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The purpose of this study is to examine whether the required disclosure and the high frequency of key audit matters (KAMs) are likely to moderate the effect of higher credit risk on earnings quality.

This study uses 15,106 Taiwanese firm-year observations to explore the relationship between earnings quality and credit risk during the 2011 to 2020 period. We use the two-stage least squares method to test whether the presence of KAM disclosures moderated the association between earnings quality and credit risk and also to examine whether higher KAM frequency moderates the association between earnings quality and credit risk.

Our results provide evidence that the presence of a KAM disclosure requirement moderates the impact of firms with higher credit risk on earnings quality. In addition, there is significant evidence that the higher the frequency of KAM disclosures the greater the moderation impact that is found.

This research investigates whether the disclosure and high frequency of KAMs moderates the effect of credit riskiness on earnings quality. This study improves our understanding of whether more KAMs disclosures would improve earnings quality of firms with higher credit risk. In addition, we also use Beneish M-SCORE, as an alternative earnings quality proxy, to reinforce our empirical results. This markedly differentiates this paper from other studies.

After completion of the case study, the students will be able to understand the calculation of cost of individual sources of funds and cost of capital, examine various tools such as economic value added and cash value added analyses which help determining whether a company has added value to its shareholders or not and explore the application of Benford’s law and the Beneish M-score in detecting manipulation of numbers in financial statements.

Nimmy Jacob, a newly recruited research analyst with an equity research firm, was entrusted with tracking the “auto ancillary industry”, specifically “Minda Corporation Ltd” (MIL). MIL was a leading diversified auto components manufacturing companies in India. The company’s share price meteorically rose during February 2021–2022 (Figure 1). The company’s turnover over the past few years had grown at a compounded annual growth rate of 15% during the three preceding years. The company had in the recent past bought a 15% stake in another competitor, Pricol Ltd, for a consideration of INR 400 crores and previously had used joint ventures and acquisitions to scale up its operations. Jacob, apart from the conventional financial analysis, had to ascertain whether all the strategic decisions were adding value to the shareholders’ investments by exploring the various tools available for the same and also calculate the minimum expected rate of return for MIL. Jacob was apprehensive about the financial statements, although the numbers for the company were good. Jacob was skeptical about a high-growth company having the incentive to manipulate its earnings. Manipulations could be in the form of abnormal increase in accruals, inconsistency in expenses and high days of receivables. Therefore, Jacobs used certain analytics/statistical tools to detect any manipulation of numbers in the financial statements of the company and to ascertain apt findings about the company.

This case study is intended for discussion in corporate finance, financial reporting and analysis and financial analytics at Master of Business Administration/undergraduate level.

Teaching notes are available for educators only.

CSS1: Accounting and finance

This study aims to examine the determinants of Foreign Corrupt Practices Act (FCPA) violations and consequences of FCPA enforcements.

This paper uses publicly available data from Compustat, I/B/E/S and Thomson Reuters databases, combined with Securities and Exchange Commission (SEC) and Department of Justice (DOJ) cases, to extract insights on FCPA violations and enforcements using econometric approaches.

The main determinants of FCPA violations appear to be firm size, multinational structure, country corruption and Sarbanes-Oxley Act control weaknesses. Traditional misreporting risks (F-score and M-score) do not predict FCPA violations. This study discovers significant differences between FCPA violations by motivation, as in, sale generation, rent extraction or cost evasion. Bribes motivated by sale generation or rent extraction are partially driven by the extent of the firm’s global operations, whereas bribes motivated by cost evasion relate to internal influences. This study also finds that enforcement is more salient for criminal violations (DOJ enforcement), compared to civil violations (SEC enforcement).

This research provides new insights into the determinants of FCPA violations while underscoring the need for effective measures to combat bribery and promote ethical business practices. This research contributes to the ongoing efforts to curtail bribery, offering valuable insights into the characteristics of firms more likely to engage in bribery and contexts in which these activities occur. It provides critical implications for regulatory bodies, highlighting the differential responses of firms to varying types of enforcement, namely, criminal versus civil, as the authors observe greater decreases in internal control weaknesses following DOJ enforcement compared to SEC enforcement.

For enforcement agencies, the findings underscore the importance of rigorous criminal enforcement against FCPA violations, highlighting the improved control environments prompted by DOJ actions. Managers will find this research relevant, as it demonstrates that a firm’s entry into international markets substantially elevates the risk of its representatives engaging in bribery with foreign officials. In addition, the results are of interest to regulators, revealing that the underlying motivations driving a firm’s activities can significantly alter the factors to consider that might lead to an FCPA violation.

This paper is the original work of the authors and explores the determinants and consequences of FCPA violations and enforcement actions since 2002. To the best of the authors’ knowledge, it is the first to explore bribe determinants by their motive and documents industry-wide benefits arising from criminal enforcement.

