The Influence of Earnings Management and Bank Market Structure on Bank Performance: International Evidence

Nacasius U. UjahAssistant Professor of Finance

College of Business and TechnologyUniversity of Nebraska at Kearny

ujahn2@unk.edu

Jorge BrusaProfessor of Finance

Sanchez School of BusinessTexas A&M International University

jbsa@tamiu.edu

Collins E. OkaforSanchez School of Business

Texas A&M International University okafor.e.collins@gmail.com

AbstractThe goal of this paper is to examine the influence of bank structure and earnings management on bank performance in international markets. We hypothesize that the different structure of banks could lead to different levels of earnings management and therefore to different levels of performance. To attain this goal, we examine the financial records of banks in China, India, Mexico, Nigeria, Russia and South Africa. The results of the investigation indicate: 1) the proxies for bank market structure (bank concentration ratio) and earnings management (loan loss allowance) have a significantly negative influence on bank performance. 2) The results of the percentile regression analyses indicate that upper percentile banks concentration ratio and loan loss allowance had higher inverse relation to performance as compared to lower percentile banks.

JEL Classification: C36, G21, M41, M42

I. Introduction

Healy and Wahlen (1999) defined earnings management as the alteration of firms reported economic performance by insiders to either ‘mislead some stakeholders’ or to ‘influence contractual outcomes.’ These insiders can use their discretion in financial reporting to overstate the ‘true’ position of earnings and bide unfavorable earnings realizations that may prompt outsiders to take action against insiders. Leuz, Nanda and Wysocki (2003) extended the previous definition indicating that in the presence of extensive earnings management, financial reports inaccurately reflect firm performance and consequently weaken outsider’s ability to govern the firm . Empirical evidence on earnings manipulation in developed countries includes; Teoh, Welch, and Wong (1998) who found that issuers of initial public offerings with unusually high accruals – earnings manipulation – experience poor stock returns in the three years after the IPO. Perry and Williams (1994) reported that managers increase discretionary accruals in the year prior to the announcement of management buyouts. Kleim (2002) shows that reduction in board or audit committee independence is accompanied by large increases in abnormal accruals.

Earnings management has been studied in international markets. For instance, Shen and Chih (2005) examined companies in 48 countries and found that in the majority of these countries managers manipulate earnings. Leuz, Nanda and Wysocki (2003) reported that economies with strong enforcement display the lowest level of earnings management and economies with weak enforcement display highest level of earnings management. .

Researchers not only examined earnings management in industrial firms, but also in financial firms. Some of these investigations include Adams, Carow and Perry (2009) who showed that insiders benefit from the managed accounting information despite the regulatory oversight of thrifts and Kanagaretnam, Lim and Lobo (2010) who reported that auditor’s expertise mitigate managed earnings for banks in twenty nine countries. More recently, Meisel Scott (2013) examined earnings management in bank mergers.

Still pending the investigation in the area of international banking is the combined influence of earnings management and bank structure on bank performance. We hypothesize that the different structure of the banks could lead to different levels of earnings management and therefore different levels of performance. The goal of this paper is to investigate this hypothesis. To attain this goal, we examine the financial records of banks in China, India, Mexico, Nigeria, Russia and South Africa. The data used in our investigation is from the Bureau van Dijk database and the period examined is from 1997 to 2009.

The results of the investigation indicate: 1) the proxies for bank market structure (bank concentration ratio) and earnings management (loan loss allowance) were statistically significant on influencing bank performance. 2) The influence of these variables on bank performance was negative. It means that the higher the level of concentration, the higher the level of earning management and therefore the lower the bank performance. 3) The results of percentile regression analyses indicate that upper percentile banks concentration ratio and loan loss allowance had higher inverse relation to performance as compared to lower percentile banks. The rest of this paper is organized as follows. Section 2 reviews supporting literatures dealing with scholarly articles of earnings management and banking sector structure. Section 3 describes

the methodology and data used in this paper. Section 4 presents the empirical estimation results, robust models to strengthening the result, addresses issue of endogeneity, and discusses the summary statistics. Finally, section 5 offers our conclusion and suggest further research relating to the issue addressed in this paper.

