THE ANALYSIS OF FRAUD HEXAGON TOWARDS EARNINGS MANAGEMENT

Duffin¹, Deva Djohan ²

Institut Bisnis Informasi Teknologi dan Bisnis, Medan

[email protected], [email protected]

		Abstract
Received:	05-03-2022	Introduction: The COVID-19 pandemic has emerged and is still raging around the world and as can be seen from the severity of the COVID-19 pandemic, it has led to rampant fraud committed by many corporations, especially in Indonesia. According to a survey conducted by RSM Indonesia (2020), 80% of respondents stated that in the midst of the pandemic, fraud committed in Indonesia was increasing in their own organizations. Although the least common type of fraud is financial statement fraud, the losses incurred are quite large. The most cases occurred in the banking sector in 2020. The formulation of the research problem that the author wrote was to examine more deeply the impact of the hexagon fraud factor on financial statement fraud determined by discretionary accruals. Objective: This study aims to determine the effect and correlation of all hexagon fraud proxies on financial statement fraud using discretionary accruals in the banking sector listed on the Indonesia Stock Exchange (IDX) from 2018-2020. Methods: The research design is a quantitative design with descriptive and correlational methods, especially multiple linear regression analysis. Results: The findings of this study are that financial stability and the nature of the industry and rationalization have a positive and significant effect on discretionary accruals. Meanwhile, personal financial needs, financial targets, capabilities, and collusion have a negative and insignificant effect on discretionary accruals. In addition, external pressure, effective monitoring and ego/arrogance have a positive and insignificant effect on discretionary accruals. Conclusion: The conclusion of this research is as follows: 1. Stimulus as measured by financial stability, opportunity as measured by nature of industry, and rationalization as measured by total accrual to total assets ratio has significant impact towards earnings management in banking sector of Indonesia Stock Exchange.
Accepted:	05-03-2022
Published:	20-03-2022
Keywords:	Discretionary Accrual; Fraud Hexagons; Financial Statement; Earnings Management

Corresponding Author: Duffin

E-mail: [email protected]

PENDAHULUAN

The pandemic of COVID-19 has emerged and still keep on raging around the world. It has been one of the worst pandemics that are still happening and affecting all sectors in the world, nonetheless Indonesia. The recession that had been predicted earlier really happened at the third quarter of 2020, where the economic growth was -3,49%. It happened because many corporations laid-off the employees to cut their operational costs, while some could not survive and went bankrupt. This led to low consumption rate of the society, even with the help of the government�s stimulus checks.

The severity of COVID-19 pandemic led to the increase of frauds conducted by many corporations especially in Indonesia. According to the survey conducted by RSM Indonesia (2020), 80% of the respondents stated that in the midst of pandemic, the fraud conducted in Indonesia increased in their own organizations. Fraud as defined by Association of Fraud Examiner (2018), is �any intentional act or omission designed to deceive others, resulting in the victim suffering a loss and/or the perpetrator achieving a gain.� There are three types of fraud, which are asset misappropriation, corruption, and financial statements fraud.

During 2020, the financial statement fraud is the least happened fraud, which is only 3% from the total cases happened. Although it is the least fraud happened, it was the fraud which led to the most losses. According to the Report to the Nation of Asia Pacific Region (2020), the financial statement fraud in median resulted in $3,000,000 loss, with banking sector is reported as the most cases in 2020. This is mostly driven by the lack of supervising because of the Work from Home (WFH) policy suggested by the government to stop the spread of COVID-19 (RSM Indonesia, 2020).

As mentioned by Cressey Donald in his research called �Other People�s Money: A Study in the Social Psychology of Embezzlement�, there are three factors that may encourage a fraud to be conducted, which are pressure, opportunity and rationalization. Donald�s theory is known as Fraud Triangle. In 2004, D. T. Wolfe and Hermanson added one more factor which is called as capacity, creating the Fraud Diamond theory. According to them, a fraud may not happen without a capable person in an organization. The next development of this theory is in 2011, developed by Jonathan Marks, a partner of Crowe Horwath. He then replaced the capacity factor into competence, which has the similar definition with the capacity in Fraud Diamond and added one more factor which is arrogance (ego), defined as the superiority on the rights that he/she has and is not bounded by the control and policy of the organization. By adding one more factor, he invented the Fraud Pentagon theory.

