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This study aims to develop a comprehensive insolvency model tailored to Islamic banks, ensuring alignment with Shariah principles throughout pre-insolvency, bankruptcy and post-bankruptcy stages.

The research adopts a qualitative research method, using a desktop research approach. Primary sources and secondary sources are examined to gather information and draw conclusions.

This study presents a comprehensive insolvency model designed for Islamic banks, rooted in Shariah principles. The model covers pre-insolvency, bankruptcy (taflis) and post-bankruptcy stages, incorporating key Shariah parameters to ensure adherence to Islamic finance principles. It addresses challenges such as adapting to dynamic financial landscapes and varying interpretations of Shariah principles. Notably, the model recognizes the separate legal personality of Islamic banks and emphasizes transparency, fairness and compliance with religious obligations. In the post-bankruptcy stage, directors are urged to voluntarily settle remaining debts, aligning with ethical and Shariah-compliant standards.

The study contributes to the stability and growth of Shariah-compliant financial systems by extending insolvency principles to Islamic banks, providing a foundation for future research and policymaking specific to this context.

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 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.

Sustainability is essential to the ongoing operations of banks, though it is much less clear how Islamic corporate governance (ICG) promotes economic sustainability (ES) and thereby prevents bankruptcy. To explore the unexplored, this study aims to examine the efficacy of ICG in preventing bankruptcy and enhancing the ES of Islamic banks operating in Pakistan.

The current study measures ES through Altman's Z-score to analyze the level of the industry's stability and consequently examines the effect of ICG on the ES of Islamic banks in Pakistan for the post-financial-crises period. Using the country-level data, this study utilized a fixed-effect model and two-stage least squares (2SLS) techniques on balanced panel data spanning from 2009 to 2020 to provide empirical evidence.

The empirical results unveiled that board size and meetings have a significant positive influence on the ES while managerial ownership demonstrated an unfavorable effect on ES. Interestingly, the insignificant effect of women directors became significant with the inclusion of controlled variables. Overall, the findings indicate that ICG is an efficient tool for promoting ES in Islamic banks and preventing them from the negative effects of emerging crises.

The findings provide concrete insights for policymakers, regulators and other concerned stakeholders to execute a sturdy corporate governance system that not only oversees the economic, social and ethical aspects but also provides measures to alleviate the impacts of potential risks like the COVID-19 pandemic.

Examining the role of ICG in alleviating bankruptcy risk is an informative and useful endeavor for all social actors.

To the best of the authors’ knowledge, this study is one of the first efforts to provide evidence-based insights on the role of ICG in preventing bankruptcy and offers a potential research direction for ES.

Accounting information systems are mainly rule-based, and data are usually available and well-structured. However, many accounting systems are yet to catch up with current technological developments. Thus, artificial intelligence (AI) in financial accounting is often applied only in pilot projects. Using AI-based forecasts in accounting enables proactive management and detailed analysis. However, thus far, there is little knowledge about which prediction models have already been evaluated for accounting problems. Given this lack of research, our study aims to summarize existing findings on how AI is used for forecasting purposes in financial accounting. Therefore, the authors aim to provide a comprehensive overview and agenda for future researchers to gain more generalizable knowledge.

The authors identify existing research on AI-based forecasting in financial accounting by conducting a systematic literature review. For this purpose, the authors used Scopus and Web of Science as scientific databases. The data collection resulted in a final sample size of 47 studies. These studies were analyzed regarding their forecasting purpose, sample size, period and applied machine learning algorithms.

The authors identified three application areas and presented details regarding the accuracy and AI methods used. Our findings show that sociotechnical and generalizable knowledge is still missing. Therefore, the authors also develop an open research agenda that future researchers can address to enable the more frequent and efficient use of AI-based forecasts in financial accounting.

Owing to the rapid development of AI algorithms, our results can only provide an overview of the current state of research. Therefore, it is likely that new AI algorithms will be applied, which have not yet been covered in existing research. However, interested researchers can use our findings and future research agenda to develop this field further.

Given the high relevance of AI in financial accounting, our results have several implications and potential benefits for practitioners. First, the authors provide an overview of AI algorithms used in different accounting use cases. Based on this overview, companies can evaluate the AI algorithms that are most suitable for their practical needs. Second, practitioners can use our results as a benchmark of what prediction accuracy is achievable and should strive for. Finally, our study identified several blind spots in the research, such as ensuring employee acceptance of machine learning algorithms in companies. However, companies should consider this to implement AI in financial accounting successfully.

To the best of our knowledge, no study has yet been conducted that provided a comprehensive overview of AI-based forecasting in financial accounting. Given the high potential of AI in accounting, the authors aimed to bridge this research gap. Moreover, our cross-application view provides general insights into the superiority of specific algorithms.

