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This study updates the literature review of Jones (1987) published in this journal. The study pays particular attention to two important themes that have shaped the field over the past 35 years: (1) the development of a range of innovative new statistical learning methods, particularly advanced machine learning methods such as stochastic gradient boosting, adaptive boosting, random forests and deep learning, and (2) the emergence of a wide variety of bankruptcy predictor variables extending beyond traditional financial ratios, including market-based variables, earnings management proxies, auditor going concern opinions (GCOs) and corporate governance attributes. Several directions for future research are discussed.

This study provides a systematic review of the corporate failure literature over the past 35 years with a particular focus on the emergence of new statistical learning methodologies and predictor variables. This synthesis of the literature evaluates the strength and limitations of different modelling approaches under different circumstances and provides an overall evaluation the relative contribution of alternative predictor variables. The study aims to provide a transparent, reproducible and interpretable review of the literature. The literature review also takes a theme-centric rather than author-centric approach and focuses on structured themes that have dominated the literature since 1987.

There are several major findings of this study. First, advanced machine learning methods appear to have the most promise for future firm failure research. Not only do these methods predict significantly better than conventional models, but they also possess many appealing statistical properties. Second, there are now a much wider range of variables being used to model and predict firm failure. However, the literature needs to be interpreted with some caution given the many mixed findings. Finally, there are still a number of unresolved methodological issues arising from the Jones (1987) study that still requiring research attention.

The study explains the connections and derivations between a wide range of firm failure models, from simpler linear models to advanced machine learning methods such as gradient boosting, random forests, adaptive boosting and deep learning. The paper highlights the most promising models for future research, particularly in terms of their predictive power, underlying statistical properties and issues of practical implementation. The study also draws together an extensive literature on alternative predictor variables and provides insights into the role and behaviour of alternative predictor variables in firm failure research.

This study aims to explore the relationship between fraud triangle theory (FTT) and the accounting fraud phenomenon in all listed companies in China.

The CSMAR database is used as the sample, including 16,063 data of all listed companies in Shanghai and Shenzhen markets for the 2010–2020 period. The authors also use quantitative methods, such as regression analysis, to investigate the relationship between five variables (cover three elements of FTT) and fraud occurrence.

Results show that leverage and liquidity ratios positively affect fraud detection, whereas return on net equity, audit size and independent director percentage negatively affect fraud detection.

This study enriches theoretical research on the causes of accounting fraud in China and is of great significance to the sound development of China’s capital market.

This study aims to focus on the impact of COVID-19 on the Spanish wine sector and the financial resilience of Spanish wineries in the period 2019–2020.

The data set contains 355 limited companies of the Spanish wine sector which were active in the period 2019–2020. The explanatory variables used are size and age of the company, exports, subsidies and gender distribution in the workforce. The financial statements of the companies are treated as compositional data, using log-ratios for asset structure, leverage, margin, turnover and debt maturity. The first-difference estimator is used for the panel-data model relating the differences in the log-ratios between 2020 and 2019 to the explanatory variables.

In average terms, margin and turnover have significantly worsened between 2019 and 2020, while debt maturity has increased. A larger firm size, a greater age, a higher share of women in the workforce and subsidies have made wineries more resilient between 2019 and 2020.

To the best of the authors’ knowledge, this is the first financial statement analysis of the impact of COVID-19 in the winery sector.

To report inflated operating performance indicators, such as operating revenue and operating profit, managers vertically reposition revenue and expense items inside the income statement. This study aims to investigate the relationship between credit market incentives and these practices.

This study examined a sample of 1,592 Bombay Stock Exchange-listed companies from 2009 to 2021 and tested them using panel data regression models. The propensity score matching method and different measurements of classification shifting practices are used to validate the results.

The conclusions drawn from the empirical data show that firms prefer revenue shifting over expense shifting to prevent debt covenant violations. It shows that the firm’s classification-shifting practices are driven by credit market incentives. This finding is consistent with the notion of positive accounting theory that firms engage in classification shifting (earnings management) to avoid violation of debt covenants. Further, the firm’s preference for revenue shifting is in line with the ease-need-advantage-based shifting framework where firms choose the shifting tool based on costs and constraints associated with each tool.

The finding suggests that if managers heavily rely on revenue shifting to avoid debt covenant violations, the firm may end up breaking these covenants based on its actual operating performance. Managers may use aggressive accounting techniques to prevent covenant violations, which can be a warning indicator of financial difficulties or operational problems. It highlights the necessity for creditors and investors to carefully evaluate a company’s financial stability outside of the financial statements that are publicly disclosed. Authorities should create separate forensic accounting standards for auditors to check revenue items and stop the corporate misfeasance of revenue shifting.

The study is among the earlier attempts to provide empirical evidence on credit market incentives behind classification shifting practices. It is the first study that documents the substitution relationship between classification shifting forms for avoiding violation of debt covenants.

This paper aims to document the effect of shariah compliance on stock price synchronicity.

This paper uses the data of non-financial firms from India and various estimation procedures (pooled OLS and instrument variable regression) to test the arguments presented in this paper. The time period of the study ranges between 2000 and 2019.