In 2018, George L Vousinas refined the Fraud Pentagon theory by adding one more component which is collusion. It is the teamwork of several individuals with external parties of an organization or among the employees of an organization to conduct a fraud. The model nowadays is known as S.C.C.O.R.E model (Stimulus, Competence, Collusion, Opportunity, Rationalization, and Ego Model).

The financial statement fraud can be conducted with the manipulation of accounting records, which may lead to earnings management using discretionary accrual. This is to boost the revenue of a company, as RSM Indonesia reported that 56% of the respondents believed that the company�s revenues are affected during this pandemic of COVID-19. Companies were trying to show the picture of �stable� company to the investor, as they are afraid of disinvestment by the investor which might lead to panic selling. To achieve that condition, companies use the earnings management.

Earnings management is the act of management of a company to manipulate the income. It is used by management to achieve several objectives (Gao & Gao, 2016). The earnings management itself is divided into accrual basis earnings management and real basis earnings management. Accrual basis earnings management can be conducted by modifying the accruals that still can be changed, known as discretionary accrual. Management still can manipulate the amount of the accruals such as depreciation methods, provision for doubtful debts policy, and inventory valuation method (Trisnawati, Sasongko, & Fauzi, 2015).

According to (Sari & Nugroho, 2021), stimulus factor in terms of financial stability, external pressure, and financial target does not impact significantly towards financial statement fraud, only personal financial need that has significant impact. The capability and rationalization factors do not have significant impact, while ego (arrogance) and collusion factors have significant impact towards financial statement fraud. For opportunity factor, there are mixed results, as nature of industry has significant impact, while effective monitoring does not have significant impact towards financial statement fraud.

(Kusumosari & Solikhah, 2021) stated that stimulus factor in the form of financial target and financial stability has significant impact towards financial statement fraud, meanwhile stimulus factor in the form of external pressure has no significant impact towards financial statement fraud. Collusion, opportunity, and ego (arrogance) has significant impact towards financial statement fraud. For competence factor, it does not have any significant impact. The rationalization factor which used the external auditor proxy and total accruals gave mixed results. The external auditor has no significant impact, meanwhile the total accruals have significant impact towards financial statement fraud.

The mixed results challenge the writers to investigate more of the fraud hexagon factors impact towards financial statement fraud that is determined by discretionary accrual, especially in banking sector in Indonesia.

According to (Birt et al., 2008), �Earnings management refers to managers� use of accounting discretion via accounting policy choices and/or estimations to portray a desired level of earnings in a particular reporting period.� There are several motives of why management may conduct earnings management, such as contractual motivations, political motivations, taxation motivations, changes of CEO, initial public offerings, and to communicate certain information to investors. In general, there are two types of earnings management, which are discretionary accrual and discretionary expenses. Discretionary accrual is the accruals that management can influence of, such as provision for doubtful debts, inventory valuation, and policies in loan agreement (Trisnawati et al., 2015). Meanwhile, discretionary expenses is the earnings management through company�s operational decisions (Rankin, Stanton, McGowan, Ferlauto, & Tilling, 2012). It affects either directly or indirectly towards cash flows.There are several forms of earnings management, which are taking a bath, income minimization, income maximization, and income smoothing.

