A Case Study of Companies in Malaysia Relating to The Impact of Audit Quality on Earning Quality

The main task of an auditor is related to the company's income, savings and investment. In addition, the duties and responsibilities of an auditor are to examine financial records, write reports at the end of audit procedures that determine the level of accuracy and clarity that the organization takes into account, ensuring that financial reports follow accounting principles. This is required for the firms to improve operations, build trust among stakeholders, and promote accountability. The current study's goal is to measure the influence of audit quality on earnings quality. A quantitative research methodology will be utilized to examine secondary data from 435 Malaysian companies listed on Bursa Malaysia's Main Market. The data collected is for the period from 2016 to 2021. The E-Views application will be utilized for the statistical investigations since it includes a range of built-in specification procedures to investigate panel data sets. The study's results show that audit quality have effect on earning quality, with audit quality having a beneficial effect on earning quality. The results show how complicated the idea of earning quality is and how dependent it is on a variety of elements, including business characteristics, accounting procedures, controls and governance, auditors, goals, and external elements.


Introduction
It is commonly acknowledged that auditing is essential to the growth and improvement of both the global economy and commercial organizations.Auditors are supposed to offer unbiased assessments of whether businesses are managed ethically and successfully to achieve the desired objectives in a business setting.The function of an auditor may be broken down into five categories: goals, reporting lines, profession, and relationships with other people.This is necessary for the businesses to enhance operations, establish stakeholder confidence, and establish responsibility (Kruk et al., 2018).According to Al-Khaddash et al (2013), the stakeholders need to be assured that the reported statistics are accurately measured and fairly presented.As a result, auditors must increase their level of expertise and independence, make use of the proper resources, who perform each of their jobs by providing consultancy services.
A increasing body of research on the relationship between audit quality and earnings management contends that using better auditors is associated with less earnings management.The size of the audit company may be used as a proxy for audit quality since larger firms are assumed to be more independent and better qualified.In other words, as the audit firm's size increases, the quality of the audit improves (Ha and Nguyen, 2020).To promote high-quality audits, an institutional environment must be set up and followed.Similarly, Tsipouridou and Spathis (2012) discovered that there is no discernible difference between the earnings management methods of enterprises audited by the Big Four auditors and those of non-Big Four auditors in the context of Greece cases.According to AlKhaddash et al (2013), the size of the audit company has little impact on the choice of an auditor.The authors assert that there may be a number of variables that influence audit quality, highlighting that audit quality is not just influenced by audit firm size.Almomani (2015) conducted research on the relationship between audit quality and earning quality, concluding that there is a strong connection between the two.Also, some studies have examined the effectiveness of audits on earning managements.According to research by Nawaiseh (2016), the audit quality, which is measured using audit tenure, audit fees, and major multinational auditing firms as proxy, has a considerable impact on earning management.The objective of the current study is to quantify the impact of audit quality on earnings quality.

Literature Review Agency Theory
Agency theory is a branch of economics that studies the relationship between principals (such as shareholders) and agents (such as managers) in an organization.The theory explains how conflicts of interest can arise between the two parties and proposes ways to align their interests to achieve the organization's goals.The principal-agent relationship exists when one party (the principal) hires another party (the agent) to act on their behalf.The agent is expected to make decisions and take actions that serve the principal's interests.However, the agent may also have their own interests, such as maximizing their own wealth or job security, which may conflict with the principal's interests (Villiers and Kelly, 2017).
Agency theory proposes that the principal and agent can mitigate these conflicts of interest by establishing contracts, incentive mechanisms, monitoring and control systems, and other governance mechanisms.These mechanisms can align the agent's interests with those of the principal and reduce the risk of opportunistic behavior by the agent.Agency theory has been applied to a wide range of industries, including finance, law, and healthcare.It has also been used to study corporate governance and executive compensation (Hong et al., 2016).
In the context of earnings quality, agency theory suggests that managers may have incentives to manipulate reported earnings to meet financial targets or to conceal poor performance.Therefore, higher agency costs (i.e., costs associated with agency problems) can lead to lower earnings quality.In the context of audit quality, agency theory suggests that auditors may have incentives to please their clients or to maintain ongoing relationships with them, which could compromise their independence and objectivity.Therefore, higher agency costs can lead to lower audit quality.Overall, agency theory can help explain why earnings quality and audit quality may be influenced by factors such as agency costs, conflicts of interest, and information asymmetries.Understanding these relationships can help companies and auditors identify and mitigate potential risks to financial reporting quality.