II. Supporting Literature

To the best of our knowledge, we are the first to conjecture and to streamline if symmetry exists between earnings management of banks and their market structure. Past literatures on earnings management have focused on: first, the effect on firms performance and when firms are going public - (Erickson & Wang, 1999) showed increase accruals resulting in enhanced earnings prior to initial public offering, seasoned equity offer, and stock financed acquisitions. (Coles, Hertzel, & Kalpathy, 2006) show that firm manage earnings through decreased discretionary accruals in the period following the announcement of cancellations of executive stock option up to the time stock option is reissued. Consequently, (Gong, Louis, & Sun, 2008) find that managers deflate earnings prior to open market share repurchases and that the magnitude of managed earnings is related to the proportion of shares repurchased.

Second, on audit and other regulatory committee – (Xie, Davidson III, & DaDalt, 2003) find that board and audit committee members with corporate or financial backgrounds are associated with firms that have smaller discretionary accruals, also, the board and audit committee meeting frequency is associated with reduced levels of discretionary accruals. Finally, investor protection and corporate governance - (Leuz, et al., 2003) show that earnings management are higher for economies with small stock market, clustered ownership, weak investor rights, and weak legal enforcement. While these scholars did not address the effect of manage earnings in the banking industry, (Shen & Chih, 2005) did. They assert that, to abate banks’ incentives to manage earnings and improve the reliability of financial reports, stringent accounting disclosure requirements is more effective than developing strong anti-director rights.

Like earning management papers, banking market structure articles ignores to show a synergistic correlation between the degree of earnings manipulation and bank structures. Generally, bank structure articles tend to focus on the competitive nature of the industry. (Bikker & Haaf, 2002) show that concentration impairs banking competitiveness. Also, (Beck, Demirguc-Kunt, & Levine, 2003) propose that highly concentrated banking systems are less likely to suffer from crises. (Berger, Klapper, & Turk-Ariss, 2009) find that banks with higher degree of market power have less overall risk exposure and market power increases loan portfolio risk.

Howbeit, we draw our rationale on the effect of banking market structure on earnings management by considering the incentives for banks to manage earnings and the areas which earnings management usually occurs vis-à-vis loan provisions, possibly via expenses, margin, and revenue manipulations (Dechow, Sloan, & Sweeney, 1995). The incentives include effect of bank run, uncertainty of banks, and effect from regulation. (Shen & Chih, 2005) suggest that the opacity of banks exposes the entire financial system to bank runs, contagion, and other variation of systemic risk. (Morgan, 2002) noted that, uncertainty over banks stems from their assets, loans and trading assets, the risk of which are hard to observe or easy to change. Furthermore, (Morgan, 2002) indicated that banks with high leverage compounds the uncertainty over their assets, in turn, their assets present bankers with ample opportunities for risk or asset substitution,

which higher leverage inclines them to do so. (Shen & Chih, 2005)suggested that earnings management is one of the management skills that banks adopt to avoid violating regulations whose non-performing loan ratio, capital adequacy ratio, liquidity ratio are strictly regulated. As (Berger, et al., 2009) find that market power increases loan portfolio risk, therefore, we assume that market power and market concentration will increase the likelihood for banks to increase manage earnings.

III. Methodology

This paper uses two common measures of earnings management found in the literatures pertaining to banks managed discretionary and non-discretionary accruals. While other earnings management method exist like the original and modified (Jones, 1991) model to identify changes in accruals and capital structure, (Dechow, et al., 1995) model for addressing non-loan loss accruals, and the (Leuz, et al., 2003) models for country variations in earnings management, here we use the two loan model documented by (Hasan & Wall, 2004) which are loan loss reserves and loan loss provision. (Hasan & Wall, 2004) noted that –

Banks analyze loans for impairment on an individual basis where appropriate. However, given the cost of individual analysis, many types of relatively homogenous, small loans, such as credit

card loans, are analyzed on a portfolio basis. The accounting procedures followed by banks headquartered outside the United States follow the same basic steps. Perhaps the biggest

procedural difference is that many non-US banks divide their loan loss accounting into two parts: specific allowance or reserves. As a bank concludes that individual loans will not be

recovered in full, it adds to its specific allowances. The general allowances arises for groups of loans where the bank has not identified impairment on any specific loan, but based on historical

experience the bank believes it likely that some part of the loan portfolio is impaired.

