Debt Policy, Reputable External Auditor, Company Size, And Profit Quality

−The financial report describes company performance before the users: public investors and creditors. Moreover, this report must deliver superior profits to display the beneficial information. For this reason, this study attempts to know its determinants by utilizing causing factors, like debt policy and reputable external auditors as the primary variable and company size as the control. Besides, this study sets the companies in the retail trade industry in the Indonesian capital market from 2013 until 2018 as the population, where a simple random sampling technique is utilized to grab the samples. After that, this study employs the regression model and related statistical features to check the intended hypotheses. After examining them statistically, this study reveals that debt policy and the company size affect profit quality positively. However, reputable external auditors do not influence this quality.


INTRODUCTION
Fundamentally, financial statements are a tool for companies to influence the decision of their investors in the capital market to buy, sell, and keep holding their stocks (Husnan, 2015).Moreover, the quality of these statements is reflected by reported profits (Shan, 2015).When a profits surprise happens, the market tends to react positively, and vice versa (Landsman & Maydew, 2002).For managers, profits validate their operating attainment in the company (Singh et al., 2016).Through profits, the company can expand its potential business, pay dividends to its stockholders (Gitman & Zutter, 2015), borrow money from banks, issue bonds in the capital market (Husnan, 2015), and attract new investors domestically and globally (Nguyen & Nguyen, 2020).
Because these informed earnings relate to the firm's value in the capital market, managers' incentive to manage these earnings exists (Lin et al., 2006).The managers in a company with high debt will apply the accounting technique that can elevate profits (Watts & Zimmerman, 1990).This statement is proven by the study by Ramadan (2015), Houqe et al. (2017), Nalarreason et al. (2019), and Rahman et al. (2020) demonstrate that companies with high debt tend to manage their earnings; therefore, their earning quality decreases.Unfortunately, other related studies are unreliable in documenting this evidence.Rodríguez-Pérez and Van Hemmen (2010), Abid et al. (2018), Nariman and Ekadjaja (2018), Ngo andLe (2021), Trablesi (2021), and Kurniawan and Antonio (2022) report that the more debt, the lower the discretionary accrual or earning management level, causing an increase in earnings superiority.In their study, Ghosh and Moon (2010) find that profit quality is the quadratic function of debt.Meanwhile, Widayanti et al. (2014), Nasution and Jonnergård (2017), Almarayeh et al. (2020), Hamdan (2020), andTrang andMai (2021) show no association.
By referring to the issues presented, this study intends to investigate the influence of debt policy and reputable external auditors on profit quality by utilizing company size as the control variable and the retail trade companies listed in the Indonesian capital market as samples.According to Rahayu (2018), this retail sector is not potential anymore since society's lifestyle has changed.People no longer visit the malls for shopping.Instead, they come to meet and hang out with the others in these places.Therefore, many companies suffer losses, making earnings quality relevant to be investigated.
To make a strong relationship between debt policy and profit quality, we use the debt covenant theory proposed by Kalay (Kalay, 1982) and earning management concept offered by (Watts & Zimmerman, 1990) and Scoot (2015).Before borrowing money, the company, through managers, signs a contract.This contract protects the lender's interest from harmful actions, such as investing in risky projects (Kalay, 1982).If managers violate this contract, they will manage earnings by choosing the accounting methods elevating profits (Watts & Zimmerman, 1990) by transferring future profits to current ones (Scoot, 2015).Thus, profit quality decreases, as Ramadan (2015), Nalarreason et al. (2019), andRahman et al. (2020) verify.Suppose they obey the contract and do not manage the profits.In that case, the profit quality will increase, as demonstrated by Rodríguez-Pérez and Van Hemmen (2010), Abid et al. (2018), and Nariman and Ekadjaja (2018).
Furthermore, to explain the relationship between external auditors affiliated with the big four and profit quality, we use the concept from Francis and Yu (2009), stating that the big four auditors have a quality control system and specialists in auditing, financial, and tax accounting, and valuation.With these features, they can help their clients create qualified profits.This explanation is also supported by Kouaib and Jarboui (2014), Soliman and Ragab (2014), Khurnanto and Syafruddin (2015), Piyawiboon (2015), Houqe et al. (2017), Murniati et al. (2018), Rahman et al. (2020), andTrablesi (2021), demonstrates.
Theoretically, this study verifies one of two perspectives explaining the association between debt policy and profit quality and proves the effectiveness of external auditors affiliated with the big four in creating profit quality.Practically, this study is expected to ensure creditors and public investors that managers do not manage the profits.Besides, the selection of external auditors becomes an interesting problem to solve.