Earnings Quality
Earnings quality refers to the degree to which a company's reported earnings accurately reflect its financial performance.High-quality earnings provide useful and reliable information to investors and other stakeholders, while low-quality earnings may be misleading or manipulated through accounting practices.There are various measures of earnings quality, including earnings persistence, predictability, and smoothness, as well as metrics that capture the degree of earnings management and accounting conservatism.For example, earnings management involves using accounting methods to manipulate reported earnings in a way that benefits the company or its managers.This can include adjusting accruals or other accounting estimates to smooth out earnings, recognize revenue prematurely, or defer expenses to future periods (Dechow & Dichev, 2002).
Earnings management is receiving more attention as manipulative techniques that enable managers to accomplish reporting targets under certain economic conditions.Earnings management occurs when managers use judgment in financial reporting and in structuring transactions to alter financial reports to either mislead some stakeholders about the company's underlying economic performance or to influence contractual outcomes that depend on reported accounting practices.A fair and lawful management decision-making and reporting process that aims to achieve and reveal steady and predictable financial results is often referred to as earning management (Mushiirah et al., 2018).
Earnings management as deviations from standard operating procedures that happen as a result of managerial involvement in the reporting process, namely through accounting estimations and methodologies as well as operational choices.Managers control earnings for three reasons: capitalmarket motive, contractual motivation, and regulatory motivation.This might be accomplished, for instance, by speeding up sales, changing shipping plans, and deferring expenses for maintenance and R&D.Managers participate in earnings management to maximize their pay, such as bonus plans and stock options, or to minimize the cost of capital or political expenses.In this respect, minimizing capital expenditures or political expenses will help businesses, whilst maximizing remuneration will benefit management at the expense of shareholders.Using high-quality auditors can help alleviate some of the negative effects of these income management methods.High-quality auditors often provide greater audit quality as well as more reliable and credible information (Sulaiman et al., 2018).
The concept of earnings quality has important implications for financial reporting and analysis, as well as for corporate governance and investor decision-making.A company with high-quality earnings is more likely to be viewed positively by investors and analysts, while a company with low-quality earnings may face skepticism or even legal or regulatory scrutiny.Auditors play a key role in ensuring earnings quality, as they are responsible for reviewing a company's financial statements and assessing the reliability of its accounting practices.However, auditors may also be subject to conflicts of interest or other pressures that can compromise their independence and objectivity.As such, the quality of audit work is also an important factor in ensuring the accuracy and integrity of reported earnings.
Earnings quality is a measure of the reliability and usefulness of a company's reported earnings.It is related to but distinct from earnings management, which involves the manipulation of earnings through accounting practices.The concept of earnings quality is important for financial reporting and analysis, corporate governance, and investor decisionmaking, and is closely tied to the quality of audit work and the effectiveness of corporate governance mechanisms.So it can be say that higher earning quality will lower earning management, which is opposite relationship.

Audit Quality
According to DeAngelo (1981), audit quality is "the market judged joint chance that a specific auditor would both detect and disclose a violation in a client's system" (p.186).This basically divides the two components of audit quality into the chance of finding inaccuracies and the appropriateness of reporting those findings (DeAngelo, 1981).Work by DeAngelo (1981) laid the groundwork for audit-quality research.Prior to DeAngelo's (1981) research, audit quality was never specifically identified, and it was typically seen unreasonable to make a distinction between the top eight Certified Public Accounting (CPA) companies and all other CPA firms as long as professional norms and qualifications were upheld.In reality, the American Institute of Certified Public Accountants (AICPA) claimed that the size of the auditor should not be taken into consideration when choosing an auditor, supporting the claim that audit quality is unaffected by auditor size.DeAngelo (1981), in contrast, was the first to assert that bigger audit companies offer a greater degree of audit quality.His research on audit fees supports this claim, contending that there is less incentive for the auditor to act opportunistically when the auditor's clientele is larger and the client's share of total clients is smaller.As a result, there is a higher likelihood that the perceived audit quality will improve.The notion of audit quality is rephrased into two elements in more recent literature by Knechel (2016), building on DeAngelo's (1981) earlier work: 1) auditor knowledge (probability of uncovering misstatements), and 2) auditor independence (likelihood of disclosing the discovered misstatements).As both characteristics are seen to be favorably connected to audit quality, these two variables are often handled as different audit components.