According to (Johnstone-Zehms, Gramling, & Rittenberg, 2015), �Fraud is an intentional act involving the use of deception that results in a material misstatement of financial statements.� There are three types of fraud: misappropriation of assets, financial statement fraud, and corruption. Misappropriation of assets refers to the theft or misusage of organization�s assets. For example: cash thefts and inventory theft. After the cash or inventory is stolen, the financial record will be manipulated to conceal the fraud. The intentional manipulation of financial statements to misstate the real condition of an organization is called as financial statement fraud. It may take various forms such as manipulation or alteration of accounting records or supporting documents, misapplication of accounting principles, and omission of events, transactions, or other information. Meanwhile the typical definition of corruption encompasses the misuse or abuse of the office (whether public or corporate) for the sake of private self-interest (Zyglidopoulos, Fleming, & Rothenberg, 2009). The type of corruption that may happen comprises of illegal gratuities, bribery, economic extortion, and conflicts of interest.

METODE PENELITIAN

The population of this research is all of the companies of banking subsector listed in Indonesian Exchange (IDX) from year 2018 � 2020, with the total population of 43 companies. The type of the sampling technique used in this research is purposive sampling. The sample in this research is chosen by using these criteria:

1. The company is listed in IDX for three years consecutively from 2018 � 2020.

2. The company submits annual report and/or financial statements for three years consecutively from 2018 � 2020.

Table 1.

Sample Determination according to Criteria

No	Criteria	Total
1.	Companies of banking subsector listed in IDX for three years consecutively from 2018 � 2020	43
2.	Companies which do not submit annual report and/or financial statements for three years consecutively from 2018 � 2020	(14)
Total of Sample		29
Total of Data Observation (3 years)		87

This research will use multiple linear regression, specifically using the ordinary least square (OLS) regression. To ensure that the regression model is free from classical assumption problems, some of the classical assumption tests will be conducted:

a. Normality test, conducted using Kolmogorov-Smirnov test.

b. Heteroscedasticity test, conducted using Glejser test.

c. Multicollinearity test, conducted using tolerance and variation inflation factor (VIF) test.

d. Autocorrelation test, conducted using runs test.

This research will use 5% significance value and the coefficient of determination will be determined with Adjusted R Square percentage. For hypothesis test, the tests that will be conducted is t-test which is to show the significance of an independent variable towards dependent variable. The regression model is shown below:

DA = β0 +β1FS + β2PFN + β3EP + β4FT + β5CAP + β6COL + β7NOI + β8EMO + β9RAZ + β10AROO

+ e

HASIL DAN PEMBAHASAN

There are 46 banks that are listed in Indonesian Stock Exchange (IDX) by the end of year 2020. After selecting by the criteria stated above, there are 17 banks that are not eligible to be used as the research sample. Thus, there are 29 banks that are used as research sample. This research used 3 years of data from year 2018 � 2020, therefore the total data used in the research is 87 data. However, because of the normality test, there are 12 data considered as outliers and are removed from the processing stage. There are 75 data used to conduct the research analysis as explained below.

1. Descriptive Statistics

The descriptive analysis for this research showed the analysis of each variable�s mean, standard deviation, minimum value, and maximum value. The table below showed each variable�s descriptive statistics analysis:

Table 3.

Descriptive Statistics

		FS	PFN	EP	FT	CAP	COL	NOI	EMO	RAZ	ARRO	DA
N	Valid	75	75	75	75	75	75	75	75	75	75	75
	Missing	0	0	0	0	0	0	0	0	0	0	0
Mean		,0616	,000976017785	,7771	,0124	,320	,347	2,2509	,5735	-,0037	2,707	,0059486
Std. Deviation		,09755	,0034878343501	,16614	,02003	,4696	,4791	8,84180	,10719	,07762	,6733	,02829890
Minimum		-,25	,0000000000	,16	-,05	,0	,0	-3,14	,33	-,26	1,0	-,05745
Maximum		,38	,0170678636	,93	,09	1,0	1,0	68,25	1,00	,21	4,0	,07882

Table 3 showed that:

1. For financial stability (FS) variable, the average is 0.0616. The standard deviation of the variable is 0.09755. The minimum value is -0.25, derived from Bank KB Bukopin, Tbk. The maximum value is 0.38, derived from Bank Ina Perdana, Tbk.

