Phase 3: Methodology and Results -Research Paper -Team Project

When Auditor Changes Reveal Risk: Predecessor Exit Signals and Audit Fee Changes

Katherine Labaut

Florida Gulf Coast University

ACG 6655: Independent Audit II

Dr. Daniel Acheampong

September 20, 2026

Introduction and Research Context

When a company switches auditors, the SEC makes them explain why the change happened. These explanations might mention whether the old auditor quit, was fired, or found problems with how the company controls its finances. This information tells us something important about the company and the audit, but most people just think about the fact that the auditor changed. The real question we want to answer is: does this risk information that gets disclosed affect what the new auditor charges?

Think about how auditors set their fees. They charge based on how much time the audit will take, how complicated the company is, and how risky the engagement seems. We know from past research that fees depend on things like company size and complexity. But nobody has really studied what happens to fees when a company discloses multiple problems all at once during an auditor change. Most studies in the past have looked at either how tenure affects fees or how switching auditors affects fees, but they looked at these things separately. Plus, they built their study samples by piecing together data from different sources instead of looking at the actual SEC filings where auditor transitions are reported. This means they missed the chance to study what really happens when multiple risk signals come out together.

Our study fills this gap by looking directly at the SEC filings about auditor transitions and asking whether the risk signals disclosed in those filings, such as conflicts, control problems, and other reporting issues, predict how much audit fees change. By studying the actual transition events reported to the SEC, we can see whether audit fees respond to the specific risks that companies disclose about the transition.

Literature Review and Identification of Research Gap

Researchers have learned a lot about what affects audit fees. Back in 1980, Simunic showed that the size of the company and how complicated it is are the main things that determine what auditors charge. More recently, in 2006, Hay, Knechel, and Wong looked at many different audits fee studies and found that these same relationships hold true no matter what time period you look at or what country you are in. This tells us that auditors do raise or lower their fees based on company characteristics that can be measured. But even with all this research, one important question has never really been answered: when a company changes auditors and discloses information about risk, do auditors adjust their fees to account for that information?

Researchers have studied auditor changes, but usually they look at one specific situation at a time. Some studies compare resignations to dismissals. Others look at cases where the auditor disagreed with the company. Still others examine how long it takes a company to find a new auditor. Bleibtreu and Stefani found in 2018 that fees go up in the first year after an auditor change in multiple countries. This suggests that auditors charge more because of the costs involved in learning about a new client. DeFond's work on how long auditors stay with one client shows that auditors who have worked with the same company for many years have become more efficient and charge less.

The gap in the research is that all these studies look at one type of signal separately. Some look at resignations, some at dismissals, some at disagreements, and some at control problems. But none of them ask what happens when all these signals show up together during one auditor transition. The SEC requires companies to disclose when auditors change and why, which gives us a chance to study this. All the risk signals come out at the same time as the auditor transition is announced. This is what makes our research different. We want to know whether having multiple risk signals together predicts even bigger changes in audit fees than we would expect from other factors.

Research Questions and Hypotheses

Our first research question is straightforward: when a company discloses risk signals about an auditor departure, do audit fees go up? When an auditor leaves under circumstances that suggest problems, like disagreements or control issues, the new auditor has to figure out what those problems are and how to handle them. This might require more work or more risk assessment. We expect that when these risk signals are disclosed, the new auditor will charge higher fees to cover the extra work involved. Here are our hypotheses:

Research Question 1:

Null Hypothesis: Audit fees do not change differently when risk signals are disclosed versus when they are not.

Alternative Hypothesis: When risk signals are disclosed, audit fees increase more than when no risk signals are present.

Our second research question gets more specific: we know from our data that control problems and conflicts often show up at the same time. In fact, over 2,500 observations have both types of signals. So we are asking: when a conflict signal is already present, do control signals add anything extra to the fee calculation? In other words, does information about control problems help predict fees beyond what the conflict signal alone tells us?