Audit Quality and Earnings Quality
Auditing plays a crucial function in lowering the expenses of intermediaries between management and the stakeholders in businesses.By confirming the accuracy of financial reporting, auditors are expected to help users of financial statements.Unfortunately, this significant role may not actually play out as expected.Managers have a favorable environment and a chance to participate in earnings management due to the knowledge asymmetry between management and shareholders.Moreover, managers have selfinterested motivations to use this knowledge asymmetry to window-dress reported revenues.In order to save agency expenses, shareholders must thus rely on contracting and supervision.In this regard, an excellent auditor can serve as a useful monitoring device (Al-Baidhani, 2014).
Audit quality refers to the level of assurance provided by the auditor that the financial statements are free from material misstatements, while earnings quality refers to the reliability and relevance of reported earnings as an indicator of the company's financial performance and position.Research has shown that audit quality is positively associated with earnings quality.A high-quality audit can increase the reliability of financial statements and reduce the likelihood of earnings manipulation or misstatement (Francis et al., 1999).Auditors play a critical role in detecting and reporting financial statement errors and fraud, which can have a significant impact on earnings quality.
Moreover, auditors also provide a level of credibility to financial statements that can enhance the quality of earnings by increasing investor confidence in the accuracy of reported earnings.This, in turn, can lead to improved access to capital markets and reduced cost of capital However, it is important to note that audit quality alone is not sufficient to ensure high earnings quality.Companies also need to adopt strong internal control systems and adhere to generally accepted accounting principles (GAAP) to ensure the accuracy and reliability of their financial statements.So, it can be say, there is a strong positive relationship between audit quality and earnings quality.High-quality audits can enhance the reliability and credibility of financial statements, which can lead to improved earnings quality and increased investor confidence.
Previous studies have demonstrated a favorable correlation between enhancing the Earnings Quality and audit quality (Balsam et al., 2003;Francis et al., 1999;Mcdougal, 2011).According to Abdul Rashid et al (2012), the Audit Quality is a crucial issue that managers in businesses should consider while engaging in Initial Public Offering (IPO) activities.Initial improvements in the level of shareholder trust in the capital market toward the operations of the firm were facilitated by the presence of the Audit Quality function in the business prospectus.This description supports the following hypothesis: H1: There is an effect of audit quality on earnings quality

Research Methodology
The data used were obtained from the annual reports, and the study's population included Malaysian listed companies on Bursa Malaysia's Main Market.The objectives of this study will be accomplished via a quantitative research approach.A variety of industries will be represented in the sample of listed firms chosen for this study from 2016 to 2021 that were listed on Bursa Malaysia's Main Market are included in the population.These industries can be grouped under commerce, business services, real estate, industrial items, consumer goods, and technology.
Businesses with missing variable data for any period from 2016 to 2021 and incomplete online annual reports for any year from 2016 to 2021 reduce the data.The final sample is thus made up of 435 data.In this study, secondary data will be used, and all statistical analyses will be done with the help of the E-Views program, which has a variety of built-in specification approaches to look at the panel data sets.
Several methods, such as pooled OLS, fixed effects, and random effects, are used to estimate regression models with panel data.Each method has a unique set of presumptions and limitations.
The technique you select will be determined by the precise study goals and the data's structure, thus it's critical to thoroughly consider the assumptions of each strategy before making a decision.In this inquiry, the Lagrange Multiplier Test, Hausman Test, and Chow Test were used.
1 The residual values obtained from the regression analysis between the financial performance component and total accruals, according to this model, show the quality of the profits.Last but not least, using the model's projected residual values from the regression analysis, accrual Earnings quality was substituted among the selected firms.

Diagnostic Test Normality Test
A normality test is a statistical analysis that assesses whether a given dataset is normally distributed or not.Normality is an assumption of many statistical analyses, and if a dataset is not normally distributed, certain statistical tests may not be appropriate (Sekaran and Bougie, 2016).

Linearity Test
A linearity test is a statistical analysis that tests whether there is a linear relationship between two variables.Linear relationships are often assumed in regression analyses, so a linearity test can help determine whether the regression model is appropriate (Montgomery et al., 2012).

Multicollinearity Test
Multicollinearity refers to the situation where two or more independent variables in a regression model are highly correlated with each other.Multicollinearity can cause problems in regression analyses, such as making it difficult to identify the unique contribution of each independent variable.A multicollinearity test is a statistical analysis that identifies whether multicollinearity is present in the data (Sekaran & Bougie, 2016).The variance inflation factor (VIF) can be used to identify multicollinearity.