2. For personal financial need (PFN) variable, the average is 0.000976017785. The standard deviation of the variable is 0.0034878343501. The minimum value is 0.0000000000, derived from 30 data of 14 banks. The maximum value is 0.0170678636, derived from Bank of India Indonesia, Tbk.

3. For external pressure (EP) variable, the average is 0.7771. The standard deviation of the variable is 0.16614. The minimum value is 0.16, derived from Bank BTPN Syariah, Tbk. The maximum value is 0.93, derived from Bank Pembangunan Daerah Banten, Tbk.

4. For financial target (FT) variable, the average is 0.0124. The standard deviation of the variable is 0.02003. The minimum value is -0.05, derived from Bank Harda Internasional, Tbk. The maximum value is 0.09, derived from Bank BTPN Syariah, Tbk.

5. For capability (CAP) variable, the average is 0.320. The standard deviation of the variable is 0.4696. The minimum value is 0.0, derived from 51 data of 28 banks. The maximum value is 1.0, derived from 24 data of 17 banks.

6. For collusion (COL) variable, the average is 0.347. The standard deviation of the variable is 0.4791. The minimum value is 0.0, derived from 49 data of 18 banks. The maximum value is 1.0, derived from 26 data of 10 banks

7. For nature of industry (NOI) variable, the average is 2,2509. The standard deviation of the variable is 8,84180. The minimum value is -3.14, derived from Bank of India Indonesia, Tbk. The maximum value is 68.25, derived from Bank KB Bukopin, Tbk.

8. For effective monitoring (EMO) variable, the average is 0.5735. The standard deviation of the variable is 0.10719. The minimum value is 0.33, derived from Bank Neo Commerce, Tbk. The maximum value is 1.00, derived from Bank Pembangunan Daerah Jawa Timur, Tbk and Bank Pembangunan Daerah Banten, Tbk.

9. For rationalization (RAZ) variable, the average is -0.0037. The standard deviation of the variable is 0.07762. The minimum value is -0.26, derived from Bank Ina Perdana, Tbk. The maximum value is 0.21, derived from Bank of India Indonesia, Tbk.

10. For ego/arrogance (ARRO) variable, the average is 2.707. The standard deviation of the variable is 0.6733. The minimum value is 1.0, derived from Bank Harda Internasional, Tbk and Bank Neo Commerce, Tbk. The maximum value is 4.0, derived from Bank Negara Indonesia (Persero), Tbk, Bank Maybank Indonesia, Tbk, Bank Woori Saudara Indonesia 1906, Tbk, and Bank MNC Internasional, Tbk.

11. For discretionary accrual (DA) variable, the average is 0.0059486. The standard deviation of the variable is 0.02829890. The minimum value is -0.05745, derived from Bank KB Bukopin, Tbk. The maximum value is 0.07882, derived from Bank Oke Indonesia, Tbk.

2. Classical Assumption Tests

a. Normality Test

Normality test aims to know the residual distribution from a regression model. If it is normally distributed, then the model can be used in the regression analysis. Normality test can be done using Kolmogorov-Smirnov test, where the residuals are determined as normally distributed when the significance is more than 0.05. Below is the table showing the result of Kolmogorov-Smirnov test using the 87 data:

Table 4

Normality Test using Kolmogorov-Smirnov Test before Data Screening

One-Sample Kolmogorov-Smirnov Test

		Unstandardized Residual
N		87
Normal Parameters^a,b	Mean	,0000000
	Std. Deviation	,03086930
Most Extreme Differences	Absolute	,126
	Positive	,120
	Negative	-,126
Test Statistic		,126
Asymp. Sig. (2-tailed)		,002^c

a. Test distribution is Normal.

b. Calculated from data.

c. Lilliefors Significance Correction.