Research Question 2:

Null Hypothesis: Among transitions with a conflict signal, control problems do not make fees change any differently.

Alternative Hypothesis: Among transitions with a conflict signal, control problems are associated with even larger fee increases.

Our third research question looks at the total number of risk signals. If one risk signal leads to higher fees, what happens when there are two signals or three signals? We think the effect might be cumulative, meaning more signals lead to bigger fee increases.

Research Question 3:

Null Hypothesis: The total number of different risk signals does not affect how much audit fees change.

Alternative Hypothesis: The more different risk signals present, the larger the increase in audit fees.

Theoretical Development and Risk Signal Mechanism

Disclosed information during auditor transitions affects what auditors charge because of how information works in markets. Auditors set their fees based on how risky they think an engagement is and how much work they expect to do. When a company discloses risk signals about the transition, like mentioning disagreements or control problems, it gives the new auditor clearer information about the engagement. Without these disclosures, there would be more uncertainty. But when the company is required to disclose these problems, the new auditor knows specifically what issues exist and what they may need to address.

This clearer picture helps the new auditor predict what the engagement will require. They can estimate the work needed to handle the known problems or disagreements better. When auditors have better information about risk, their fee increases make sense and reflect the real risk involved. It is not just about the normal costs of learning a new client. Instead, the fee covers the specific risks that were disclosed.

This is different from what other researchers have studied about auditor changes. Some studies look at whether new auditors charge more in their first year just because they are new. Other studies look at whether auditors who have worked with a client for many years charge less because they know the client better. Our research is different because we are examining what happens when specific risk information is disclosed. The SEC requires companies to disclose these transition circumstances, which creates an opportunity for us to study whether audit fees increase when risk signals are present and whether more risk signals lead to bigger fee increases.

Research Design and Methodology

We obtain our data from the Wharton Research Data Service Audit Analytics database, which contains auditor change events disclosed in SEC filings from January 2000 through June 2026. The database encompasses 471 variables detailing auditor identities, transition dates, transition circumstances, and audit fee information. Our initial sample includes 19,384 auditor change observations with complete predecessor and successor auditor identification and transition date information. To make sure our fee data and timing are reliable; we apply specific selection rules to our sample. We keep only observations where we have both the fees from before the auditor changes and the fees from after. We also require that the fiscal year end for the prior fees occurs before the predecessor auditor left, and the fiscal year end for the matched fees occurs after the new auditor started. Using these selection rules, we ended up with 13,519 observations. We then applied additional requirements, keeping only observations with complete information about company size, profitability, whether the company had losses, and what type of auditor they hired. This narrowed our sample to 11,444 observations that we could analyze.

The main outcome we are measuring is how much the audit fees changed around the time the auditor switched. We calculate this by taking the matched fees (from after the new auditor started) and subtracting the prior fees (from before the previous auditor left). It is important to be clear that we are calling this the fee change during the transition, not the full year fee for the new auditor. We make this distinction because we cannot be sure from our data that the new auditor performed the entire audit in that fiscal year.

The main factors we are interested in are the risk signals that get disclosed during an auditor transition. We look at conflict signals, which means the predecessor auditor and the company disagree about accounting or reporting. We look at control signals, which means the predecessor auditor found problems with internal controls. We look at reporting signals, which means there was disagreement or other reporting issues. An important finding from our data is that these types of signals often happen at the same time. For instance, 2,514 observations show both a conflict signal and a control signal together. We measure these both as yes or no variables (is a conflict signal present?) and as a count of how many different types of signals are present (ranging from zero to three).

We also include information about the auditors, such as whether they are part of a Big 4 firm and whether they are registered with the PCAOB. For the company, we measure size using total assets, profitability using annual net income, loss status, what industry they are in using SIC codes, and how long the company has been operating. We also account for time effects and industry effects to capture how fees might vary by year or by industry.

