 S.E. of regression 0.069

Number of instruments = 30;

Test for AR(1) errors: z = -9.75705 [0.0000]

Test for AR(2) errors: z = -0.0124762 [0.9900]

Sargan over-identification test: Chi-square(13) = 116.298 [0.0000]

Wald (joint) test: Chi-square(16) = 1164.78 [0.0000]

TABLE 5 :TWO-STEP DYNAMIC PANEL, USING 4245 OBSERVATIONS

INCLUDED 849 CROSS-SECTIONAL UNITS - (SYS-GMM)

Variable Coefficient Std. Error z p-value

Cash/TA(-1) 0.594 0.057 10.354 0.000 ***

const 0.086 0.021 4.146 0.000 ***

CE/TA -0.084 0.029 -2.931 0.003 ***

LEV -0.053 0.010 -5.067 0.000 ***

CF/TA 0.105 0.036 2.917 0.004 ***

RD/S 0.000 0.000 0.311 0.756

NWC/TA -0.039 0.011 -3.477 0.001 ***

NEI 0.041 0.017 2.456 0.014 **

NDI 0.062 0.028 2.203 0.028 **

DIV 0.000 0.004 -0.048 0.962

SIZE 0.003 0.002 1.684 0.092 *

RISK 0.023 0.052 0.442 0.658

AGE 0.000 0.000 3.095 0.002 ***

DFS -2.126 0.427 -4.979 0.000 ***

TBY -0.214 0.061 -3.525 0.000 ***

Sum squared resid 20.985 S.E. of regression 0.0705

Number of instruments = 30;

150

Test for AR(1) errors: z = -8.23953 [0.0000]

Test for AR(2) errors: z = -0.233543 [0.8153]

Sargan over-identification test: Chi-square(13) = 37.1706 [0.5004]

Wald (joint) test: Chi-square(16) = 1001.17 [0.0000]

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