It can be seen from the table above that the significance is 0.002, which implies that the data is not normally distributed. One of the causes is that there are outliers, which distorted the residuals to be not normally distributed. This can be solved by data screening, in which trimming the outliers out from the research data. During the data screening, there are 12 data considered as outliers, therefore, left 75 data that fulfilled the normality assumption. The normality test is conducted once again, and the table below showed the result:

Table 5

Normality Test using Kolmogorov-Smirnov Test before Data Screening

One-Sample Kolmogorov-Smirnov Test

		Unstandardized Residual
N		75
Normal Parameters^a,b	Mean	,0015282
	Std. Deviation	,01139439
Most Extreme Differences	Absolute	,053
	Positive	,035
	Negative	-,053
Test Statistic		,053
Asymp. Sig. (2-tailed)		,200^c,d

a. Test distribution is Normal.

b. Calculated from data.

c. Lilliefors Significance Correction.

d. This is a lower bound of the true significance

����������� The table above showed that the significance is 0.200, which is more than 0.05. Thus, the residuals of the regression model are normally distributed, and the normality test is satisfied.

b. Heteroscedasticity Test

Heteroscedasticity test is done to know whether in the regression model, the variance are heterogenous or not. One of the tests is Glejser test, in which each independent variable is tested against the residuals which the values have been absolute. If the significance of each independent variable is more than 0.05, then there is no heteroscedasticity problem in the regression model. The table below showed the heteroscedasticity test using Glejser test.

Table 6

Heteroscedasticity Test using Glejser Test

Coefficients^a
Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	,007	,006		1,176	,244
	FS	-,004	,010	-,071	-,400	,690
	PFN	-,048	,212	-,030	-,225	,823
	EP	,002	,005	,070	,462	,646
	FT	-,024	,048	-,086	-,499	,619
	CAP	,001	,002	,076	,552	,583
	COL	1,753E-5	,002	,002	,011	,991
	NOI	-8,137E-5	,000	-,129	-,909	,367
	EMO	-,001	,007	-,028	-,213	,832
	RAZ	-,007	,010	-,095	-,664	,509
	ARRO	,000	,001	,024	,180	,858
a. Dependent Variable: ABS_RES3

As shown in the table above, the significance of each independent variable is more than 0.05, thus there is no heteroscedasticity problem in this regression model.

c. Multicollinearity Test

Multicollinearity test is to test whether the independent variables are related or not. Multicollinearity can be tested by using VIF and tolerance test. If VIF value of each independent variable is less than 10 and the tolerance value is more than 0.1, then it can be concluded that the variables are free from multicollinearity. The table below showed the multicollinearity test of each independent variable using VIF value and tolerance value:

Table 7.

Multicollinearity Test using VIF value and tolerance value

Model		Collinearity Statistics
		Tolerance	VIF
1	(Constant)
	FS	,476	2,100
	PFN	,848	1,180
	EP	,643	1,555
	FT	,504	1,985
	CAP	,782	1,279
	COL	,861	1,161
	NOI	,741	1,349
	EMO	,849	1,178
	RAZ	,726	1,377
	ARRO	,828	1,207

As it can be seen from the table above, there are no independent variables that have VIF value more than 10 and tolerance value less than 0.1. Therefore, the regression model is free from multicollinearity problem.

d. Autocorrelation Test

Autocorrelation test is to test whether time-series data have autocorrelation problem, which is a non-independent error pattern that violates regression assumption where each independent error is from previous period. Autocorrelation test can be done by using runs test. If the significance of the test is more than 0.05, thus there is no autocorrelation problem in the data tested. The table below showed the table of autocorrelation test using runs test:

Table 8.

Autocorrelation Test using Runs Test

	Unstandardized Residual
Test Value^a	,00045
Cases < Test Value	37
Cases >= Test Value	38
Total Cases	75
Number of Runs	41
Z	,583
Asymp. Sig. (2-tailed)	,560

a. Median

It can be seen from the table above, the significance of the test is 0.560, which is more than 0.05. Thus, there is no autocorrelation problem in the research data.