Earnings Management –

Loan Loss ModelsA widely used and accepted model for literatures addressing earnings management in banks, in part, due to the recognition of revenue as it is earned and expenses as they are incurred regardless of the timing of the actual cash flows by banks (Hasan & Wall, 2004). The two common loan loss models are Loan Loss Provision (LLP) and Loan Loss Reserves or Allowances (LLA). (Adams, et al., 2009; Hasan & Wall, 2004) distinguish the two models as the former addresses banks accruals from the income statement, while the latter considers bank accruals from the balance sheet.

Both loan loss models can be stated below following (Hasan & Wall, 2004).

LLP¿

Assets ¿−1=a0

1Assets¿−1

+β1NPL¿

Assets ¿−1+β2

NCO¿

Assets¿−1+β3

LOAN ¿

Assets¿−1+β 4

EQUITY ¿

Assets¿−1+D+ϵ ¿

(1)

and

LLA¿

Assets ¿=a0

1Assets¿

+β1NPL¿

Assets¿+β2

NCO¿

Assets¿+ β3

LOAN¿

Assets¿+β4

EQUITY ¿

Assets¿+D+ϵ¿

(2)

Where the right hand side variables are our dependant variables regressed on non-performing loans which may suggest the proportion of non-performing loans the banks may expect to lose, net charge offs, total loans, and on equity. All of which were normalized by present total asset. We also extract the discretionary component of accrual earnings like the modified Jones model, hence, accounting for any noise or infraction from other variables.

Banking Structure – As discussed earlier, numerous scholarly works have addressed the effect of banking structure to other issues, but none to our knowledge has tried to show relation or cause and effect of a country’s banking market structure to the degree of managed earnings among banks. While structure may impact the degree of collusion and bank run, it is also possible that for emerging countries, their structure may exist to muffle whatever insiders do. Banks with monopoly power would determine, with respect to perfect competition, equilibrium with higher loan rates and a smaller quantity of loanable funds. This will clearly impact economic growth (Cetorelli & Gambera, 2001).

As majority of businesses and households in countries are affiliated with a bank through deposit accounts, loans, or other financial services, and hence are directly affected by the pricing conduct and other behavior of the bank. Any market failure, inefficiency, or anticompetitive conduct among banks is likely to impose more severe costs throughout the economy – in terms of both allocated efficiency and distributional fairness – than would similar defects in many other industries, and it becomes particularly important to understand the causes and consequences of competition in the banking industry (Shaffer, 2004). Thus, need for parsimonious dealings and fairness it all banks with regards to its givers: depositors and investors. As a market failure or anticompetitive conduct may exacerbate a systemic reaction of bank runs.

This paper uses two measures to proxy for market structure in the banking industry. The first is a structural measure known as the Panzar-Rosse H statistics. The latter is known as the concentration ratio. The concentration ratio measures the ratio of the total asset of the largest three banks relatively to the total banks in the financial industry of that country, where countries with larger total asset ratio are in the hands of the three top banks, then such country would be suggested to behave more within the confine of a bank based market than a market based.

Panzar-Rosse H Statistics:Created by Panzar and Rosse as a framework with which to asses banking market structure, the model uses bank level data to measure how a change in factor input prices is reflected in equilibrium revenues earned by bank. Equivocally, it can be restated as a model that determines equilibrium output and the equilibrium number of banks by maximizing profits at both the bank level and the industry level (Bikker & Haaf, 2002). In a state of perfect market competition, marginal cost and total revenues increase is proportional to input prices. However, in a monopolist market, an increase in factor input prices raises marginal costs but reduces output and total revenues (Chen & Liao, 2011).

