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An empirical examination of the relationship between audit fee and firm performance.

INTRODUCTION

Sarbances-Oxley Act of 2002 provides the framework for the current model for corporate governance in the United States since the corporate scandals of the 1990s. Effective corporate governance involves protecting shareholders' rights by ensuring the equitable treatment of all shareholders including minority and foreign shareholders. Strong corporate governance encourages active cooperation between the corporation and its stakeholders and promotes an environment of transparency in the disclosure of financial performance. Good governance involves effective monitoring of management by the board and requires that the board be accountable to the firm and shareholders. Effective corporate governance results in the sustainability of financiallysound enterprises and promotes investor confidence.

Effective governance can only be achieved when the structure of corporate governance involves internal and external monitoring. Among other rules, Mintz and Krishnan (2009) Sarbanes-Oxley imposes standards which require that:

* External auditing companies should be banned from offering consulting services in the interest of truly independent audits and the avoidance of conflicts of interest. (Note: Not only may audit firms not offer consulting services to SEC audit clients, but they are also banned from offering accounting information systems services, appraisal and valuation services, bookkeeping related to record keeping and financial reporting, actuarial services, internal audit outsourcing services, management functions or expert services, recruitment services, investment banking services and legal services.)

* The audit committee commands authority over the auditing activities. To this end, the audit committee designates the external auditors, evaluates and resolves differences between the auditors and management, reviews internal controls, and provides approval of any major changes in accounting methods.

* CEOs and CFOs must certify the accuracy of the financial statements filed with Securities and Exchange Commission (SEC).

* Section 404 requires that management evaluate internal controls and mandates that auditors independently review operations and produce a report which becomes part of an integrated audit of the financial statements.

Given the corporate scandals of 1990s and the financial crisis of 2008, investors are more than ever concerned about the quality of corporate governance and the need for transparency and its impact on stock market and portfolio performance. This widespread view that 'governance matters' necessitates the creation of metrics that allow investment managers to quickly and accurately identify the relative performance of companies vis a vis their governance structures (Malhotra & McDonald, 2011). One of the pillars of good corporate governance is independent audit of financial statements by independent public accountants so that the information is transparent and investors and creditors can make their investment and lending decisions in an informed manner. The audit of ENRON by Arthur Andersen (AA) exemplified how conflicts of interest could potentially impact auditor independence. AA's audit fees were $25 million while its consulting fees were $27 million for the year 2000. Of course, before SOX, many audit firms often contended that consulting for audit clients actually enhanced audit performance, providing auditors with greater insight into their client's financial activities. But after the debacles of firms like ENRON and WorldCom, the U.S. Congress would have none of it, passing the SOX Act and barring such apparent conflicts of interest that potentially increase risk that audits may fail to fairly assess financial statement information.

The study is important for several reasons. First, if there is a link between audit fee and a firm's performance as measured by firm's return on asset and return on equity, then shareholders should notice it.

The second reason that the topic is important is that investors need to know if audit fee which is considered an important governance variable and is used in all governance measuring metrics contributes to improved risk-adjusted returns.

Thirdly, by analyzing the relationship between audit fee and firm performance, this study will provide investors and regulators a better understanding of the impact of audit fee and audit quality on the corporate performance.

PREVIOUS STUDIES

Previous research points out the relationship between audit committee characteristics and better governance. For instance, Persons (2009) finds that firms which made earlier voluntary ethics disclosure were likely to have a larger and more independent audit committee that met more often, and were less likely to engage in fraudulent financial reporting.

Moutinho, Cerqueira, and Brandao (2012) found that increases in operating performance of a firm are accompanied by decreases in audit fees. Iyengar and Zampelli (2007) found a significant negative relationship between non-audit fees and the sensitivity of CEO pay to firm performance.

Chan, Farrell, Healy, and Lee (2011) examined firm performance following a change in auditor due to audit fee savings. They found that change in auditors does not cause a change in firm performance in a statistically significant manner.

Stanley (2011) hypothesized a link between observed audit price as measured by audit fee and future changes in a company's economic performance. The study reported that audit fee disclosure is a leading indicator of the operating performance dimension of clients' business risk.