3. Hypothesis Test

In linear regression analysis, t-test is used to show the partial impact of each independent variable towards the dependent variable. The t-test is used to test H1 to H10 of this research. With 95% degree of confidence, the hypothesis will be rejected if tcount is less than ttable and accepted if tcount is more than the ttable. Below is the result of t-test for each variable:

Table 9.

t-Test Result

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.
		B	Std. Error	Beta
1	(Constant)	-,029	,011		-2,612	,011
	FS	,281	,019	,970	15,127	,000
	PFN	-,282	,390	-,035	-,724	,472
	EP	,015	,009	,088	1,598	,115
	FT	-,089	,088	-,063	-1,009	,317
	CAP	-,006	,003	-,094	-1,873	,066
	COL	-,002	,003	-,032	-,662	,511
	NOI	,0004	,0002	,116	2,247	,028
	EMO	,012	,013	,045	,935	,353
	RAZ	,300	,019	,822	15,830	,000
	ARRO	,001	,002	,032	,655	,515

The table 9 showed that partial influence test (t-test) for financial stability, nature of industry, and rationalization has significant impact towards earnings management, as the significance are less than 0.05. Meanwhile, the partial influence test (t-test) for personal financial needs, external pressure, financial target, capability, collusion, effective monitoring, and ego/arrogance has no significant impact towards earnings management, as the significance are more than 0.05.

Based on table 9, the regression equation is as follow:

DA = 0,281 FS � 0.282 PFN + 0.015 EP � 0.089 FT � 0.006 CAP � 0.002 COL + 0.0004 NOI + 0.012 EMO + 0.300 RAZ + 0.001 ARRO

4. Analysis

a. Financial stability has significant impact on earnings management.

Stimulus measured by financial stability towards discretionary accrual showed the tcount is 15.127, meanwhile the ttable is 1.99773, therefore, tcount is more than ttable, with significance of 0.000, which is less than 0.05, and the coefficient of regression is positive. It can be concluded that financial stability is positively and significantly impact discretionary accrual.Thus, H1 is accepted. This result supports the finding by (Sihombing & Rahardjo, 2014), in which they concluded that financial stability is significant towards earnings management. Financial stability is very important to assure the investors that a company is growing well, including banks. Investors will always hope that a company grows well that can be seen through the growth of assets. This puts pressure for the banks to grow their assets continuously. To satisfy investors� expectation, a bank will tend to manipulate its financial statements to fulfill its investors� expectation. Not only the investors that must be satisfied, but also the customers. Customers tend to deposit in a bank with high amount of assets, as they will feel safe to deposit their money in the bank.

b. Personal financial needs has significant impact on earnings management.

Stimulus measured by personal financial need towards discretionary accrual showed the tcount is - 0.724, meanwhile the ttable is -1.99773, therefore, -tcount is more than -ttable, with significance of 0.472, which is more than 0.05, and the coefficient of regression is negative. It can be concluded that personal financial need is negatively and not significantly impact discretionary accrual. Thus, H2 is rejected. This result supports the finding by (Aprilia, 2017), in which she concluded that personal financial needs has no significant impact towards earnings management. A bank has a complex structure, as a bank will have hundreds of branches among a country and even overseas. Thus, its assets will be more than enough for its operation to generate revenues to pay the salary of its employees. This will discourage the executives of the bank to manipulate its financial statements as their compensation are enough to fulfill their personal needs. Not only that, the risk to manipulate is high as it will hit the reputation of the bank and the executives might lose their positions and never to be accepted in the banking industry anymore.

c. External pressure has significant impact on earnings management.

Stimulus measured by external pressure towards discretionary accrual showed the tcount is 1.598, meanwhile the ttable is 1.99773, therefore, tcount is less than ttable, with significance of 0.115, which is more than 0.05, and the coefficient of regression is positive. It can be concluded that external pressure is positively and not significantly impact discretionary accrual. Thus, H3 is rejected. This result supports the finding of (Kusumosari & Solikhah, 2021), in which external pressure has no significant impact towards earnings management. The external pressure specifically from the shareholders as the principal of a company, usually motivate the management as agent to manipulate its financial statement to fulfill the target or expectation of the shareholders. This is to raise enough sources of funds to grow the company. In banking sector, such search of funds is easy to be done as banks can lend from other banks or even central banks. Thus, management will not manipulate the financial statements just to fulfill the expectation of the shareholders.

d. Financial target has significant impact on earnings management.

