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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.

The purpose of this study is to examine the impact of corporate social responsibility (CSR) expenditure and business responsibility report (BRR) on a firm’s financial performance. Additionally, the study explores whether CSR expenditure and firm performance are related linearly or otherwise. The study also assesses the influence of mandating CSR expenditure on a firm’s performance.

The study is set in India and uses a nine-year data set from 165 companies listed on the Bombay Stock Exchange. Data compilation and analysis are done by using content analysis and panel data regressions.

The main findings of the study are that the effect of CSR expenditure on firm performance in India is non-linear and can be characterized as parabolic for investigated firms. While some performance indicators suggest a U-shaped relationship, others show an inverted U-type pattern, making a definitive conclusion elusive in either direction. BRR scores themselves have a positive impact on firm performance. Mandatory CSR expenditure affects the financial performance negatively, but the market performance improves in general.

The study provides new insights on the relationship between CSR expenditure, BRR scores and firm performance from India, which is not only a notable emerging market but also has other gripping characteristics. It has a prolific history of philanthropy, and yet, it is the first country in the world to mandate CSR expenditure in recent times. The equation between reported economic progress and general quality of life remains intriguing, and yet the number of studies on the effects of CSR expenditure on firm performance are no match to the volume of ongoing and completed works in more developed markets. This study attempts to trim the gap and provide some useful insights for managers, policymakers and stakeholders, apart from prompting further research.

The primary purpose of this paper is to identify the so-called core authors and their publications according to pre-defined criteria and thereby direct the users to the fastest and easiest way to get a picture of the otherwise pervasive field of bankruptcy prediction models. The authors aim to present state-of-the-art bankruptcy prediction models assembled by the field's core authors and critically examine the approaches and methods adopted.

The authors conducted a literature search in November 2022 through scientific databases Scopus, ScienceDirect and the Web of Science, focussing on a publication period from 2010 to 2022. The database search query was formulated as “Bankruptcy Prediction” and “Model or Tool”. However, the authors intentionally did not specify any model or tool to make the search non-discriminatory. The authors reviewed over 7,300 articles.

This paper has addressed the research questions: (1) What are the most important publications of the core authors in terms of the target country, size of the sample, sector of the economy and specialization in SME? (2) What are the most used methods for deriving or adjusting models appearing in the articles of the core authors? (3) To what extent do the core authors include accounting-based variables, non-financial or macroeconomic indicators, in their prediction models? Despite the advantages of new-age methods, based on the information in the articles analyzed, it can be deduced that conventional methods will continue to be beneficial, mainly due to the higher degree of ease of use and the transferability of the derived model.

The authors identify several gaps in the literature which this research does not address but could be the focus of future research.

The authors provide practitioners and academics with an extract from a wide range of studies, available in scientific databases, on bankruptcy prediction models or tools, resulting in a large number of records being reviewed. This research will interest shareholders, corporations, and financial institutions interested in models of financial distress prediction or bankruptcy prediction to help identify troubled firms in the early stages of distress.

Bankruptcy is a major concern for society in general, especially in today's economic environment. Therefore, being able to predict possible business failure at an early stage will give an organization time to address the issue and maybe avoid bankruptcy.

To the authors' knowledge, this is the first paper to identify the core authors in the bankruptcy prediction model and methods field. The primary value of the study is the current overview and analysis of the theoretical and practical development of knowledge in this field in the form of the construction of new models using classical or new-age methods. Also, the paper adds value by critically examining existing models and their modifications, including a discussion of the benefits of non-accounting variables usage.

Although previous research has investigated how performance feedback may affect firms’ strategic actions, their findings has been inconsistent. The relationship between performance feedback and firms’ strategic activities thus appears complex. Moreover, the authors contend that it may vary with the measurement strategies employed (i.e. social or historical feedback, operationalizations of strategic actions or accounting- and market-based performance indicators) and the national contexts.

Therefore, the current article presents a comprehensive meta-analysis of prior research, including 1,637,817 sample observations from 101 studies that span more than 18 countries.