Heteroskedasticity Test
Heteroskedasticity refers to the situation where the variance of the residuals in a regression model is not constant across all values of the independent variable(s).Heteroskedasticity can cause problems in regression analyses, such as making it difficult to estimate the standard errors of the regression coefficients.A heteroskedasticity test is a statistical analysis that identifies whether heteroskedasticity is present in the data (Sekaran & Bougie, 2016).The null hypothesis is rejected if the regression model clearly exhibits heteroscedasticity and the heteroscedasticity probability (p-value) is greater than 0.05.

Autocorrelation Test
Autocorrelation refers to the situation where the residuals in a regression model are correlated with each other.Autocorrelation can cause problems in regression analyses, such as making it difficult to estimate the standard errors of the regression coefficients.An autocorrelation test is a statistical analysis that identifies whether autocorrelation is present in the data.The null hypothesis is rejected if the regression model clearly exhibits heteroscedasticity and the heteroscedasticity probability (p-value) is greater than 0.05 (Sekaran & Bougie, 2016).

Regression Analysis
Regression analysis is a statistical analysis that examines the relationship between a dependent variable and one or more independent variables.The goal of regression analysis is to identify the best-fitting line (or curve) that describes the relationship between the variables (Sekaran & Bougie, 2016).The null hypothesis is rejected if the regression model clearly exhibits heteroscedasticity and the heteroscedasticity probability (p-value) is greater than 0.05.In Table 2, the descriptive statistics for the independent variable are shown based on the minimum, maximum, average, and standard deviation.The average value of the audit quality variable is 2.823162.These findings fall short of the average value of 0.0775 found in research by (Setiawan et al., 2019).This is due to the research's usage of various samples from various nations.In particular, the telecoms and retail service businesses registered on the Indonesia Stock Exchange between 2012 and 2017 were utilized as a sample of public companies in this study.

Normality Test
The investigation's findings, which are represented in Figure 1, offer support for the assertion that the dataset does not flagrantly deviate from the normalcy assumption.

Linearity Test
The linearity test could suggest other models that would be more appropriate if a linear regression model is not suited for the data.In Table 3, the linearity test is displayed.
Table 3 The Linearity Test Variable The Linearity Test EQ 0.752 Residuals 0.046 The dependent variable (residuals) has a standard deviation of 0.046 and the independent variable (EQ) has a standard deviation of 0.752, as shown in table.3above.The potential that EQ and the residuals have a linear relationship is increased by the fact that the standard deviation of the residuals is relatively low.The VIF values for audit quality are 1.312601 < 10, which makes it evident from table 4 that there was no multicollinearity.

Heteroscedasticity Test
A significant p-value for the heteroscedasticity test in Table 5 reveals that there is heteroscedasticity.When the p-value for the heteroscedasticity test is less than 0.05, the evidence is strongly in favor of the null hypothesis, which states that the variances of the residuals are the same across all levels of the independent variables.This shows that residual variability is not constant over the range of predictor variables, which may affect the accuracy and dependability of the regression model's predictions.

Autocorrelation Test
Table 6 was used to make this determination.As the value of DW = 1.914051 is more than 1.67 (du) and less than 4.-1.67, it was concluded that regression model does not statistically exhibit any autocorrelation (4-du).As a result, the estimations of the regression coefficients can be skewed, and the validity of the hypothesis tests might be compromised.As a result, it is essential to eliminate any autocorrelation in the residuals before analyzing the results of a regression analysis.The data above are displayed on Table 7. and the p value for the Lagrange Multiplier Test in this study is 0.0427 0.005.The results suggest that random effects models ought to be utilized as a result.9 shows the significance value for the Chow test was 0.1969 > 0.005, showing that the model chosen has a frequent impact (OLS)

