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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 investigate the relationship between corporate social responsibility (CSR) and financial distress and the moderating effect of firm characteristics, auditor characteristics and the Coronavirus disease 2019 (Covid-19) in China.

The research question is empirically examined on the basis of a data set of 1,257 Chinese-listed firms from 2011 to 2021. The dependent variable is financial distress risk, which is measured mainly by Z-score. CSR score is used as a proxy for CSR. Propensity score matching, two-stage least square and generalized method of moments are adopted to mitigate the potential endogeneity issue.

This study reveals that CSR can reduce financial distress. Specifically, results show an inverse relationship between CSR and financial distress, more significantly in non-state-owned enterprises, firms with non-BigN auditor and during Covid-19. The results are consistent and robust to endogeneity tests and sensitivity analyses.

This study enriches the literature on CSR and financial distress, resulting in a more attractive corporate environment, improved financial stability and more crisis-resistant economies in China.

This paper aims to study the default risk of small and medium-sized enterprises in the construction sector.

An unbalanced sample of 2,754 Portuguese companies from the construction sector, from 2008 to 2020, is analysed. Companies are classified in default or compliant following an ex-ante criterion. Then, using the stepwise analysis, the most relevant variables are selected, which are later used in the logit model. To verify the robustness of the results, a sample of legally insolvent companies is added (mixed criterion) and the initial sample is split into two subperiods.

Financial variables are the most relevant to predict the pattern for this sample. The main conclusions show that smaller and older companies, more indebted, with more liquidity and with higher EBIT have a higher probability of default. These conclusions are confirmed using a mixed criterion to classify companies as default or compliant and including a macroeconomic dummy.

This work not only contributes to enlarging the literature review but also makes relevant contributions to practice. Companies from the construction sector can understand which indicators must control to avoid financial problems. The government also has relevant information that can help in adapting or creating regulations for recovering or revitalizing companies.

This study proposed an ex-ante criterion that can be used for all types of companies. Most works use a legal or a mixed criterion that does not allow for detecting signs of financial problems in advance. Moreover, the sample used is almost unexplored – SMEs from a sector with great mortality rate.

This study aims to explore how earnings management techniques are affected by corporate financial debt risk (FDR), internal control (IC) effectiveness and CEO education.

The study uses a sample from listed firms in China from 2010 to 2017, comprising different industries, including agriculture, forestry, livestock farming and fishing; mining; manufacturing; electric power, gas and water production and supply; construction; transport and storage; information technology; the real estate industry; social services; and communication and cultural. The regression analysis is used to test the hypotheses. The two-stage least squares technique is used to check for endogeneity issues.

The study finds that firms are less likely to manage real earnings when they have more robust IC and FDR. Likewise, companies with weak ICs are more likely to manipulate real earnings. Besides, the study finds an influence of CEO education on the relationship between IC, FDR and real earnings management (REM). These results can be applied to the sectors in the sample covered by the research, and the authors do not overlook the energy industry sector for the importance of its role in the economy.

There are some limitations for the researcher when performing any research, and this study is no exception. Researchers are urged to take these circumstances into consideration when generalizing or comparing the results because the methods used to calculate the measurement variables in each study may differ somewhat from those used in other research. In addition, expanding the current research design to incorporate additional nations may be an area of interest for future research and could aid in evaluating the effects of nation-specific elements (such as inflation, culture, legal systems and political considerations) on the usefulness of IC and decreasing FDR. Second, the current study focuses on the impact of IC and FDR on REM; this paper does not dissect the “black box” of IC and consider how each element affects earnings management. Future research may need to focus specifically on how effective IC would affect earnings management and precisely what IC mechanisms would discourage the management of earnings.

Helping companies listed in China to make decisions and improve investors’ vision of the results of real companies’ businesses, as well as helping management to avoid falling into debt risk and the consequent effects and manipulation of earnings.

By highlighting the significance of IC and debt risk in enhancing information quality in China, the results contribute to the body of work examining the relationship between IC, FDR and REM. In addition, this study uses a CEO’s education to moderate this link.

The purpose of this study is to examine the impact of financial distress (FD) on investment-cash flow sensitivity (ICFS) of Indian firms.

The study uses the system generalized method of moments (GMM) technique to investigate the effect of FD on ICFS of Indian firms during the period from 2001 to 2019.

Using FD measures like Ohlson's bankruptcy method, Altman's Z-score model and financial-distress ratio, the researchers find that FD increases ICFS and negatively affects corporate investment. The researchers’ findings explain that FD increases restrictions on external financing, which makes cash flow more important for corporate investment. Additionally, the researchers find that the effects of FD on ICFS are weak (strong) for bigger and group affiliated (smaller and standalone) firms. The study’s findings are robust to several measures of FD, group affiliation and firm size.

First, the researchers find that FD affects the ICFS, therefore, financially distressed firms should have sufficient internal funds or external funds for investment. Second, lending agencies should also consider the firms' FD condition before providing funds to secure their money. Third, investors should be very careful while investing in a financially distressed firm as we find that financially distressed firms face a decline in their investment which might reduce firm profitability.

This study contributes to the existing literature by providing empirical evidence by analyzing the impact of FD on ICFS in the context of India. As per the authors’ knowledge, this is the first-ever attempt to examine the effect of FD on ICFS.

