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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 empirical study’s primary goal is to examine the connection between accounting information and share price for financial companies listed on Jordan’s expanding Amman Stock Exchange (ASE) between 2014 and 2018.

The correlation between accounting data and share price was investigated using multiple regression analysis. In this vein, “pooled ordinary least squares (OLS), fixed effect and random effect” static panel data estimators were used. The OLS model was chosen as the best model after a series of diagnostic tests.

The multiple proxies of accounting information value relevance have a positive and considerable impact on the market value per share, according to panel data research. Comparatively, the authors find proof that among the other accounting data – earnings, dividends and cash flow from operations – book value is statistically the most value-relevant.

This empirical investigation was only conducted in Jordan. Because it is very likely to obtain different results in other nations, the findings cannot be applied to other business environments.

The findings of this paper may highlight the amazing relationship between accounting information and share price for policymakers, regulators and other stakeholders in developing nations, notably in Jordan. This could pave the way for effective accounting disclosures.

Seldom does empirical research on the relationship between accounting data and share prices from publicly traded companies on ASE exist. So, by demonstrating empirical findings from Jordanian companies, this study fills the gap in the existing literature and knowledge.

This study aims to compare the value relevance of accounting numbers prepared under the Accounting and Auditing Organization for Islamic Financial Institutions (AAOIFI) standards with those produced under the International Financial Reporting Standards (IFRS) for Takaful companies (TC).

The authors assess the value relevance of accounting numbers using the Easton and Harris (1991) and Ohlson (1995) return and price models. They also use 54 insurance companies from 10 developing countries in Asia and the Middle East from 2006 to 2015.

The analysis shows that book value is significantly related to stock price under AAOIFI and IFRS. It also shows that TC adopting AAOIFI accounting standards have a more significant effect on stock price. This suggests that AAOIFI standards are more value relevant than IFRS.

TC and their stakeholders can use the findings to determine which accounting standards (IFRS or AAOIFI) produce the more relevant accounting information. This study is useful for investors that consider Islamic ethical practices to make their investment decisions for the standards-setting bodies that focus on establishing accounting standards for the Takaful industry.

The authors investigate a new aspect of the topic of value relevance. To the best of the authors’ knowledge, they believe this is the first paper examining the value relevance of TC’ accounting information prepared under AAOIFI and IFRS.

The paper aims to investigate the impact of political connections and board ethnicity on the value relevance of earnings and book value in Mauritius.

This study is based on a sample of 541 Mauritian-listed firm-year observations for 2001–2016. Financial and board diversity data have been collected using the listed firms’ annual reports and from reports published by the Stock Exchange of Mauritius. Political connection data was derived from the directory of Chief of State and Cabinet members. The research hypotheses were empirically tested using a modified Ohlson (1995) price model.

This study shows that political connections negatively impact the value relevance of earnings and book value. The authors find that firms with Franco-Mauritian directors will constrain political connections’ negative impact. The authors find contrasting results for Indo-Mauritian directors since they form an integral part of the government in Mauritius.

This study contributes to the scarce accounting literature in Mauritius. Firstly, no study has investigated the relationship between the value relevance of accounting information and political connections in Mauritius. Secondly, Mauritius’s capital market is dominated by a non-indigenous ethnic group, Franco-Mauritians, who remain the economic elite. Hence, Mauritius presents an opportunity to bring forth another important aspect in the capital market and corporate governance; diversity on the board of directors. Therefore, the study extends to the political connections and board diversity literature.

This paper aims to test whether the extent of compliance with International Financial Reporting Standards (IFRS) 7 requirements is value relevant and whether it influences the value relevance of the firm's accounting information (book value of shareholders' equity and net income).

The sample for this paper consists of 288 financial institutions listed on the Toronto Stock Exchange (TSX) from 2016 to 2019. Panel regressions have been used in this study.

The findings reveal that compliance with IFRS 7 is positively associated with the firm's market value. After making a classification between high-compliance and low-compliance companies, the authors' results indicate that the compliance level is positively associated with the value relevance of net income. Surprisingly, when examining the value relevance of financial instruments disclosures (FID) supplied after the adoption of IFRS 9, the authors find that book values of shareholders' equity and earnings are not more value relevant in the post-IFRS 9 period.

Given that the authors' analysis has been restricted to the Canadian setting, the regression results might not be generalized for other countries with different capital markets features.