Prior studies provided consistent evidence of a negative relation between audit fees and the current economic state of a client firm. According to Simunic (1980), leverage, return on investment, loss history and liquidity are associated with client's financial distress and as a result, they are likely to be proxies for client's business risk. Choi, Kim, and Zang (2010) hypothesized return on assets as a proxy for risk of a client that can be used to explain audit fee. Bell, Landsman, and Shackelford (2001) reported that risk characteristics of a client (also known as business risk) impact hourly audit fee and number of audit hours required to audit the client. Barron, Pratt, and Stice (2001) also pointed out that in industries that have a high litigation rate, audit fees tend to be on the higher side, because audit risk increases significantly. Bedard and Johnstone's (2004) study showed if a company has a record of weak corporate governance and they have a higher risk of earnings manipulation, auditors increase their fees, because they will have to spend higher number of hours to audit that client.

Mitra, Hossain, and Deis (2007) studied the relationship between ownership characteristics and audit fee. The reported that audit fee is positively related to diffused institutional stock ownership of a company and is negatively related to managerial ownership of a company. Chan, Farrell, Healey, and Lee (2011) reported that a change in auditor to save audit fee resulted in positive stock return and earnings performance.

This study extends previous studies on corporate governance by analyzing the link between audit fee and firm performance for the period 2001 to 2011.

DATA AND METHODOLOGY

In order to evaluate relationship between audit fee, non-audit fee, and firm performance, we use panel data analysis to assess the relationship between audit fee, non-audit fee, managerial compensation, and firm's performance as measured by return on assets and return on equity for thirty companies that are part of the Dow Jones Industrial Average over the period of 2001--2011. A panel is a cross-section or group of people who are surveyed periodically over a given time span. A panel data set offers several econometric benefits over traditional pure cross section or pure time series data sets. Panel data analysis is being used extensively in economics and finance research to study cross-country economic issues (Maddala, 1999 ; Webb & Hall, 2009). Panel data approach offers several advantages. Firstly, panel data methodology produces more reliable parameter estimates, because the number of observations is typically much larger in panel data. As result, linear regression results are more robustness.

In panel data analysis, explanatory variables vary in two dimensions cross-section and time series. Therefore, the variables are less likely to be highly correlated and as a result, panel data also alleviates the problem of multicollinearity. Furthermore, some of the effects cannot be detected in a pure cross section or time series data, but by combining cross section and time series data, panel data makes it possible to identify and measure effects that cannot be detected in pure cross section or time series data. For instance, sometimes it is argued that cross section data reflect short-run behavior, while time series data emphasize long-run effects. By combining the crosssection and time series features of a data set, a more general and comprehensive dynamic structure can be formulated and estimated. According to Balestra (1995), the panel data accounts for the fact that individuals, firms, states, or countries are heterogeneous. Time series and cross-section studies that do not control for this heterogeneity run the risk of obtaining biased results (Baltagi 2000). Panel data controls for individual heterogeneity.

EMPIRICAL ANALYSIS

Table 1 provides summary statistics of the variables used in this study for the period 2001 and 2011. Audit fee is the annual dollar amount of fee paid to auditors of the firm to perform audit of the company. On an average, the audit fee has gone up every year from a low $8.52 million in 2001 to high of $27.84 million in 2011. Return on asset has fluctuated between a low of 6.53 percent in 2009 to a high of 8.65 percent in 2008. Panel data shows an average return on assets of 7.63%. Similarly, return on equity has fluctuated between a low of 17.91% in 2001 to a higher of 24.18 percent in 2011. Panel data shows an average return on equity of 20.27 percent.

In order to empirically examine the relationship between audit fees and a firm's performance over a period of 2001 to 2011, we model audit fee as a function of return on return assets (ROA) and return on equity (ROE). We run two regression models:

* First regression is the model in equation 1 that includes return on assets (ROA) as independent variable

* In the second regression, we include return on equity as independent variables, and

* Table 2 summarizes the panel data regression analysis of the relation between audit fee, non-audit fee, and a firm's performance for the period 2001 to 2011. Firm's performance is measured in terms of return on assets and return on equity.