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.

This paper investigates the role of economic disagreement in the cross-sectional pricing of individual stocks. Economic disagreement is quantified with ex ante measures of cross-sectional dispersion in economic forecasts from the Survey of Professional Forecasters (SPF), determining the degree of disagreement among professional forecasters over changes in economic fundamentals.

The authors introduce a broad index of economic disagreement based on the innovations in the cross-sectional dispersion of economic forecasts for output, inflation and unemployment so that the index is a shock measure that captures different aspects of disagreement over economic fundamentals and also reflects unexpected news or surprise about the state of the aggregate economy. After building the broad index of economic disagreement, the authors test out-of-sample performance of the index in predicting the cross-sectional variation in future stock returns.

Univariate portfolio analyses indicate that decile portfolios that are long in stocks with the lowest disagreement beta and short in stocks with the highest disagreement beta yield a risk-adjusted annual return of 7.2%. The results remain robust after controlling for well-known pricing effects. The results are consistent with a preference-based explanation that ambiguity-averse investors demand extra compensation to hold stocks with high disagreement risk and the investors are willing to pay high prices for stocks with large hedging benefits. The results also support the mispricing hypothesis that the high disagreement beta provides an indirect way to measure dispersed opinion and overpricing.

Most literature measures disagreement about individual stocks with the standard deviation of earnings forecasts made by financial analysts and examines the cross-sectional relation between this measure and individual stock returns. Unlike prior studies, the authors focus on disagreement about the economy instead of disagreement about earnings growth. The authors' argument is that disagreement about the economy is a major factor that would explain disagreement about stock fundamentals. The authors find that disagreement in economic forecasts does indeed have a significant impact on the cross-sectional pricing of individual stocks.

To determine the effect that covenants have on the credit ratings assigned by the two major agencies.

The authors examine 1,822 bond issues from 1991 to 2018, with a two-stage methodology to account for the endogeneity of the firms' choices and the ordinal nature of the ratings. The authors use Hendry's model selection method to find the best-fitting models from 37 control variables; the final models feature 20–24 orthogonalized variables, all significant at 5% and most at 1%.

The study’s results suggest that restrictive covenants positively affect ratings, particularly for bonds on the border of junk and investment grade. However, this effect appears to be decreasing with time, suggesting the financial crisis of 2008 has impacted ratings. Additionally, divergent covenant treatment leads to split ratings where the two agencies assign different levels of ratings on the same bonds. The study’s findings provide key insights into the factors that differentiate ratings given by each agency.

Managers must balance the perceived benefits of covenants against the costs, included lower credit ratings.

No other study has examined this issue controlling for both the ordinal nature of the ratings and the endogeneity in the decision to include specific covenants.

This paper aims to examine the relationship between executive pay disparity and the cost of debt.

The authors use a sample of syndicated bank loans granted to United States (US) listed firms from 1992 to 2014 and adopt the loan yield spread (Chief Executive Officer (CEO) pay slice) as the main proxy for the cost of debt (executive pay disparity). The authors also use the Heckman two-stage model to address the sample selection bias and the two-stage least squares and propensity score matching methods to control the potential endogeneity issues. To test different views about executive pay disparity, the authors adopt the cash-to-stock ratio to proxy for managerial risk-shifting incentives.

The authors find that the cost of debt is significantly higher for firms with larger executive pay disparity, which is robust to sample selection bias, endogeneity concerns, alternative measures and various controls. This positive relationship increases with the risk-shifting incentives of CEOs instead of other top executives, which supports the managerial power view, and is stronger for firms with higher levels of financial distress. The findings suggest that creditors view executive pay disparity are associated with higher credit risk and CEO entrenchment.

This paper reveals one “dark” side of executive pay disparity: it increases the cost of debt and identifies a significant role played by CEOs' risk-shifting incentives. The authors provide direct evidence of the relevance of pay differential to corporate credit analysis.

Environmental, social and governance (ESG) issues have become the cornerstone of investment decisions in firms today. With that, publicly traded ESG indices (like the BSE ESG 100 index in India) have come into existence. The existing literature signifies that ESG generates financial implications and induces stability. The current study aims to test whether the firms listed on the ESG index (ESG-sensitive firms) face less financial distress than those not listed on such an index.

The study applies panel data difference-in-differences (DID) regression by considering ESG as an unstaggered treatment to 74 non-financial firms listed on India's Bombay Stock Exchanges (BSE) 100 index. In total, 42 firms are ESG treated as they got listed on the BSE ESG 100 index, formed in 2017. The remaining 32 firms form the control group. The confidence intervals and standard errors are estimated using clustered robust errors and the Donald and Lang method.

Listing on the ESG index matters for financial stability; differences in financial distress are significant on financial distress. ESG-sensitive firms face less financial distress than non-ESG firms (or firms not perceived as ESG-sensitive). The results are consistent across two financial distress measures, Altman z-scores for emerged and emerging markets. Thus, the DID in distress status between ESG-sensitive and non-ESG firms matter.

The study creates vibrant implications for practitioners using ESG to reduce financial distress.

The study is one of its kind to test the treatment effects of ESG on firm value and quantify treatment effects on financial distress.