To test our hypotheses, we use a statistical method called ordinary least squares regression. Our outcome variable is the fee change, and we see how the risk signals predict this change. We start with basic models and gradually add more company and auditor factors to see if our risk signal results hold up when we include these other variables. We also break down our analysis by company size and industry to see whether the relationship between risk signals and fees works the same way for all companies or whether it differs depending on company characteristics.

Data Availability and Verification

We get all the data we need from the Audit Analytics database through WRDS. The database tells us when auditors change by providing the dates when the old auditor left and when the new auditor started. We can identify both the predecessor and successor auditors, which lets us figure out whether they are Big 4 firms and whether they are registered with the PCAOB. The database also has multiple ways to measure audit fees. For our main analysis, we use the matched fiscal year fee, which we verified carefully when we built our sample. We also run additional tests using different ways of measuring fees to make sure our results are consistent. The database includes financial information like total assets, revenue, net income, and cash flow measured over multiple years. We also have industry information for every company using SIC codes.

Our data sample is large enough to give us reliable results. We have 13,519 observations where fees are positive and meet our selection criteria. When we add the additional requirements about company information, we get 11,444 observations we can analyze. When we looked at risk signals, we found that 9,904 observations had no risk signals, 1,075 had one signal, 2,037 had two signals, and 503 had three signals. This spread across signal counts helps us see whether fees increase more when companies have more risk signals. Our data covers 26 years from 2000 to 2026 and includes companies from different industries and of different sizes. This wide range means our findings should apply more broadly rather than just to one type of company or industry.

Significance and Contribution

This research adds to what we know about how audit fees are set. Specifically, we look at whether audit fees change based on information that companies disclose when they switch auditors. Researchers have studied audit fees extensively, but no one has really answered this question: when a company discloses multiple risk signals all at once during an auditor transition, do these signals together predict changes in what the new auditor charges?

Our findings have real-world applications for several groups. Audit committees that are thinking about switching auditors can use our results to decide whether the fees the new auditor is proposing make sense given the risks that were disclosed. Finance teams can check whether the fees being charged match the circumstances of the transition. Audit firms can compare their own pricing to what the research shows about the relationship between risk and fees. When management and audit committees are deciding whether to change auditors or which firm to hire, they can consider what our study shows about how fees respond to disclosed risk.

What makes our work different from previous research is this: other studies have looked at either tenure effects or switching effects on their own. We are asking what happens when multiple risk signals show up together during a transition that is disclosed to the SEC. Instead of trying to build an auditor change sample from different sources, we look at the actual SEC filings where transitions are announced. This lets us see the real moment when risk information is disclosed and when fees might change. Our main question, whether fees go up when risk signals accumulate during auditor transitions, helps fill in a gap in understanding. Specifically, it helps us understand how auditors price risk when that risk is openly disclosed and can be verified through SEC filings.

Overall, this study gives us real evidence about whether audit fees actually reflect the risk signals that companies disclose during auditor transitions. This helps us better understand how the audit market works and how auditors set prices when engagement risk is clearly communicated to the market.

References

Bleibtreu, J., & Stefani, R. (2018). Auditor changes and audit fees: Evidence from emerging markets. Journal of Accounting and Public Policy, 37(1), 17–35.

DeFond, M. L., Raghunandan, K., & Subramanyam, K. R. (2002). Do non-audit service fees impair auditor independence? Evidence from going concern audit opinions. Journal of Accounting Research, 40(4), 1247–1274.

Hay, D. C., Knechel, W. R., & Wong, N. (2006). Audit fees: A meta-analysis of the effect of supply and demand attributes. Contemporary Accounting Research, 23(1), 141–191.

Litt, B., Sharma, D., Simpson, S. J., & Tanyi, P. N. (2014). A roadmap of auditor switching research. Auditing: A Journal of Practice & Theory, 33(4), 1–28.

Simunic, D. A. (1980). The pricing of audit services: Theory and evidence. Journal of Accounting Research, 18(1), 161–190.