As shown in Table 2, return on assets is negatively related to audit fee and is statistically significant at 1 percent rejection level. If return on assets is high, audit fee is lower, on an average by 23 basis points. With high return on assets, auditors perceive that there is a lower risk level for that company. If the return on assets is low, audit fee is high, because the signal to the auditors is that the company is not doing well and the management is not using the assets in an efficient way to generate return. Return on assets is also negatively related to total fee to auditors that includes audit related fees and tax fees.

When we use return on equity as a measure of a firm's performance, return on equity is weakly negatively related to audit fee. If a firm's return on equity is low, audit fee is higher. Audit fee is also negatively related to return on equity, but the relationship is not statistically significant.

In order to analyze the two way link between audit fee and firm performance, we use a panel cointegration framework for a panel of 30 companies over a period of 2001 to 2011. Table 3 summarizes the results of the unit root for audit fee, return on assets, and return on equity in levels. The results are reported with and without trend.

A Summary of the Tests of Nonstationarity

Table 3 shows Unit root test using Im, Pesaran, and Shin method is performed for audit fees, return on assets, and return on equity for a panel of 30 companies that are part of Dow Jones Industrial Average for the period 2001 to 2011. The null hypothesis is that all the series in the panel have a unit root and the alternative hypothesis is that some of the series have unit roots.

Unit root analysis is performed on audit fee, return on assets, and return on equity for a panel of 30 companies over a period 2001-2011. Im, Pesaran, and Shin (2003) unit root test is used to test for nonstationarity in the data. This test is based on Augmented Dickey-Fuller test for each individual data set. The null hypothesis is that all panels have a unit root (Ho: rhoi = 0 for all i). The alternative hypothesis is that the fraction of panels that are stationary is non-zero. This allows some of the panels to possess unit roots under the alternative hypothesis.

Table 3 shows that for the audit fee, return on assets, and return on equity, null hypothesis of unit root fails to get accepted when we perform the unit root test with constant and trend, which means the series are nonstationary.

Next step is to determine if the audit fee and return on assets series and audit fee and return on equity for a panel of 30 companies are cointegrated over the period of2001 to 2011. The authors of this study use Pedroni's (1999, 2004) approach to investigate whether long-run steady state or cointegration exist between the two variables. Cointegrations are carried out for constant and constant plus time trend and the summary of the results of cointegrations analyses are presented in Table 4.

The Pedroni's tests indicate that there is a long-run relationship between audit fee and return on assets, audit fee and return on equity for 30 Dow Jones Industrial Average companies. Given that audit fee, return on assets, and return on equity are cointegrated, the authors of this study use vector autoregression models (VAR) to analyze the two way link between audit fee and return on assets and audit fee and return on equity. Table 5 summarizes the results.

As shown in Table 5, when audit fee is the dependent variable, return on assets is negatively related to the audit fee, but the relationship is statistically insignificant. Similarly, when return on assets is the dependent variable, audit fee is negatively related to audit, but it is not statistically significant in explaining the return on assets. Table 5 shows that there is no causal relationship between audit fee and return on assets in a statistically significant manner. Part 2 of Table 5 examines the causality between audit fee and return on equity. Table 5 shows that there is no statistically significant evidence of causality between return on equity and audit fee.

LIMITATIONS OF THIS STUDY

A major limitation of this study is the data size. The study is restricted to only thirty companies that are part of Dow Jones Industrial Average. Furthermore, the study examines the relationship between audit fee and firm performance where the firm performance is measured in terms of return on assets and return on equity only. A company may be using a different measure of performance.

DIRECTIONS FOR FUTURE RESEARCH

The link between firm performance and audit fee needs to be examined with a larger data set. Also, other measures of performance should be used to examine the link between audit fee and firm performance. Furthermore, new regulation requires separation of audit fee and non-audit fee to maintain independence of auditors. Future researchers should examine if this has any impact on the amount of audit fee charged to risky companies by comparing the link audit fee and firm performance before and after the new regulation.