Fundamental Research Framework
This study is quantitatively designed.According to Hartono (2012), this design intends to prove the hypotheses statistically.In this context, we use the causal approach.To achieve this aim, we use the t-statistical test of the regression model.In shape, the model can be drawn in the first picture.

Figure 1. The research model
Furthermore, we formulate the first hypothesis into two parts: A and B. Part A is built on the idea that the managers unfollow the debt covenant and manage the firm's earnings.Meanwhile, Part B is created on the idea that the managers follow the debt covenant and do not manage these earnings.Therefore, the intended hypotheses are below: H1a: The debt policy affects profit quality negatively if the managers unfollow the debt covenant and manage the firm's earnings.H1b: The debt policy affects profit quality positively if the managers follow the debt covenant and do not manage the firm's earnings.Associated with the effectiveness of the external auditor affiliated with the big four to cut earnings management or elevate profit with quality, we declare the second hypothesis as follows.
H 2 : Reputable external auditor positively affects profit quality.

Research Variable Measurement
This study utilizes two types of variables.Firstly, the dependent, i.e., profit quality.Regarding profits quality measurement, we use cash flow from operation to earnings before the interest and taxes ratio, following Abdelghany (2005) and Murniati et al. (2018).According to Penman (2009), the higher this ratio, the lower the quality of the profits.Therefore, we inverse this ratio to yield a reasonable interpretation of the content of all research hypotheses.The second variables are independent, i.e., debt policy, reputable external auditor, and firm size.

Population And Sample
The population utilized is from the consistent retail trade companies on the Indonesia stock exchange from 2013 until 2018, i.e., 19.Moreover, the name and the number of the companies can be seen in Table 1.Furthermore, the total samples (n) representing the total population (N) taken are calculated by the Slovin formula by the fault margin (FM) of 10% in Suliyanto (2009)

Data Collection Technique
Mentioning Hartono (2012), we utilize the archival method to get secondary data.In this context, we use the information from the annual reports of the associated listed firms on the Indonesian stock exchange as the sample (see Table 1), the manuscripts published in the related journals,

Model To Analyze The Data
In this research, we employ a multiple regression model with pooling data based on the feature of the utilized variables.This model mixes the cross-sectional and time series data and uses the ordinary least square (Nachrowi & Usman, 2006).With this technique, the parameters (β) are expected to be the best, most linear, and unbiased.Because of this situation, the regression model needs to achieve the classical assumptions: normality of errors, the absence of heteroskedasticity, multicollinearity, and autocorrelation (Ghozali, 2016;Gujarati & Porter, 2009).Furthermore, the regression model is obtainable in the second equation.
To test β1, β2, and β3, we compare the probability of the t-statistic related to DAR, REA, and LN(TA) with a 5 percent as the standard significance level or 10% as the relaxed significance level.If this probability is below this level, the research hypothesis is accepted based on the regression coefficient sign.Conversely, if this probability is similar to or above this level, it is rejected: the independent variable does not partially influence the dependent one.