Regression Analysis Results: Relationship between Audit Quality and Earning Quality
In this study using Kothari (2005) for the measurement of earning management.Earning quality is opposite of earning management.The p-value, which indicates a significant influence or not, was used to assess the significance of the research hypothesis in this study since it only looks at whether factors can have an impact or not.So that the discussion and justification at the table will be achieved.The model is deemed to fit as a result since the Probability F value is 0.000006 < 0.05.The independent variables in the model can explain 6.04% of the total variance of the dependent variable, according to the modified R square of 0.060394.This suggests that the variables used in the research have not been able to sufficiently explain how the factors that have a substantial impact on them relate to one another.According to Table 10, there is a strong and favorable correlation between earning quality and audit quality.It is clear from the substantial positive coefficient and t-value (t= 2.904451, p=0.03663 0.05) that the degree of audit quality and earning potential are highly correlated.Since the hypothesis accepted, Audit Quality has an impact on Earning Quality at the 5% level.The outcome is consistent with DeFond and Zhang's (2014) finding that businesses with highquality audits have higher-quality profits.In order to give a better degree of scrutiny and confidence over the company's financial reporting, high-quality auditors are more likely to detect and remedy accounting mistakes and inconsistencies.
There is evidence that audit quality and earnings quality are positively correlated, according to a different research by (Al-ahdal & Hashim, 2021).High-quality auditors are more willing to question management's accounting judgments and are better able to identify and disclose financial anomalies, which can result in more accurate and trustworthy financial reporting.Moreover, Badia et al (2020) discovered that audit quality influences earnings quality favorably.This is because competent auditors are more likely to offer a higher level of comfort regarding the company's financial reporting and are better equipped to recognize and remedy accounting problems.

Discussion
The association between earning quality and audit quality is favorable and strong.It is clear from the substantial positive coefficient and t-value (t= 2.904451, p=0.03663 0.05) that the degree of audit quality and earning potential are highly correlated.So H1 was accepted, Audit Quality has an impact on Earning Quality at the 5% level.According to the findings of DeFond and Zhang (2014); Al-ahdal & Hashim (2021); Badia et al (2020), audit quality positively affects earnings quality.According to some, audit quality directly influences earnings quality.The financial industry has shown a great deal of interest in the complex issue of earnings quality.Depending on whether they provide evidence for the causes or effects of the earnings quality proxy they evaluate, studies on earnings quality are categorized into one of two groups in the literature review.In the determinants studies by Dechow et al., the dependent variable is the earnings quality proxy 2010.The following are some examples of the commercial variables that affect the quality of earnings: (1) Firm characteristics, including size, growth and investment, debt, and company performance.(2) Standards that are based on principles as opposed to rules in regards to accounting practices, financial statement categorization, interim reporting, and accounting processes.(3) Governance and controls.(4) Auditors.(5) Goals based on capital market incentives, such as capital raising, and profitability.(6) Outside factors in the analysis of the consequences articles (Beneish and Vargus 2002), which look at the effect of earnings quality on an outcome, the earnings quality proxy acts as the independent variable.They include the possibility of litigation, audit views, market valuations, real operations, disclosure, executive compensation, labor market outcomes, cost of equity capital for the company, cost of debt for the company, and the reliability of analyst projections.The findings indicate that there is a strong and positive association between earning quality and audit quality.The study also emphasizes the fact that profits quality is a complex idea that is impacted by many different things, such as corporate characteristics, accounting practices, controls and governance, accountants, objectives, and external components.The study's findings offer crucial information to practitioners and policymakers aiming to raise the standard of financial reporting and increase the dependability of financial data.Overall, the study emphasizes the significance of audit quality in fostering earnings quality and emphasizes the need for more research to understand the intricate links between these two crucial concepts.

Conclusion
In conclusion, the study provides evidence of a favorable and strong association between earning quality and audit quality, with audit quality having a positive impact on earning quality.The findings highlight the complexity of the concept of earnings quality and its dependence on various factors such as firm characteristics, accounting practices, controls and governance, auditors, objectives, and external components.The study's results have practical implications for practitioners and policymakers seeking to improve financial reporting standards and enhance the reliability of financial data.The study also calls for further research to explore the intricate links between earning quality and audit quality, emphasizing the importance of audit quality in promoting earning quality.

Figure 1 .
Figure 1.Graphical Distributions of Normality Test . Lagrange Multiplier Test, first.If the probability value (p-value) is less than or equal to 0.05, use traditional least squares.(OLS).If the probability value (p-value) is greater than or equal to 0.05, select the common effect.2. If the probability value (p-value) is less than or equal to 0.05, use the fixed effect in the Hausman test.If the probability value (p-value) is greater than or equal to 0.05, use the random effect.3. Chow Test.The favored model is the common effect strategy if the probability crosssection F value is greater than 0.05.In the event where the probability cross section F value is smaller than 0.05, the fixed effect strategy is the preferable model.

Table 1
Descriptive Statistics of Earning Quality (EQ) by Industries

Table 2
Descriptive Statistics for independent variable

Table 8
The Hausman test results are shown in Table8, and the p-values are 0.1944 > 0.005.It may be assumed that the random effect model will be used in the current inquiry because all diagnostic tests show that it is valid and better suited than both the random effect and panel regression models.