SUMMARY AND CONCLUSIONS

Corporate governance in general and audit quality in particular became the focus of regulators since the Enron and WorldCom debacle. The resultant legislation in the form of Sarbanes-Oxley Act (SOX) of 2002 provides specific guidelines for the audit committee to ensure effective corporate governance. The financial failure on Wall Street in 2008 further initiated the passing of the Dodd-Frank bill in 2010 and as a result, companies have taken measures to improve corporate governance by creating transparency.. The role of audit committee and auditor is central in ensuring good governance so that management acts in the best interest of shareholders to create value for them. In this study, we empirically examined the relationship between audit fees and a firm's performance over a period of 2001 to 2011. The authors defined firm's performance in terms of return on assets (ROA) and return on equity (ROE). The study found a link between audit fee and return on assets and return on equity. Deterioration in the performance of a firm as measured by lower return on assets and return on equity resulted in higher audit fee. These links between firm performance, as measured by return on assets and return on equity, and audit fees are consistent with the notion that as the auditor perceives greater risk in the audit engagement, audit fees will rise. We restricted our study to thirty companies that are part of the Dow Jones Industrial Average.

REFERENCES

Balestra, P. (1995). Introduction to Linear Models for Panel Data. The Econometrics of Panel Data--A Handbook of the Theory with Applications, edited by Laszlo Matyas and Patrick Sevestre, Kluwer Academic Publishers.

Baltagi, B. (2000). Econometric Analysis of Panel Data, John Wiley and Sons, England.

Barron O, Pratt, J., & Stice, J. (2001) Misstatement direction, litigation risk, and planned audit investment. Journal of Accounting Research, 39 (3), 449-462.

Bedard, J., & Johnstone, K. (2004) Earnings manipulation risk, corporate governance risk, and auditors' planning and pricing decisions. Accounting Review, 79 (2), 277-304.

Bell, T., Landsman, W., & Shackelford, D. (2001). Auditors' perceived business risk and audit fees: Analysis and evidence. Journal of Accounting Research, 39 (1), 35-43.

Chan, K. C., Farrell, B., Healy, P., & Lee, P. (2011). Firm performance following auditor changes for audit fee savings. Journal of Business & Economics Research, 9 (10), 17-25.

Iyengar, R., Zampelli, E., & Cohen, D. (2007). Auditing independence. Financial Management, 34-37.

Choi, J., Kim, J., & Zang, Y. (2010). Do abnormally high audit fees impair audit quality? Auditing, 29 (2), 115-140

Im, K.S., Pesaran, M.H., & Shin, Y. (2003). Testing for Unit Roots in heterogeneous Panels." Journal of Economics, 115 (1), 53-74.

Maddala, G. (1999). On the use of panel data methods with cross-country data. Annales D'economie Et De Statistique, 55(56), 429-449.

Malhotra, D., & McDonald, M. (2011). Recent Trends in Corporate Governance Practices, International Journal of Corporate Governance, 2 (3/4), 201--216.

Mitra, S., Hossain, M., & Deis, D. R. (2007). The Empirical Relationship between Ownership Characteristics and Audit Fees. Review of Quantitative Finance and Accounting, 28 (3), 257-285.

Moutinho, V., Cerqueira, A., Brandao, E., & Moreira, F. (2012). Audit fees and firm performance. Rochester: Social Science Research Network. doi:http://dx.doi.org/10.2139/ssrn.2180020

Pedroni, P. (1999). Critical Values for Cointegration Tests in Heterogeneous Panels with Multiple Regressors. Oxford Bulletin of Economics and Statistics, 61, 653-670.

Pedroni, P. (2004). Panel Cointegration: Asymptotic and finite samples properties of pooled time series Tests with an application to the PPP hypothesis. Econometric Theory, 20 (3), 597-625.

Persons, O. (2009). Audit committee characteristics and earlier voluntary ethics disclosure among fraud and no-fraud firms. International Journal of Disclosure and Governance, 6, 4, 284-297.

Simunic, D.A. (1980). The pricing of audit services: theory and evidence". Journal of Accounting Research, 6, 329-43.

Stanley, J. (2011). Is the audit fee disclosure a leading indicator of clients' business risk? Auditing, 30(3), 157-179.