The Descriptive Statistics
Descriptive statistics are statistical measurements to describe the data (Hartono, 2012).To provide them, we use the IBM SPPS.The output contains the number of observations (N), minimum, maximum, average, and standard deviation (Ghozali, 2016).The total samples utilized in this research come from 16 retail companies in the Indonesian stock exchange with six years, 2013, 2014, 2015, 2016, 2017, and 2018, as the duration.Therefore, the observation number is 96.Moreover, the descriptive statistics: mean, standard deviation, maximum, and minimum based on 96 observations for these variables, i.e., profit quality, debt policy, reputable external auditors, and company size, are obtainable in Table 2.The inverse OCFOIR has a minimum of -500 and belongs to SKYB for 2015.The maximum is 72.46, which belongs to GLOB in 2015.Additionally, this OCFOIR inverse has an average and standard deviation of -3.9954 and 52.24879.The lowest DAR of 0.02 belongs to SKYB in 2017; however, the highest of 19.97 belongs to GLOB in 2018.Furthermore, the DAR has an average of 1.8101 and 4.07764 standard deviation.The dummy variable of REA has an average of 0.4792: the comparison of the big four external auditors and the non-big four is almost alike.Moreover, RIMO is the firm with the smallest natural logarithm of 8.53 in 2013.Conversely, AMRT is the firm with the most extensive natural logarithm of 16.91 in 2018.

The Correlation Matrix
The correlation determines the power of the relationship among research variables (Gujarati & Porter, 2009).The correlation used in this section is the Pearson coefficient, and the rule to interpret the value is explained by Sarwono (2006) as follows.Firstly, if the correlation is below 0.2, the association does not happen.Secondly, if the correlation is from 0.2 to 0.4 and 0.4 to 0.7, the association is weak and almost strong, respectively.Finally, if the correlation is from 0.7 to 0.9 and above 0.9, the association is strong and extremely strong, partially.Table 3 shows the correlation matrix between 1/OCFOIR and DAR of 0.333, 1/OCFOIR and LN(TA) of 0.170, and DAR and REA of 0.274.Because these values are between 0.20 and 0.40, the relationship is weak.Besides, the correlation between 1/OCFOIR and REA is 0.090, less than 0.2; hence, these variables are not associated.Meanwhile, the correlation of DAR with LN(TA) is 0.603, and REA with LN(TA) is 0.485.Because these values are between 0.40 and 0.70, as Sarwono (2006) demonstrates, the relationship is almost strong.that the correlation between the two variables is significant at 5% and 1% based on one-tailed.A negative sign shows direction, and the relational power can be interpreted based on the absolute value.Indeed, for a positive sign, the meaning can be directly made.

The Result Of The Classical Assumption Tests
Table 4 presents the examination results of classical assumptions.Firstly, the White heteroskedasticity test.It is shown by the probability of Chi-Square statistic for Observed R-squared of 0.864.This value exceeds the 5% significance level; the model is free from this matter.The second result is multicollinearity detection, exhibited by the variance inflation factor for DAR: 1.200, REA: 2.120, and LN(TA): 1.903.These values are below 10; thus, multicollinearity is not available.Thirdly, the autocorrelation based on the run test result is displayed, with the asymptotic significance of Z-statistic (2-tailed) of 0.884.Because this value exceeds the 5% significance level, the model is free from this matter based on the mode of residuals.

The assumptions
Statistical result Explanation Heteroskedasticity: White test ERROR^2 = f (DAR^2, REA^2, LN(TA)^2) The probability of Chi-Square statistic for Observed R-squared = 0.864 b) The regression model is free from heteroskedasticity.

Multicollinearity recognition
The variance inflation factor of DAR, REA, and LOG(TA) is 1.200, 2.120, and 1.903 a) The multicollinearity problem is not available.Autocorrelation: Runs test based on the mode of errors Asymptotic significance of Z-statistic (2tailed) = 0.884 a) No autocorrelation exists.
The last is the normality result, reflected by the probability of the JB-statistic of 0.000.Based on this information, the errors are not generally distributed because this value is lower than the 5% significance level (see Table 5).We allow this situation by holding the central limit theorem (see https://en.wikipedia.org/wiki/central_limit_theorem) because our total observations are extensive: 96.