The authors' findings point out that FID can affect investors' decisions as well as their confidence in the companies in which they invest. Hence, the regulatory bodies should gear more efforts to ensure high-compliance levels.

To the best of the authors' knowledge, this research is among the first attempts to investigate whether the new FID (after the adoption of IFRS 9) improves the firm disclosure quality and enhances the value relevance of accounting information.

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.

This study aims to develop a model based on the financial variables for better accuracy of financial distress prediction on the sample of private French, Spanish and Italian firms. Thus, firms in financial difficulties could timely request for troubled debt restructuring (TDR) to continue business.

This study used a sample of 312 distressed and 312 non-distressed firms. It includes 60 French, 21 Spanish and 231 Italian firms in both distressed and non-distressed groups. The data are extracted from the ORBIS database. First, the authors develop a new model by replacing a ratio in the original Z”-Score model specifically for financial distress prediction and estimate its coefficients based on linear discriminant analysis (LDA). Second, using the modified Z”-Score model, the authors develop a firm TDR probability index for distressed and non-distressed firms based on the logistic regression model.

The new model (modified Z”-Score), specifically for financial distress prediction, represents higher prediction accuracy. Moreover, the firm TDR probability index accurately depicts the probabilities trend for both groups of distressed and non-distressed firms.

The findings of this study are conclusive. However, the sample size is small. Therefore, further studies could extend the application of the prediction model developed in this study to all the EU countries.

This study has important practical implications. This study responds to the EU directive call by developing the financial distress prediction model to allow debtors to do timely debt restructuring and thus continue their businesses. Therefore, this study could be useful for practitioners and firm stakeholders, such as banks and other creditors, and investors.

This study significantly contributes to the literature in several ways. First, this study develops a model for predicting financial distress based on the argument that corporate bankruptcy and financial distress are distinct events. However, the original Z”-Score model is intended for failure prediction. Moreover, the recent literature suggests modifying and extending the prediction models. Second, the new model is tested using a sample of firms from three countries that share similarities in their TDR laws.

This study aims to investigate the effect of accounting conservatism and business strategies as mitigating tools for bankruptcy risk. It determines the association among these factors and provides insights into the effectiveness of accounting discretion and business strategies in decision-making.

The study uses a sample of 83 nonfinancial listed firms in ASE for the period from 2013 to 2019. Bankruptcy risk is measured using the Altman Z-score (1968). Accounting conservatism is measured using the accrual-based approach, and optimal business strategies are identified through cluster analysis.

The results indicate that accounting conservatism has a significant negative effect on bankruptcy risk. Increased application of accounting conservatism practices leads to a decrease in the level of bankruptcy risk. However, the type of business strategy adopted by firms does not have a significant impact on bankruptcy risk, suggesting that firms are not effectively implementing their strategies to mitigate this risk.

This study focuses on nonfinancial listed firms in the ASE, limiting the generalizability of the findings to other contexts. The study's findings contribute to the understanding of the role of accounting conservatism in reducing bankruptcy risk but highlight the need for further research on the effectiveness of business strategies in mitigating this risk.

This study lies in understanding of the role of accounting discretion in financial evaluations and emphasizes the importance of accounting conservatism as a tool for mitigating bankruptcy risk. The study's insights provide valuable guidance to practitioners, regulators and researchers in this field.

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 study aims to investigate how the intangible intensive nature of firms affects the value relevance of earnings and the book value of equity between profit- and loss-reporting firms. The study also examines how firms’ intangible intensity affects the value relevance of R&D outlays between profit- and loss-reporting firms.

An empirical analysis based on Ohlson’s (1995) framework is used. A total of 54,421 firm-year observations of Indian listed firms from financial years 1992–2016 constitute the study sample.

The findings suggest that the difference in the value relevance of earnings and the book value of equity between profit- and loss-reporting firms is more significant in non-intangible intensive firms than in intangible firms. Specifically, earnings are more value relevant in profit-reporting and non-intangible intensive firms, whereas book value of equity is more value relevant in loss-reporting and intangible intensive firms. The results also suggest that the difference in the incremental value relevance of R&D information between profit- and loss-making firms is higher in intangible intensive firms than in non-intangible intensive firms.

The findings of this study can help managers, standard-setters and investors make effective decisions.

This study offers insights into the impact of intangible intensity on the value relevance of aggregated and disaggregated accounting information between profit- and loss-making firms in institutional settings where capitalization of R&D expenditures is allowed.