Companies avoid taxes in a variety of ways and use different methods to do that, one of the most common being earnings management. The purpose of this paper is to investigate whether companies manipulate their financial data in order to reduce taxes paid.

We considered a sample of 63 listed Romanian companies for the period 2016–2021. The Beneish model was used for estimating earnings management, and the effective tax rate was used to measure tax avoidance. The analysis was carried out using regression analysis in Stata13 software.

The findings of the research indicate a negative and statistically significant association between effective tax rate and earnings management, implying that one of the main reasons why companies manipulate their earnings to reduce tax burden and avoid taxes. Moreover, our results show that return on assets (ROA) has a statistically significant negative influence on the effective tax rate. Furthermore, our analysis reveals that firm size, growth, and Big4 audit have no effect on effective tax rate.

Because it analyzes concrete cases using financial data and provides some recommendations for addressing the issue of tax avoidance, this work is useful in advancing both quantitative and qualitative research on this topic. This research is relevant for businesses, governments, regulators, audit professionals and investors.

The study, by analyzing concrete cases using reported financial data, contributes in filling the gap within the literature that results from a lack of scientific research on the relationship between tax avoidance and earnings management, and then it clarifies the nature of the causal connection between them. Moreover, it considers a combination of firm related variables including performance, size and also audit quality.

This study aims to apply Benford’s law to examine the earnings management of companies listed in emerging ASEAN-5 countries: Indonesia, Malaysia, Philippines, Thailand and Vietnam.

The authors follow Amiram et al. (2015) to measure deviations from Benford’s law of the first digits of numbers reported in financial statements. The authors use the Jones-modified performance-match model (Jones, 1991; Dechow et al., 1995; Kothari et al., 2005) to estimate accrual earnings management. The authors use a sample of 47,389 observations of listed companies in ASEAN-5 countries from 2006 to 2019. The authors also run ordinary least squares (OLS) regressions to test the hypotheses.

The authors find that the first digits of numbers reported in the financial statements of companies in the sample closely conform to Benford’s law. Further evidence shows that the deviation from Benford’s law is positively related to abnormal accruals. The relationship between deviation from Benford’s law and abnormal accruals is more pronounced for the post-international financial reporting standards adoption period. The results survive for some robustness checks.

The authors show that Benford’s law holds for financial statements of companies listed in the emerging ASEAN-5 countries.

Auditors could use Benford’s law as an analytical procedure to assess the risks of material misstatements. Also, other users could apply Benford’s law on audited financial statements to foresee undetected misstatements.

The authors provide original evidence that financial statements of ASEAN-5 countries follow Benford’s law. The evidence supports the usefulness of Benford’s law in developing markets.

This paper undertakes an extensive and systematic review of the literature on earnings management (EM) over the past three decades (1992–2022). Furthermore, the study identifies emerging research themes and proposes future avenues for further investigation in the realm of EM.

For this study, a comprehensive collection of 2,775 articles on EM published between 1992 and 2022 was extracted from the Scopus database. The author employed various tools, including Microsoft Excel, R studio, Gephi and visualization of similarities viewer, to conduct bibliometric, content, thematic and cluster analyses. Additionally, the study examined the literature across three distinct periods: prior to the enactment of the Sarbanes-Oxley Act (1992–2001), subsequent to the implementation of the Sarbanes-Oxley Act (2002–2012), and after the adoption of International Financial Reporting Standards (2013–2022) to draw more inferences and insights on EM research.

The study identifies three major themes, namely the operationalization of EM constructs, the trade-off between EM tools (accrual EM, real EM and classification shifting) and the role of corporate governance in mitigating EM in emerging markets. Existing literature in these areas presents mixed and inconclusive findings, suggesting the need for further theoretical development. Further, the study findings observe a shift in research focus over time: initially, understanding manipulation techniques, then evaluating regulatory measures, and more recently, investigating the impact of global accounting standards. Several emerging research themes (technology advancements, cross-cultural and cross-national studies, sustainability, behavioral aspects and non-financial indicators of EM) have been identified. This study subsequent analysis reveals an evolving EM landscape, with researchers from disciplines like data science, computer science and engineering applying their analytical expertise to detect EM anomalies. Furthermore, this study offers significant insights into sophisticated EM techniques such as neural networks, machine learning techniques and hidden Markov models, among others, as well as relevant theories including dynamic capabilities theory, learning curve theory, psychological contract theory and normative institutional theory. These techniques and theories demonstrate the need for further advancement in the field of EM. Lastly, the findings shed light on prominent EM journals, authors and countries.

This study conducts quantitative bibliometric and thematic analyses of the existing literature on EM while identifying areas that require further development to advance EM research.

The purpose of the paper is to examine the association between internal control quality (ICQ) and audit report lag (ARL) and to test whether family directors affect the relationship.

ICQ is measured by using the framework developed by Michelon et al. (2015), while ARL is measured as the number of days from fiscal year-end to the date of the auditor's report.