Following (Bikker & Haaf, 2002; Chen & Liao, 2011), this paper uses the P-R H model to measure the degree of competition in the banking industry by estimating for each country, the following empirically reduced form of bank revenues:

ln ( π i ,t )=α+βln PF i ,t+γln PLi ,t+δln PK i , t+ηi ln LNASST i ,t+η2ln NONTAi ,t +η3 ln DPSF i ,t+η4 ln EQTAi ,t +η5 OI+εi , t

(3)Where t is the number of periods observed, and i is the total number of banks in a country in our data. Ln denotes the natural lo operator, π denotes bank’s interest revenues, PF stands for annual funding rate calculated as interest expense to total funds, PL denotes price of personnel expenses calculated as annual personnel expenses to total assets, PK designates the price of physical capital expenditure calculated as non-interest expenses to fixed assets, LNASST is the ratio of loans to total assets, NONTA is the ratio of non-earning asset to total asset, DPSF is the ratio of customer deposits to the sum of customer deposits and short term funding, EQTA is the ratio of equity to total asset, and OI is the ratio of income to total assets.

The model posits that banks use three input factors - deposits (PF), labor (PL), and physical capital (PK). These factors are shown in equation three and are controlled by four other inputs. The H statistics is calculated as the sum of the elasticities of a bank’s total revenue with respect to the factors, therefore, H statistics equals to the sum of the coefficients of the three factors.

Concentration Ratio (CR): This ratio describes the degree of banking competition within the banking sector. The indicator is quantified as the combined market share of the three biggest banks in terms of total assets from the financial development and structure database. A high value of CR can act as a barrier to entry for new firms (Williams, 2003), also, it can impede the growth of other firms within the industry, and it could lead of high information asymmetry, where earnings can be mismanaged against of wishes of their investors. Therefore, we posit that high concentration would positively impact earnings management as a highly concentrated commercial banking sector might result in lack of competitive pressure to attract savings and channel them efficiently to investors. A highly fragmented market might be evidence for undercapitalized banks (Beck, Demirgüç-Kunt, & Levine, 2000).

Bank Performance – Synonymous to other firms, the measures of performance include the traditional measures of performance – ROA and ROE. Recently, financial experts have considered other measures like the net interest margin (NIM) and other economic and market based measures of performance. The most commonly used performance measures is the ROE as shown in scholarly works of (Berger, Clarke, Cull, Klapper, & Udell, 2005; Bonin, Hasan, & Wachtel, 2005). In this paper we use a statistical approach known as principal component analysis - PCA to create a standardize measures of bank performance, as recently ROE and other measures of performance have become suspicious and view with disdain. A 2010 report published by the European Central Bank suggested that ROE report can be misleading, be manipulated, or provide wrong incentive as they are influenced by quite strong seasonal factors. Thus, European Central Bank suggested a comprehensive performance analysis framework that goes beyond a particular indicator, one that encompasses the quality of assets, funding capacity, and the risk associated with the production of value would be preferred.

To develop such a comprehensive and robust performance measure that encompasses the three areas of performance – quality of asset, funding capacity, and risk. We apply the principal component analysis (PCA) technique to construct a performance value for the banks in our pooled regression. PCA method is more meaningful to use than a straightforward averaging of all the indicators of performance, as PCAs allow us to derive a robust weighted average of all indicators. In this paper, we use twenty three known ratios to generate a performance score covering asset quality, capital requirement, operation ratios, and liquidity ratios, all extracted from Bankscope. Appendix A documents the ratios and their implications to banks.

IV Data and Summary Statistics

Scholars generally tend to eliminate emerging and frontier countries from data analyzed in their works due to missing data points. We used data collected from Bureau van Dijk - BvD database. BvD is housed within the Wharton Research Data Services – WRDS and provides four information databases: Amadeus, Bank-scope, Isis, and Osiris. Here, we use Bank-scope to extrapolate our datasets for banks from seven emerging and frontier markets. The seven countries are Brazil, China, India, Mexico, Nigeria, Russia, and South Africa. The choice to focus on these seven countries where based on the following - data for these countries are available, their banks seems to be greatly involved in their financial markets, and indicators suggest a transition from bank to market based. Our data range is from 1997 to 2009. We also use (Beck, et al., 2000) financial structure database updated to the year 2009 to extrapolate bank concentration rates in each country.