The results show that shariah-compliant firms have significantly higher levels of synchronicity than non-compliant firms. The findings hold after comprehensive inclusion of relevant controls and to a number of sensitivity tests. The authors attribute this result to the unique financial characteristics (lower levels of leverage, liquidity and cash) of shariah-compliant firms. The paper argues that these characteristics are related to better information environment which is responsible for higher levels of synchronicity. The paper also shows that the difference in the synchronicity levels of the two groups is less pronounced for those shariah-compliant firms that have relatively high levels of leverage and cash ratios.

The authors believe that this is an initial attempt to document the impact of shariah compliance on stock price synchronicity.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

Bank creditworthiness refers to the evaluation of a bank’s ability to meet its financial obligations. It is an assessment of the bank’s financial health, stability and capacity to manage risks. This paper aims to investigate the credit rating patterns that are crucial for assessing creditworthiness of the Islamic banks, thereby evaluating the stability of their industry.

Three distinct machine learning algorithms are exploited and evaluated for the desired objective. This research initially uses the decision tree machine learning algorithm as a base learner conducting an in-depth comparison with the ensemble decision tree and Random Forest. Subsequently, the Apriori algorithm is deployed to uncover the most significant attributes impacting a bank’s credit rating. To appraise the previously elucidated models, a ten-fold cross-validation method is applied. This method involves segmenting the data sets into ten folds, with nine used for training and one for testing alternatively ten times changeable. This approach aims to mitigate any potential biases that could arise during the learning and training phases. Following this process, the accuracy is assessed and depicted in a confusion matrix as outlined in the methodology section.

The findings of this investigation reveal that the Random Forest machine learning algorithm superperforms others, achieving an impressive 90.5% accuracy in predicting credit ratings. Notably, our research sheds light on the significance of the loan-to-deposit ratio as a primary attribute affecting credit rating predictions. Moreover, this study uncovers additional pivotal banking features that intensely impact the measurements under study. This paper’s findings provide evidence that the loan-to-deposit ratio looks to be the purest bank attribute that affects credit rating prediction. In addition, deposit-to-assets ratio and profit sharing investment account ratio criteria are found to be effective in credit rating prediction and the ownership structure criterion came to be viewed as one of the essential bank attributes in credit rating prediction.

These findings contribute significant evidence to the understanding of attributes that strongly influence credit rating predictions within the banking sector. This study uniquely contributes by uncovering patterns that have not been previously documented in the literature, broadening our understanding in this field.

This study, using stakeholder approach, aims to examine the impact of corporate governance and risk-taking on the performance of the top 100 nonfinancial listed firms in Vietnam from 2015 to 2019.

The theoretical and empirical studies are reviewed for rational hypotheses development. Firm performance is represented by return on assets, return on equity and Tobin’s Q.

Specifically, concentrated ownership structure, large workforce, being a great workplace, quick sales growth, high receivables turnover, being funded by both the state and foreigners and high-risk exposure positively affect firm performance. However, a high level of state ownership or foreign ownership, more independent members on board, large board size and chief executive officer (CEO) duality show an inverse effect. Besides, an inverted U-shaped relationship with firm performance is recognized for liquidity ratios.

This study uses three triangles, including governance, risk, and performance. The paper offers some evidence-based recommendations to improve firm performance in Vietnamese businesses.

The purpose of this paper is to construct an investment account holders' transparency and disclosure (IAH-T&D) index based on the new and revised accounting standard for investment accounts of the Accounting and Auditing Organization for Islamic Financial Institutions Standards (AAOIFI) (2020). It also aims to measure and compare the compliance level with IAH-T&D over years and between countries.

This study uses the content analysis method to analyze the content of 270 annual reports across 30 Islamic banks (IBs) in 10 Middle East and North Africa countries during the period from 2010 to 2019.

This study introduces a new IAH-T&D index which consists of 27 items representing four categories: investment accounts disclosure (11 items), incentive earnings disclosure (1 item), allocations and reserve disclosure (4 items) and general requirements for disclosure (11 items). The analysis shows that the level of IAH-T&D is 51%. The level of compliance varies over the years and across countries.

To the best of the authors’ knowledge, this is the first study that offers an original self-constructed-T&D index that could enhance future research related to determinants and consequences of IAH-T&D practice in IBs.

This paper aims to determine whether Islamic banks (IBs) and conventional banks (CBs) in Tunisia are distinguishable from one another based on financial characteristics during the 2005–2014 period covering the 2008 global financial crisis (GFC) and the 2011 Tunisian revolution.

For the comparison between IBs and CBs, 11 hypotheses are formulated to distinguish between the two types of banks. The authors use a univariate analysis based on the multi-dimension figures investigation and a multivariate one based on the robust OLS technique for panel linear regression with mixed effects.

Bank-specific factors, dummy and dummy interacting variables indicate that there are differences between Islamic and conventional bank behavior. Both methods show that IBs are more liquid, more profitable and riskier than CBs. Post-2011 Tunisian revolution, small IBs (small CBs) are more (less) solvent, large IBs are more stable and both types of banks are more liquid, which explain why Tunisian governments have relay on bank system to cover budget deficits post-2011 revolution.

In investigating the feature of IBs and CBs from the Tunisian context, the authors take into account the effect of two abnormal events (2008 GFC and 2011 Tunisian revolution) on IBs through interaction variables.