D. K. Malhotra

Raymond Poteau

Philip Russel

Philadelphia University

About the Authors:

D.K. Malhotra is Nydick family term chair and professor of finance at Philadelphia University. Dr. Malhotra has published over 90 research articles in such journals as European Journal of Operational Research, Journal of Financial Research, Financial Review, Journal of Portfolio Management, Omega--The International Journal of Management Science, European Journal of Finance, Quarterly Journal of Business and Economics, Journal of Economics and Finance, Journal of Multinational Financial Management, Financial Counseling and Planning Journal, Journal of Retail Banking, The Real Estate Finance Journal, The International Journal of Finance, Journal of Intelligent Systems, Advances in Futures and Options Research, Journal of Commercial Lending, Journal of Global Business, Journal of Lending and Credit Risk Management, International Journal of Business, Marketing, and Decision Sciences among others.

Raymond R. Poteau is the Nancy Beacham Term Chair and professor of accounting at Philadelphia University. He has authored or coauthored Financial Accounting and Advanced Accounting textbooks, as well as articles and manuscripts in the areas of financial accounting & reporting, internal auditing, and finance. He worked in public accounting and health care finance for 12 years prior to starting his career in higher education. During a period of more than three decades with Philadelphia University, Professor Poteau also served as the dean of the School of Business Administration for a five-year period.

Philip Russel is a professor of finance and interim dean of the school of business administration at Philadelphia University. Philip's main research interests are in the areas of corporate bankruptcy and managed portfolios. His work has been published in several journals such as Quarterly Journal of Business and Economics, International Journal of Finance, Review of Financial Economics, Managerial Finance, Risk Management Association Journal, Journal of Alternative Investments, and others.,
Table 1
Summary Statistics of Data used in This Study

             Return on Assets,   Return on Equity,   Audit Fee,
             In percent          in percent          In millions

2001

Mean         6.78                17.91               8.52
Std. Dev     5.86                12.16               5.77
Maximum      19.80               45.37               23.50
Minimum      -2.99               -3.79               1.16

2002

Mean         6.58                18.23               12.00
Std. Dev     5.46                11.87               7.54
Maximum      19.81               46.02               38.70
Minimum      -1.31               -2.55               2.14

2003

Mean         6.97                17.95               14.27
Std. Dev     4.99                8.86                10.03
Maximum      16.24               42.31               55.30
Minimum      0.86                2.51                2.70

2004

Mean         7.84                18.98               19.98
Std. Dev     4.99                8.53                13.74
Maximum      15.96               37.28               78.20
Minimum      0.38                4.19                3.90

2005

Mean         8.48                20.79               20.59
Std. Dev     5.70                8.96                15.06
Maximum      17.94               41.27               89.40
Minimum      0.71                6.45                3.67

2006

Mean         8.68                23.20               22.44
Std. Dev     5.17                11.16               14.70
Maximum      18.10               51.57               85.80
Minimum      1.01                6.37                5.45

2007

Mean         8.43                22.64               23.16
Std. Dev     5.01                10.46               13.94
Maximum      22.26               45.23               81.40
Minimum      0.86                9.49                4.86

2008

Mean         8.65                17.68               25.03
Std. Dev     6.14                45.87               17.57
Maximum      24.29               91.60               94.30
Minimum      0.14                -205.10             4.96

2009

Mean         6.53                18.45               26.26
Std. Dev     5.04                14.76               19.92
Maximum      18.71               62.73               94.80
Minimum      -2.57               -7.98               4.61

2010

Mean         7.53                22.93               27.06
Std. Dev     5.70                22.66               20.24
Maximum      21.79               119.70              95.60
Minimum      -0.16               -1.70               4.56
Mean         7.48                24.18               27.84
Std. Dev     5.48                23.07               20.05
Maximum      21.30               114.11              96.60
Minimum      0.00                0.04                4.75
Mean         7.63                20.27               20.65
Std. Dev.    5.40                19.21               16.16
Maximum      24.29               119.70              96.60
Minimum      -2.99               -205.1              1.16

Table 2
Panel Data Regression Analysis of the Relation between Audit Fee, Non-
audit Fee, and a Firm's Performance for the Period 2001 to 2011.
Firm's Performance is Measured in Terms of Return on Assets and Return
on Equity