Initially, our datasets consisted of active and non-active banks within each country, thus, generating over four hundred banks with corresponding duplicate and triplicate information on each bank for the aforementioned countries. We eliminate all non-active banks, duplicates and triplicates to arrive at a total of 173 banks for the countries with India having a higher share of sixty-one banks and Mexico having the least share of nine banks. Furthermore, we distinguish between local and foreign banks in this analysis by eliminating all foreign banks in each of the seven countries that may hold a large stake in the economy. Hence, all the banks that are included in our dataset have their financial records available for the year range. We eliminate banks that do not have available data beginning in 1997 as such the bank size was reduced to 121 banks.

Table 1Summary Statistics – Concentration is extracted from (Beck, et al., 2000). H Statistics is calculated using equation 3. Performance is a construct generated by principal component analysis (PCA) method after combining performance score covering asset quality, capital requirement, operation ratios, and liquidity ratios. RLLA and RLLP are the discretionary component of loan loss allowance and loan loss provision from equation 1 and 2. Asset is total asset of bank in our dataset.

  Concentration

H Statistics Performance RLLA RLLP

           H Statistics -0.1118        Performance 0.1621 -0.005      

RLLA -0.0444 0.0061 -0.0727    RLLP 0.0357 0.0156 -0.0117 0.1019  Asset 0.0699 -0.1105 -0.0163 -0.0814 -0.0162

Before proceeding to generate regression results, table 1 shows the correlation matrix between each indicator. As this paper is geared to understanding the relationship and effects of earnings management, banking structures, and bank performance, in table 1 the matrix points to the effect that none of the indicators are highly collinear. Using equation 1 and 2, we regress loan loss allowance and loan loss provision on the suggested indicators, and then we extract the residual which represents the discretionary aspect of the firm’s total accruals. Primarily, we would expect the collinear relationship between both managed discretionary properties to be high as shown in (Adams, et al., 2009) but for the seven countries, there seem to be discrepancies between the properties and behavior of both managed earnings indicators. Equation 3 is used to generate the H Statistics in the regression, and the principal component analysis (PCA) technique is used to produce a weighted scale for performance of banks in our dataset. The PCA method generated nine constructs for the twenty-four ratios of banks performance. We summed and averaged the nine constructs together to generate our performance scale after the PCA analysis was completed.

Table 2Descriptive Statistics – Concentration is extracted from (Beck, et al., 2000). H Statistics is calculated using equation 3. Performance is a construct generated by principal component analysis (PCA) method after combining performance score covering asset quality, capital requirement, operation ratios, and liquidity ratios. RLLA and RLLP are the discretionary component of loan loss allowance and loan loss provision from equation 1 and 2. Asset is total asset of bank in our dataset. The e after the values for each variable represents the numbers of zero before (-) or after (+) the values shown.

QuantilesVariable Mean Std.Dev. Min .25 .50 .75 Max

Concentration 0.4908 0.20 0.12 0.34 0.39 0.66 1H Statistics 2600.33 9594.62 0.17 1.12 1.61 2.69 40086

Performance -0.00 0.56 -2.88 -0.27 -0.12 0.11 4.06RLLA 0.00 6.10 -20.46 -1.85 -0.69 0.69 113.19RLLP 0.00 56.49 -624.82 -10.83 -2.44 7.71 681.25Asset 516,001 1,264,454 1.71 2309 105,408 406,526 1.30e+07

The variability of the data for the seven countries in this paper is shown in table 2. Table 2 provides evidence that some countries have a very high concentration ratio in regards to their top three banks as evidence with a maximum value of 1. Our dataset discover that the H statistics output affirms to the imbalance described by (Chen & Liao, 2011). (Chen & Liao, 2011) asserted that in a perfect market competition, marginal cost and total revenues increase is proportional to input prices. However, in a monopolist market, an increase in factor input prices raises marginal costs but reduces output and total revenues. From the performance construct, the descriptive statistics indicates that some banks are underperforming while others have a high performance rate. As noted by (Hasan & Wall, 2004), the division of loan loss accounting into two groups

suggest that the allowance and provision group have different variance, with the provision group having the highest.