Stimulus measured by financial target towards discretionary accrual showed the tcount is -1.009, meanwhile the ttable is -1.99773, therefore, -tcount is more than -ttable, with significance of 0.317, which is more than 0.05, and the coefficient of regression is negative. It can be concluded that financial target is negatively and not significantly impact discretionary accrual. Thus, H4 is rejected. This result supports the finding of (Firda Ratnasari, 2019), in which they found that financial target has no significant impact towards earnings management. Financial target measured by return on asset (ROA) is commonly used by investors to measure the asset effectiveness in generating net income of a company. Therefore, the management is pressured on using its assets effectively or they simply hold on acquiring new assets as it will affect the ROA. However, in banking industry, ROA is not a very useful measurement. It is because in banking industry, the more useful asset is human capital, which can come up with innovations in this digital era, thus it will develop new innovative products that will satisfy customers. Not only that, the trained human capital will carry out high quality services to customers, which will lead to higher revenues for the banks. Therefore, the management does not need to manipulate financial statements to achieve such financial targets from investors.

e. Capability has significant impact on earnings management.

Capability measured by change of board of directors towards discretionary accrual showed the tcount is -1.873, meanwhile the ttable is -1.99773, therefore, -tcount is more than -ttable, with significance of 0.066, which is more than 0.05, and the coefficient of regression is negative. It can be concluded that capability is negatively and not significantly impact discretionary accrual. Thus, H5 is rejected. This result supports the finding of (Estu Ratnasari & Solikhah, 2019), in which they found that capability has no significant impact towards earnings management. Change of board of directors is purposely done to assign competent and visionary directors to achieve the vision and mission of a company, including the banks. The new director will focus on the new plan to achieve the vision of the bank, which will help the bank to grow more branches in its operating country, even to overseas. The old �habit� of new director such as doing �big bath� is not a necessary manipulation to be done as it will shrink the bank and will slow down the growth of the bank. Thus, new director will not try to conduct earnings management.

f. Collusion has significant impact on earnings management.

Collusion measured by cooperation with government towards discretionary accrual showed the tcount is -0.662, meanwhile the ttable is -1.99773, therefore, -tcount is more than -ttable, with significance of 0.511, which is more than 0.05, and the coefficient of regression is negative. It can be concluded that collusion is negatively and not significantly impact discretionary accrual. Thus, H6 is rejected. This result supports the findings of (Tsakilla, 2021), in which collusion has no significant impact towards earnings management. Cooperation with government will happen when a company performs well and is stable. Companies which strive for cooperation with government will try to window-dressing its financial statement to ensure the government and win the bid. Such thing will not happen in banking industry as government itself has several state-owned banks, besides the central bank. Private-owned banks will not conduct such earnings management as it will affect the reputation of the bank once discovered by the Financial Services Authority of Indonesia (Otoritas Jasa Keuangan), where the banks can be suspended from operation. State-owned banks also do not have to conduct earnings management as they will always in cooperation with government. A collusion conducted by the state- owned banks with other parties will be discovered by the Corruption Eradication Commission of Indonesia (Komisi Pemberantasan Korupsi) and public will not trust the bank anymore. Thus, earnings management will not be conducted with the motivation of collusion.

g. Nature of industry has significant impact on earnings management.