The results indicate that (1) performance that are below or above aspirational levels generally has a positive relationship with firms’ strategic actions; (2) these relationships are contingent on the implementation forms taken by the key variables, such as performance feedback, strategic actions and performance indicators; and (3) the relationships are much stronger in countries where managerial discretion is greater.

The findings contribute to the clarification of long-standing theoretical and empirical debates regarding the relationship between performance feedback and strategic actions, as well as some pertinent directions for future research.

This study aims to empirically investigate the impact of political instability on the banking stability of the dual banking system in the Middle East and North African (MENA) countries.

The study measures banking stability with probability of default (PD) and Zscore by employing the generalised method of moment (GMM) between 2007 and 2021 on the dual banking system in the region. The authors further estimate short-long-run situations coupled with a robustness test using a generalised least square (GLS) model.

The authors' findings indicate that institutional factors of political stability, crisis period, high-crisis countries, law and order and macroeconomic indicators influence the two types of banking stability in the region. The authors found the consistency of the factors explaining stability in the region in both short-and long-run situations. Consequently, the study also reveals the adverse effects of crisis periods and high-crisis countries on banking stability.

The results of this study explicitly identify the critical need for sustaining political stability and abiding by laws and order to achieve dual banking stability in the region. Therefore, policymakers may consider allowing the region's banks to operate beyond retail banking since diversification enhances banking stability.

The authors' study balances by employing dual stability measurement in predicting the impact of political instability, law and order and other indicators on the MENA region's two banking models. This study uncovers the effect of the global crisis period on banking stability and high-crisis countries in the region and verifies the models' robustness.

As of now, the digitization of corporate finance presents a paradigm shift in business strategy, innovation, financing and managerial capability around the globe. However, the prevailing finance scholarly works hardly document the impact of the digitalization of corporate finance on firm performance with global evidence and analysis. Hence, the contemporary debate on whether firm performance is genuinely stimulated because of the digitalization of corporate finance or not has been a pressing issue in the relevant literature. Therefore, the purpose of this study is to identify a data-driven, concise response to an unaddressed finance issue if the performance of high-digitalized firms (HDFs) outperforms that of their counterpart peers for wealth maximization.

The first stage test models examine the firm performance of relatively high-digitalized firms as opposed to low-digitalized firms based on the system GMM. The second stage test of the probabilistic (logit) model infers that the probability of being HDFs explores because of better performance. Then, the authors execute robust checks based on the different quantile regressions and Z-score-based system GMM. In addition, the authors recheck and present the test results of the fixed effect and random effect to capture time-invariant individual heterogeneity. Finally, the supplementary test findings of firms’ credit strength by using Altman five- and four-factor Z-score models are presented.

By using cross-country panel analysis as 15 years’ test bed for HDFs and low digitalized firms (LDFs), the test results indicate that the overall firm performance of a digitalized firm is significantly better than that of a non-digitalized firm. The global evidence documents that HDFs are exposed to higher values and are financially more persistent as compared to their counterparts. The finding is remarkably concomitant across several possible subsample analysis, such as country–industry–size–period analysis.

This study can be remarkably effective in encouraging managers, policymakers and investors to acknowledge the need for adopting the required digitalization. Overall, this original study addresses a core research gap in the corporate finance literature and remarkably provides further direction to rethink the assumptions of firm digitalization on additive value and thereby identify optimal decisions for wealth maximization. The findings also imply that investors require an additional risk premium if they invest in relatively LDFs, which have relatively lower market value and weaker firm performance.

From an investors point of view, the academic novelty contributes to an innovative and unsettled issue on the impact of digitization of corporate finance on firm performance because there is a new question of high or low digitization of corporate finance in the global market. Hence, this academic novelty contributes to sharing global evidence of the digitalization of corporate finance and its effect on firm performances. In addition, an intensive critical review analysis is conducted based on the most recent and relevant scholarly works published in the top-tier journals of finance and business stream to fix the hypothesis. Overall, this study addresses a core research gap in the corporate finance literature; notably provides further direction to rethink firm digitalization; and thereby identifies optimal decisions for shareholders’ wealth maximization.