IV Empirical Results

Regression Results –

The scope of this paper is to analyze the effect of banks earnings management and bank structure on the performance of banks. As such in our result section, we run the following regression model:

Performance=f (Bank Structure ,Earnings Management ,Control ,Country Dummies)(4)

where the expected level of performance can be influenced by the independent variables bank structure and earnings management. Bank structure is captured in two forms – bank concentration and the P-R H Statistics. Earnings management is captured in two forms and is inputted as the residuals from loan loss provisions and loan loss allowance regression indicated as equation 1 and 2. Control and dummy variable are included in the regression to affirm that the significance of each independent variable is certain for the countries to which their local banks data were collected. Our control variable is total assets of banks to capture the size effect, and our dummy variables are country dummies. We report the regression results as models in Table 3.

Table 3Ordinary least square regression of country dummies, total asset, discretionary loan loss provisions, discretionary loan loss allowance, and banking structure measures from bank performance. In all models the explained variable is bank performance. In Model 1 and 2, P-R H statistics a variable measuring banking market structure on the basis of a change in factor is used. Model 3 and 4 uses the Bank concentration ratio based on the percentage of the total assets held by the top three banks. The discretionary loan loss variables RLLA for discretionary loan loss allowance and RLLP for discretionary loan loss provision are included in separate models – were Model 1 and 3 show inclusion of RLLA and Model 2 and 4 show inclusion of RLLP. We report the beta coefficients and standard error of each variable with significance at: *** p<0.01, ** p<0.05, * p<0.1

  Model 1 Model 2 Model 3 Model 4  coef se coef se coef se coef se

H Statistics -0.000 0.000 -0.000 0.000        Concentration         -0.493*** 0.172 -0.528*** 0.171

RLLA     -0.010*** 0.002     -0.010*** 0.002RLLP -0.000 0.000     -0.000 0.000    Asset 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000

Dummy CHINA -0.277*** 0.061 -0.310*** 0.061 -0.178** 0.070 -0.205*** 0.069Dummy INDIA -0.522*** 0.045 -0.548*** 0.045 -0.612*** 0.054 -0.644*** 0.054

Dummy MEXICO -0.454*** 0.111 -0.464*** 0.111 -0.393*** 0.113 -0.399*** 0.112Dummy NIGERIA -0.271*** 0.054 -0.284*** 0.054 -0.356*** 0.053 -0.376*** 0.053Dummy RUSSIA -0.334** 0.165 -0.316* 0.164 -0.469*** 0.171 -0.460*** 0.170Dummy SOUTH

AFRICA -0.175*** 0.053 -0.203*** 0.053 -0.012 0.078 -0.028 0.078

               _cons 0.333*** 0.039 0.355*** 0.039 0.592*** 0.098 0.632*** 0.098

N 1577 1577 1577 1577R-squared 0.103 0.113 0.107 0.118

Adjusted R-squared 0.098 0.108 0.102 0.113

The ordinary least square regression results in Table 3 highlights the linear effect of the dependent variable on the explanatory and conditional variables. As banking market structure and earnings management are captured by two variables each, we show four models to pontificate on the effect of either measures of banking structure and earnings management. H Statistics which is a proxy for change in input factors on determining the revenues of banks in a dense or less concentrated market was not statistically significant in models 1 and 2. Bank concentration ratio – Concentration is statistically significant in Models 3 and 4 and retained its directionality effects, thus as bank performance ratios show positive or growth in value the level of concentration will wither. Like the H Statistics, discretionary loan loss provision (DLLP) which proxy for earnings manipulation from the income statement were not statistically significant in models 1 and 3, but discretionary loan loss allowance (DLLA) a proxy for the value of earnings management from banks balance sheet were statistically significant.

On significance, the country dummy variables were statistically significant in models except for South African banks that were not statistically significant in models 3 and 4. Overall, their significance posits a significant difference between each country banks when compared to Brazil which was the first dummy not included in all models. All significance had a negative directionality indicating that banks in Brazil performed higher compared to the banks in the remaining six countries. From table 3, all statistically significant results had negative directionality, thus intimating that improvement in bank performance by these variables can be achieved when bank managers in these countries reduce the rate of financial data manipulation and when the banking sector of the financial industry in each country are not oligopolized by few banks.