Statistical result
Meaning The probability of the JB-statistic = 0.000 The normality of errors does not exist.

The Estimation Result Of The Regression Model
Once examining and solving the classical assumptions, the subsequent stage is the regression model estimation, where the result is reachable in Table 6.In this table, the probability of the t-statistic for a positive regression coefficient of DAR is 0.0964, and LN(TA) is 0.0362.Because the likelihood is lower than a 10% relaxed significance level, we approve the first and third hypotheses: debt policy and company size positively affect profit quality.Conversely, the positive sign of REA is statistically insignificant, demonstrated by the related probability of 0.9989, above the 10% relaxed significance level.Thus, external auditor quality does not affect profits quality.

Discussion
The testing result of the first hypothesis declares a positive influence of debt policy on profits quality.Therefore, we support part b of the first hypothesis (H1b).It indicates that the managers are truthful and do not take the opportunity to move profits to the current position when the firm uses more debt.Instead, they report earnings with quality.Hence, The testing result of the second hypothesis exhibits that reputable external auditors do not profit quality, although the sign is still positive.This situation occurs because the number of firms employing the auditors affiliated with the big four and non-big four is alike: seven (see Table 7 for their code).Also, the total companies switching auditors to be the big four are the same: two, i.e., GLOB and TRIO.
Table 7.The code of companies employing the big and non-big four external auditors

Description
The code Total companies The company as the user of the big four auditors AMRT, CSAP, ERAA, LPPF, MAPI, MIDI, and RALS

7
The company, as the user of the non-big-four auditors ACES, ECII, MPPA, RIMO, SKYB, SONA, and TELE 7 Without the relationship between the reputable external auditor and profit quality, this study confirms Widayanti et al. (2014) after researching 44 high-profile companies in the Indonesian capital market from 2009 to 2012.Furthermore, this study confirms Laily (2017) although using a different proxy of reputable audit quality: the number of clients of the external auditors when investigating 86 manufacturing enterprises listed on the Indonesian capital market in 2018.Also, this study affirms Nasution and Jonnergård (2017), employing nine hundred seventy-six firm-year observations from 2008 to 2013 in the Sweden stock market with big-four and non-big-four auditors as the proxy.Similarly, this evidence is confirmed by Abid et al. (2018) after investigating 183 firms listed on the Karachi Stock Exchange between 2009 and 2013, Almarayeh et al. (2020) with 251 non-financial firm-year observations in the Amman Stock Exchange from 2012 to 2016, Kurniawan and Antonio (2022) utilizing 104 industrial companies listed on the Indonesian capital market between 2017 and 2019 as their samples.
The position of the logarithm natural of total assets as the company size is statistically significant (see the probability of 0.0362 in Table 5).Therefore, it becomes a reasonable control variable with a positive sign in this regression model.It means that a big firm has satisfactory-structured bookkeeping and internal governance division and can hire specialists to improve the financial reporting process (Ibrahim & Abubakar, 2019).This situation is in line with Liu and Lu (2007), employing the random effect model with 5,977 consistently listed firm-year observations from 1999 to 2005 in China, Ghosh and Moon (2010) using the 8,240 firm-year observations in Compustat outside public and financial sectors in the United States between 1992 and 2004.Besides, this study supports Rodríguez-Pérez and Van Hemmen (2010), employing 192 non-financial and parental firms listed on the Spanish capital market from 1992 to 2022, Ghazali et al. (2015) applying 1,116 Malaysian public-listed companies-year data between 2010 and 2012.Likewise, Houqe et al. (2017) verify this proof by investigating 7,303 listed non-financial company-year observations from 1999 to 2009 in India.Also, this study is in line with Almarayeh et al. (2020) with 251 non-financial firm-year units in the Amman Stock Exchange from 2012 to 2016, Hamdan (2020) employing the 23 industrial firms from the Gulf Cooperation Council capital market between 2014 and 2018, Rahman et al. (2020) utilizing 656 firmyear observations from 2007 to 2014 in Malaysia.Similarly, Kurniawan and Antonio (2022) confirm this inclination when studying 104 manufacturing companies listed on the Indonesian capital market from 2017 to 2019.Overall, these nine studies indicate the bigger the firms, the lower propensity to manage profits, leading to profit superiority.