The purpose of this study is to investigate the association between three corporate governance (CG) idiosyncrasies, namely audit committee characteristics, external audit quality (AQ), board diversity and firm performance (FP) in the South Asian Association for Regional Cooperation (SAARC) nations.

The study used a sample of 200 listed nonfinancial firms in the SAARC nations from 2012 to 2021. The System Generalized Method of Moment model was applied to the data consisting of 2000 firm-year observations. The Generalized Estimating Equation population-averaged model was also employed for added robustness. The study employed Tobin's Q as the measure of FP.

The findings revealed that amongst the CG variables tested, external AQ exhibited a significantly positive relationship with Tobin's Q. Significant negative influences on FP have been demonstrated by the variables of audit committee meeting and board's independence. Furthermore, gender diversity, CEO duality, audit committee strength and independence failed to record any significant association.

This study is one of the first to investigate the association between CG idiosyncrasies and FP in the SAARC nations. The study findings have important implications for policymakers and regulators in the region.

This paper aims to study the impact of CEOs’ personality and social connections on corporate risk-taking in the Russian market.

Using a sample of 93 large listed Russian corporations between 2008 and 2016, this study tests a range of personal traits, including the classical personal characteristics like age and tenure, some country-specific traits such as connections and military experience, as well as other human and social capital characteristics.

This study finds non-linear relationships between corporate risk-taking and CEO age and tenure. This study also finds that firms run by CEOs with military experience take more corporate risk. On the CEOs’ social capital side, this study’s results suggest that both political and educational connections are positively related to corporate risk-taking.

This study also tests some traits that have usually been ignored by the literature, such as marital and family status.

This study aims to empirically investigate the impact of oil prices, political instability and changes in stability on the bank diversification of the two types of banking systems in the Middle East and North African (MENA) countries.

The study uses bank diversification, stability measurement of probability of default and Zscore by adopting the generalised method of moment for the data between 2007 and 2021. The authors estimate short- and long-run dynamic panel analysis and a robustness test.

The findings reveal that Islamic banks are slightly lower in diversification and stability than conventional peers in the region. Diversification increases with a positive increase in GDP growth, law and order, political stability, bank size, asset quality, oil price, return on equity, profitability and change in banking asset-based stability. The authors found consistency in the two stability measurements in both short- and long-run situations.

Despite the change in banking stability and economic growth and oil prices improved diversification, banks in the region are not diversifying during the crisis period and political instability. Therefore, policymakers should improve mechanisms to monitor the crisis and political unrest to avoid the systemic risk that adversely affects the system through macro-financial linkages in the region.

This study uses change dual stability measurements and oil prices to predict MENA region bank diversification. The authors extended the banking literature by estimating the relationship between crisis periods, political and banking stability, oil prices and other institutional indicators of banking diversification. This study uncovers the effect of the global crisis period on banking diversification and the impact of banking stability changes and validates the models through robustness tests.

Executive decisions regarding capital financing are an important management aspect, especially during financing constraints and growth opportunities. The current study examines the impact of managerial skills of a company on capital financing decisions. Furthermore, it analyzed this nexus in financing constraints and growth opportunity situations.

The authors use the GMM (generalized method of moments) estimation approach on a dataset of 20,651 firm-year observations of Chinese A-share companies from 2010 to 2019.

The authors’ findings are compatible with management signaling and reputation enhancement theories, since they show that managerial skill is connected with more substantial debt financing. Managers with high management skills are likely to have more debt financing as they can foresee the economic future of their companies and tactfully convey private information, lowering information inequality and enhancing their reputation. Furthermore, the authors also show that firms with restricted financial resources and growth opportunities make this relationship stronger. Capital structure and managerial skill findings are unaffected by alternative specifications, omitted factors, industry group bias and endogeneity.

This study sheds fresh light on the essential manager personality trait of managing ability and how it influences complicated corporate decision-making, particularly in the tough environment due to financing constraints and competitive growth. The authors argue that high-ability managers are compelled to use debt financing not only to lessen information asymmetry but also to guarantee that the market finds their superior ability. This work contributes significantly to the managerial ability literature and the capital structure literature supporting signaling theory.