Robustness Check –

Here we consider many propositions on incidents that may influence the statistical significance of the main independent variables to the explained. We perform two tests. First, we perform a quantile regression analysis to help us answer the question if banking market structure and earnings management influence performance differently for the selected countries at low, average, or higher performing banks. Second, we investigate for endogeneity bias as well as causation effect by performing a generalized method of moment (GMM) regression to mitigate any form of bias with our models. Specially, we instrument all variables except the dummy by taking the lags of each variable.

Since quantile regression models the relation between a set of predictor variables and specific percentiles of the response variable, here, we publish and compare the OLS results in model 4 of table 3 with the quantile regression results for median, seventy-five, and twenty-five percentile. The results are published in table 4.

In the linear models table published as table 2, the regression coefficient represents the change in the response variable produced by a one unit change in the explanatory variable associated with the coefficient. The quantile regression parameter estimates the change in a specified quantile of the explained variable produced by a unit change in the explanatory variables, thus allowing us to compare how percentiles of performance may be more or less affected by certain characteristics than other percentiles. In table 4, we have five models, the OLS representing

model 4 from table 3. The rest models in table 4 are quantile regressions with the last being an inter-quantile regression between Q75 and Q25.

Table 4Quantile regression of country dummies, total asset, discretionary loan loss allowance, and banking structure measures from bank performance. In all models the explained variable is bank performance. The OLS model is the same result like model 4 in table 2. Q50 to Q25 shows the median, upper percentile and lower percentile regression respectively. Interval model is the inter-quantile regression between upper and lower percentile (.75 - .25). RLLA is the discretionary loan loss allowance. Control variables are Asset which is total asset for banks and country dummy variables. We report the beta coefficients with significance at: *** p<0.01, ** p<0.05, * p<0.1

  Explained Variable: Performance

  OLS Q50 Q75 Q25 IntervalConcentration -0.528*** -0.400*** -0.503*** -0.309*** -0.193

DLLA -0.010*** -0.001 -0.006** -0.000 -0.005*Asset 0.000 -0.000 -0.000*** -0.000 -0.000***

Dummy CHINA -0.205*** -0.294*** -0.422*** -0.175*** -0.247**Dummy INDIA -0.644*** -0.474*** -0.792*** -0.343*** -0.448***

Dummy MEXICO -0.399*** -0.210*** -0.518*** -0.244*** -0.274***Dummy NIGERIA -0.376*** -0.254*** -0.482*** -0.144*** -0.338***Dummy RUSSIA -0.460*** -0.402*** -0.693*** -0.191** -0.503***

Dummy SOUTH AFRICA -0.028 -0.173*** -0.274*** -0.167*** -0.107_cons 0.632*** 0.410*** 0.900*** 0.125*** 0.775***

The interpretation of table 4 is based mainly on our two independent variables. According to the OLS model, the mean performance of banks to bank concentration ratio is 0.528 lower than that of banks with higher concentration ratio. The quantile regression results indicate that the effect of bank concentration ratio larger negative impacts for banks on the upper percentile. The 75th percentile of performing banks had a concentration ratio of 0.503 lower than performing banks at lower percentile. We do not publish the standard errors here, but we use bootstrapping method in the quantile regression method to lessen any normality bias following (Hao & Naiman, 2007; Koenker & Hallock, 2001) which noted that both results are robust although bootstrap method should be the most preferred and more practical. Although the discretionary loan loss allowance were statistically significant in most models in table 4, the difference in magnitude was not much comparing the median, higher, and lower quantile regression to the linear model.

In our GMM regression, we perform a simultaneous equation regression where performance, bank concentration ratio, and loan loss allowance appear both as dependent variables and as regressors in the other two equations. We do not deviate from the included variables and patterns in the regression models in this paper, thus our regression models are still the same. By performing a GMM regression we attempt to control for fixed effects and to address endogeneity bias following (MacKay & Phillips, 2005; Whited, 1992). Specially, we use year to year changes rather than regular firm fixed effects to enable us to use once and twice lagged levels of the same variables as instruments. The GMM results are published as table 5.