Opportunity measured by nature of industry towards discretionary accrual showed the tcount is 2.247, meanwhile the ttable is 1.99773, therefore, tcount is more than ttable, with significance of 0.028, which is less than 0.05, and the coefficient of regression is positive. It can be concluded that nature of industry is positively and significantly impact discretionary accrual. Thus, H7 is accepted. This result supports the finding of (Sari & Nugroho, 2021), in which nature of industry has significant impact towards earnings management. From the descriptive statistics, it shows that in average, the receivables is increased every year. This might be the indication of earnings management as if the receivables is increased, it is from the uncollected revenues. Uncollected revenues are caused by lenient credit revenues given to the customers, in terms of banking sector. The marketing department will work so hard to earn revenues to achieve their own targets, however, they do not really care about the collection of the revenues. Thus, they might even manipulate the revenues earned by them to show that they achieve their targets.

h. Effective monitoring has significant impact on earnings management.

Opportunity measured by effective monitoring towards discretionary accrual showed the tcount is 0.935, meanwhile the ttable is 1.99773, therefore, tcount is less than ttable, with significance of 0.353, which is more than 0.05, and the coefficient of regression is positive. It can be concluded that effective monitoring is positively and not significantly impact discretionary accrual. Thus, H8 is rejected. This result supports the finding of (Estu Ratnasari & Solikhah, 2019), in which effective monitoring has no significant impact towards earnings management. According to Financial Services Authority rules of No. 33/PJOK.04/2014, there is a requirement that independent commissioner composition in a corporation must be at least 30% of the total commissioners in the corporation. Therefore, companies including banking sector will just fulfill the requirements. Even if the independent commissioners do their jobs in monitoring the companies, their earnings management findings will just be ignored by the board of directors and the non-independent commissioners. Thus, even the monitoring is effective, the earnings management will still be conducted.

i. Rationalization has significant impact on earnings management.

Rationalization measured by total accrual to total assets ratio towards discretionary accrual showed the tcount is 15.830, meanwhile the ttable is 1.99773, therefore, tcount is more than ttable, with significance of 0.000, which is less than 0.05, and the coefficient of regression is positive. It can be concluded that rationalization is positively and significantly impact discretionary accrual. Thus, H9 is accepted. This result supports the finding of (Kusumosari & Solikhah, 2021), in which rationalization ha significant impact towards earnings management. The management of a company will always try to pleasure the shareholders as principal of the company. They will try to achieve the targets/expectations set by the shareholders. When the operation result is not that satisfying, the management will try to manipulate the financial statements. Their rationalization is that the purpose is the increase the shareholders� wealth. Thus, they justify their faulty acts for the purpose of the shareholders� interest.

j. Ego (arrogance) has significant impact on earnings management.

Ego/arrogance measured by number of CEO pictures in annual report towards discretionary accrual showed the tcount is 0.655, meanwhile the ttable is 1.99773, therefore, tcount is less than ttable, with significance of 0.515, which is more than 0.05, and the coefficient of regression is positive. It can be concluded that ego/arrogance is positively and not significantly impact discretionary accrual. Thus, H10 is rejected. This result supports the finding of (Hidayah & Saptarini, 2019), in which ego/arrogance has no significant impact towards earnings management. The number of CEO pictures in annual report will not affect the occurrence of earnings management. No matter how many the pictures, it will not indicate the arrogance of the CEO, as the pictures in annual report will follow the template for the annual report. Only few of the banks that have annual reports that shows CEO pictures more or less than the average amount of CEO pictures. Thus, the arrogance shown by the number of CEO pictures in annual report will not affect whether earnings management is conducted or not.

KESIMPULAN

The conclusion of this research is as follows: 1. Stimulus as measured by financial stability, opportunity as measured by nature of industry, and rationalization as measured by total accrual to total assets ratio has significant impact towards earnings management in banking sector of Indonesian Stock Exchange. 2. Stimulus as measured by personal financial needs, external pressure, and financial targets, capability as measured by change of BOD, collusion as measured by cooperation with government, rationalization as measured by effective monitoring and ego/arrogance as measured by number of CEO pictures in annual report, has no significant impact towards earnings management in banking sector of Indonesian Stock Exchange.

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