CONCLUSION
This study is designed to prove and analyze the influence of debt policy and reputable external auditors on profit quality by treating company size as the control variable.To achieve this destination, we employ data from the companies in the retail industry in the Indonesian capital market from 2013 until 2018, becoming population and sample.After investigating the data related to the hypotheses, we find that debt policy can enhance profits superiority because managers do not manage earnings.As a practical implication, because of a positive association between debt policy and profit quality, creditors do not need to worry that managers report immaterial earnings when the firms use more debt in their capital structure, especially in the retail industry in Indonesia.Hence, the banks can lend their money to the firms, and investors can purchase the issued bonds of the companies.By finding the fact declaring the profit quality of the firms audited by the big four and the non-big-four is alike, the companies had better choose the non-big-four auditors with lower fees.Theoretically, although confirming the previous studies with a positive sign for ,Houqe et al. (2017),Nasution and Jonnergård (2017),Abid et al. (2018),Hamdan (2020),Rahman et al. (2020),Ngo and Le (2021), Trablesi (2021), andKurniawan and Antonio (2022).The reputable external auditor (REA) is measured by the dummy variables showing the affiliation status of this auditor.If the company is audited by one of the big four accountants, we code one for the situation.If the non-big four accountants audit the company, we code zero for it.This measurement followsKouaib and Jarboui (2014), Soliman and Ragab (2014), Widayanti et al. (2014), Khurnanto and Syafruddin (2015), Piyawiboon (2015), Houqe et al. (2017), Laily (2017), Nasution and Jonnergård (2017), Abid et al. (2018), Murniati et al. (2018), Rahman et al. (2020), and Kurniawan and Antonio (2022).The natural logarithm of total assets reflects firm size in this research.This way denotes Liu and Lu (2007), Ghosh and Moon (2010), Rodríguez-Pérez and Van Hemmen (2010), Kouaib and Jarboui (2014), Soliman and Ragab (2014), Widayanti et al. (2014), Ghazali et al. (2015), Piyawiboon (2015), Ramadan (2015), Amos et al. (2016), Houqe et al. (2017), Nasution and Jonnergård (2017), Shankaraiah and Amiri (2017), Abid et al. (2018), Nariman and Ekadjaja (2018), Almarayeh et al. (2020), Hamdan (2020), Rahman et al. (2020), Ngo and Le (2021), Trablesi (2021), and Kurniawan and Antonio (2022).
according toGhosh and Moon (2010), they can help lenders predict solvency, liquidity, and insolvency risks.By this evidence, this study aligns with the positive tendency demonstrated by Rodríguez-Pérez and Van Hemmen (2010), utilizing the 192 non-financial and parental firms listed on the Spanish capital market from 1992 to 2022.Besides, this study confirms Abid et al. (2018), with the 183 Pakistani enterprises listed on the Karachi stock exchange between 2009 and 2013 as their samples, Nariman and Ekadjaja (2018), after employing 61 manufacturing companies listed on the Indonesian stock exchange from 2013 to 2016.Also, this study confirms Ngo and Le (2021) using 216 companies listed on the Vietnam stock market between 2015 and 2018, Trablesi (2021) with 56 non-financial companies listed on the Abu Dhabi Securities Exchange and Dubai Financial Market from 2011 to 2019 as the samples.Besides, Kurniawan and Antonio (2022) confirm this indication when investigating 104 industrial companies listed on the Indonesian capital market between 2017 and 2019.

Table 1 .
The name of the company becomes the population

Table 3 .
The Pearson Correlation Matrix Result

Table 6 .
The estimation result of regression model: The effect of debt policy, reputable external auditor, and company size on profits quality