The regression models are:Performance=f (Bank Structure ,Earnings Management ,Control ,Country Dummies)

Bank Structure=f (Performance ,Earnings Management ,Control ,Country Dummies)

Earnings Managemen=f (Bank Structure , Performance ,Control , Country Dummies)

Regardless of estimation methods in this paper, we find that bank concentration ratio and DLLA have an inverse significant to performance. Likewise, the other two models in table 5 also show a negative effect in their respective models. We find that changes in bank performance, bank concentration ratio, and bank earnings management respective to the seven countries in this paper are inversely related. Following (Maksimovic & Zechner, 1991) which motivates our use of simultaneous equation methods, our results confirm that this approach is justified as bank concentration ratio and discretionary loan loss allowance explain bank performance, and bank performance and discretionary loan loss allowance explain bank concentration, but simultaneous equation does not aide in explaining discretionary loan loss allowance by bank concentration and bank performance ratio.

Table 5GMM regression output with dependent variables as Performance, Concentration, and Discretionary Loan Loss Allowance. We instrument all variables except for dummy variables and total asset by their first and second lag levels. We report the beta coefficients with significance at: *** p<0.01, ** p<0.05, * p<0.1

  Performance Concentration DLLA  coef se coef se Coef seConcentration -0.760** 0.297     -3.582 3.973DLLA -0.011* 0.006 -0.002** 0.001    Performance     -0.015* 0.008 -0.797 0.522Asset 0.000 0.000 -0.000 0.000 -0.000* 0.000Dummy CHINA -0.152* 0.079 0.193*** 0.016 -2.192*** 0.633Dummy INDIA -0.698*** 0.084 -0.192*** 0.015 -3.054** 1.513Dummy MEXICO -0.375*** 0.070 0.116*** 0.019 -0.237 0.665Dummy NIGERIA -0.409*** 0.067 -0.127*** 0.014 -1.563 1.249Dummy RUSSIA -0.525*** 0.126 -0.274*** 0.039 1.649 2.494Dummy SOUTH AFRICA 0.031 0.105 0.326*** 0.015 -1.243 1.028             _cons 0.758*** 0.174 0.531*** 0.014 3.702 2.754N 1575 1575 1575Centered R Squared 0.118 0.856 0.044

V Conclusion

The focus of this paper is to investigate three ramifications that surround managed earnings by banks in emerging and frontier markets. These effects are: first, if earnings are managed by emerging market banks. Second, what is the incentive to manage earnings by these banks? Lastly, what bias does the degree to manage earnings have with performance and its structure across countries? To this end, we find that financial performance of banks in emerging and frontier markets are not impervious to discretionary management of funds by firms. Most importantly, as these countries transit from bank based to market based financial systems, the role of banks are still heightened by the diversified roles and agent of change which they are. This delicate position is greater for banks in the frontier and emerging markets were banks are central source of loanable and bankable funds, but as the market opens, banks are also perceived as firms with strong reputation as such, their stocks are invested into in larger proportions than other sub-industries. The opportunities which these banks tend to have may induce their managers to manipulate their financial records and possibly look for avenues to limit the entrant of new banks.

Here, we focus mainly on local banks to investigate what effects do earnings management and market concentration has on its performance. Our investigation find that earnings management as proxy by loan loss allowance and market structure as proxy by bank concentration ratio were inversely statistically significant on their effects on bank performance. All things equal, suggesting that bank performance is improved by the withering of oligopolistic structure of banks and the reduction of managed earnings by these banks. Our results also hold after running other robustness check regressions like the percentile regression to find what influence does manage earnings have on a bank’s performance for banks at lower or upper echelon. Also, a generalized method of moment tests is performed to address any bias and effect relationships.

Although the results here a conclusive and robustly they all hold. Perhaps a further avenue of exploration would be to include culture variables towards attesting the degree of earnings management between different cultural orientations. Another expansion to this paper could be on the inclusion of more countries and the consideration of bank regulatory policies both locally and internationally. For instance, the capital requirement ratio set by the Basel Accord as the Tier 1, 2, and the recent Tier 3, how will these policies impact the discretionary behavior of banks and their performances may need to be explored while considering local and transnational banks.

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