Dependent Variable        Audit Fee

Number of Observations    330

Model 1: Dependent Variable Audit Fee and Total Fee to Auditors and
independent variable is Return on Assets (ROA)

R-Squared                 0.23
Return on Assets          -0.89
t-statistics              -5.96: *

Model 2: Dependent Variable Audit Fee and Total Fee to Auditors and
independent variable is Return on Equity (ROE)

R-Squared                 0.11
Return on Equity-         -0.08
t-statistics              -1 73 ***

* Statistically significant at the 1% level, ** statistically
significant at the 5% level, and *** statistically significant at the
10% level

Table 3
A Summary of the Tests of Nonstationarity

Variables          Constant          Constant + Trend

Audit Fee          -4.83 (0.00 *)    -0.77 (0.22)
Return on Assets   -1.67 (0.05 **)   -0.46 (0.32)
Return on Equity   -2.88 (0.00 *)    -1.00 (0.16)

* Statistically significant at the 1% level, **statistically
significant at the 5% level, and ***statistically significant at the
10% level

Table 4
Cointegration Test for Panel Data for Audit Fee and Return on Assets

Test                       Constant       Constant + Trend

Cointegration Test for Panel Data for Audit Fee and Return on Assets

Panel v-Statistic       1.42 (0.08 ***)   1.39 (0.08 ***)
Panel rho-statistic     -3.97 (0.00 *)    -1.31 (0.10 ***)
Panel PP-Statistic      -13.36 (0.00 *)   -13.45 (0.00 *)
Panel ADF-Statistic     -0.35 (0.00 *)      -1.12 (0.13)
Group rho-Statistic      -0.90 (0.19)       1.67 (0.95)
Group PP-Statistic      -12.95 (0.00 *)    -10.35 (0.00)
Group ADF-Statistic      -0.81 (0.21)       0.24 (0.60)

Cointegration Test for Panel Data for Audit Fee and Return on Equity

Panel v-Statistic       2.11 (0.02 **)      0.53 (0.30)
Panel rho-statistic     -4.20 (0.00 *)    -1.32 (0.09 ***)
Panel PP-Statistic      -13.21 (0.00 *)   -11.36 (0.00 *)
Panel ADF-Statistic      -1.20 (0.12)       -0.26 (0.40)
Group rho-Statistic      -1.07 (0.14)       1.42 (0.92)
Group PP-Statistic      -13.31 (0.00 *)   -11.23 (0.00 *)
Group ADF-Statistic      -0.96 (0.17)       -0.33 (0.37)

* Statistically significant at the 1% level, ** statistically
significant at the 5% level, and *** statistically significant at the
10% level

Table 5
Causality between Audit Fee, Return on Assets, and Return on Equity
for 30 Dow Jones Industrial Average Companies for the Period 2001-
2011 Using Vector Autoregressive Model (VAR)

Causality between Audit Fee and Return on Assets

Variables               Audit Fee   Return on Assets (ROA)

Constant                  1.21               1.46
                         (1.08)            (3.39 *)
Audit Fee (-1)            0.84              -0.01
                        (20.48 *)          (-0.85)
Audit Fee (-2)            0.17              0.005
                        (4.01 *)            (0.29)
Return on Assets (-1)     0.06               0.73
                         (0.37)           (12.04 *)
Return on Assets (-2)     -0.07              0.13
                         (-0.44)          (2.20 **)

Causality between Audit Fee and Return on Equity (ROE)

Variables               Audit Fee   Return on Equity (ROE)

Constant                  0.55              16.84
                         (0.57)            (6.27 *)
Audit Fee (-1)            0.84              -0.11
                        (20.94 *)          (-0.99)
Audit Fee (-2)            0.17               0.10
                        (4.06 *)            (0.86)
Return on Equity (-1)     0.01               0.28
                         (0.47)            (4.35 *)
Return on Equity (-2)     0.02              -0.05
                         (0.78)            _(071)_
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Author:Malhotra, D.K.; Poteau, Raymond; Russel, Philip
Publication:International Journal of Business, Accounting and Finance (IJBAF)
Article Type:Report
Geographic Code:1USA
Date:Sep 22, 2015
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