Using a sample of 190 French companies over the period of 2016–2019, the authors document that ICQ is negatively associated with ARL, suggesting that ICQ represents a key determinant of audit delay. When testing for the moderating effect of family directors on this relationship, findings show that under high percentage of family directors on the board, this relationship becomes insignificant.

This paper extends previous research on audit delays by investigating the moderating effect of family directors on the relation between ICQ and ARL in the French setting. The empirical evidence highlights the adverse effect of the concentration of family directors on the board on timely disclosure as proxied by ARL.

This paper investigates the relationship between risk management practices and potential fraudulent financial reporting in Malaysia by considering recent regulatory reforms of the Malaysian government on risk management practices.

The sample of this study was based on 257 firm-year observations during the 2012–2017 period. This study employed panel-least square regressions with period fixed effects.

This study found a significant association between risk management activities in the disclosure and potential fraudulent financial reporting. Nevertheless, this study found there is insignificant effect of the risk-management committee in reducing potential of fraudulent financial reporting.

This study is a pioneer research that relates firms’ risk management practices with potential fraudulent financial reporting measured by F-score. Thus, this study provides an insight to regulators on the extent of risk-management practices in deterring potential fraudulent financial reporting which can be used as an input for greater enforcement of risk-management regulations.

This study aims to objectively synthesize the volume of accounting literature on financial statement fraud (FSF) using a systematic literature review research method (SLRRM). This paper analyzes the vast FSF literature based on inclusion and exclusion criteria. These criteria filter articles that are present in the accounting fraud domain and are published in peer-reviewed quality journals based on Australian Business Deans Council (ABDC) journal ranking. Lastly, a reverse search, analyzing the articles' abstracts, further narrows the search to 88 peer-reviewed articles. After examining these 88 articles, the results imply that the current literature is shifting from traditional statistical approaches towards computational methods, specifically machine learning (ML), for predicting and detecting FSF. This evolution of the literature is influenced by the impact of micro and macro variables on FSF and the inadequacy of audit procedures to detect red flags of fraud. The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper chronicles the cluster of narratives surrounding the inadequacy of current accounting and auditing practices in preventing and detecting Financial Statement Fraud. The primary objective of this study is to objectively synthesize the volume of accounting literature on financial statement fraud. More specifically, this study will conduct a systematic literature review (SLR) to examine the evolution of financial statement fraud research and the emergence of new computational techniques to detect fraud in the accounting and finance literature.

The storyline of this study illustrates how the literature has evolved from conventional fraud detection mechanisms to computational techniques such as artificial intelligence (AI) and machine learning (ML). The findings also concluded that A* peer-reviewed journals accepted articles that showed a complete picture of performance measures of computational techniques in their results. Therefore, this paper contributes to the literature by providing insights to researchers about why ML articles on fraud do not make it to top accounting journals and which computational techniques are the best algorithms for predicting and detecting FSF.

This paper contributes to the literature by providing insights to researchers about why the evolution of accounting fraud literature from traditional statistical methods to machine learning algorithms in fraud detection and prediction.

This research has two primary goals: first, to develop a composite index that evaluates the degree to which Asian–Pacific economies are prepared to engage in public–private partnerships (PPPs), and second, to investigate the factors that have been most influential in the formation of PPP arrangements in the nations' infrastructure over the course of the period 1995–2016.

The study constructs sectoral and overall index of possible determinants of PPP. Subsequently, it examines each constructed index's role in PPP investment. The author also conducted a panel data analysis to understand the role of each of the potential determinants on PPP projects and investments. This paper analyzes the author’s empirical models using a range of cross-section and panel estimators, including Poisson, zero-inflated Poisson and fixed effect.

The study’s results based on cross-section analysis suggest that regulatory and institution quality, institutional arrangement and regulatory frameworks, financial market development and macroeconomic stability positively impact investment in PPP. Moreover, the results depict that financial market development has the most substantial impact on PPP investment, followed by macroeconomic stability and prior experience with PPPs. The panel data analysis shows that per-capita income, financial development, inflation, debt, resource import and fuel export are crucial determinants of PPP in Asian–Pacific economies.

Governments of the countries should promptly amend the important policies outlined in this study and adopt a more robust strategy to foster a competitive PPP environment. This will aid in maintaining transparency and gaining the confidence of investors. The study’s findings may assist policymakers in focusing on specific areas in need of improvement. Social welfare and industrialization are ultimately enhanced by the formulation of such policies and by attracting additional infrastructure investment.

This is the first attempt to rank countries on the basis of PPP enablers. Unlike previous studies, this study examines the role of a large number of indicators in determining PPP investment and projects in cross-section as well as panel data framework. The study also investigates the effects of PPP specific provisions and rules. Furthermore, the focus is specifically on Asian–Pacific countries, which are a mix of third-world, emerging, developing and developed countries. Focusing on Asia–Pacific is also crucial because the region is home to most of the world's population, and the region's infrastructure outcomes significantly impact their lives.