This study aims to investigate the cross-sectional differences on the association between corporate social responsibility (CSR) and future bankruptcy along the dimensions of political connection and corporate governance strength. This study intends to provide evidence on the tangible benefits for firms to invest in social capital of CSR activities and offer insights on what firms may benefit more from CSR expenditure.

Running a logistic regression on the determinants of bankruptcy model after controlling for financial stress factors based on prior literature, this study examines the moderating effect of political connection and corporate governance on the association between corporate social responsibility and future bankruptcy.

Current study documents that the negative association between corporate social responsibility and future bankruptcy is only significant for politically connected firms, but insignificant for non-politically connected firms. Specifically, the authors find that one standard deviation increase of CSR expenditure significantly reduces the propensity of future bankruptcy by 53.20% for politically-connected firms. Conversely, the negative relation between CSR only exits for firms with weak corporate governance but do not exit for firms with strong corporate governance.

Current study provides evidence on the tangible benefits for firms to invest in social capital of CSR activities and offers additional insights on what firms may benefit more from CSR expenditure.

Current study extends the research to examine the cross-sectional variations in the negative association between CSR performance and the propensity of bankruptcy. The positive moderating effect of political connection on CSR and bankruptcy suggests that political connection and CSR are complements in reducing the propensity of future bankruptcy. A more pronounced negative association between CSR and bankruptcy for firms with weaker governance suggests that firms with weak corporate governance benefits more in engaging CSR activities than firms with strong corporate governance.

European Parliament adopted a new directive on gender balance in corporate boards when by 2026, companies must employ 40% of the underrepresented sex into non-executive directors or 33% among all directors. Therefore, this study aims to analyze the impact of gender diversity (GD) on board of directors and the shareholders’ structure and their impact on the likelihood of company bankruptcy during the COVID-19 pandemic.

The data sample consists of 1,351 companies for 2019 and 2020, of which 173 were large, 351 medium-sized companies and 827 small companies. Three bankruptcy indicators were tested for each company size, and extreme gradient boosting (XGBoost) and logistic regression models were developed. These models were then cross-validated by a 10-fold approach.

XGBoost models achieved area under curve (AUC) over 98%, which is 25% higher than AUC achieved by logistic regression. Prediction models with GD features performed slightly better than those without them. Furthermore, this study indicates the existence of critical mass between 30% and 50%, which decreases the probability of bankruptcy for small and medium companies. Furthermore, the representation of women in ownership structures above 50% decreases bankruptcy likelihood.

This is a pioneering study to explore GD topics by application of ensembled machine learning methods. Moreover, the study does analyze not only the GD of boards but also shareholders. A highly innovative approach is GD analysis based on company size performed in one study considering the COVID-19 pandemic perspective.

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.

This paper aims to propose an analysis of financial regulation practices, identified thanks to an extensive benchmark carried out in eight European professional sports leagues.

Between 1970 and 2018, 81 French football clubs went bankrupt. The paper proposes an analysis of financial regulation practices in eight European professional sports leagues to enhance the prevention of bankruptcy of French football clubs. Three research questions are addressed: What are the financial and accounting disclosure practices in the main professional leagues? What assessment tools are employed to evaluate the financial risk and budgetary feasibility? What financial support measures exist for clubs and how are insolvency proceedings initiated by clubs? To identify financial regulation practices in professional sport, a selection of leagues was made based on their economic importance, specific regulatory tools used, and their approach to financial difficulties and the handling of insolvency proceedings.

Through an examination of financial regulation practices in other leagues, three main findings are highlighted: The significance of required financial documents and deadlines varies depending on the competition organizer; some leagues utilize ratio-based assessments rather than relying solely on opinions from financial oversight bodies; certain leagues have established assistance processes for troubled clubs as opposed to punitive measures resulting in administrative regulations.

This study proposes new financial regulation modalities to prevent the bankruptcy of French football clubs. Firstly, a reform management control is suggested. Secondly, the engagement of stakeholders in bankruptcy prevention is recommended. Lastly, the implementation of a dedicated policy to support clubs facing difficulties is proposed.

The French football federation and the professional league are important actors in the European football. Many bankruptcies are noted in these championships and since the COVID crisis, the financial situation of the clubs has deteriorated, pointing to a strong risk of bankruptcy in the coming years.

In past decades, artificial neural network (ANN) models have revolutionised various stock market operations due to their superior ability to deal with nonlinear data and garnered considerable attention from researchers worldwide. The present study aims to synthesize the research field concerning ANN applications in the stock market to a) systematically map the research trends, key contributors, scientific collaborations, and knowledge structure, and b) uncover the challenges and future research areas in the field.

To provide a comprehensive appraisal of the extant literature, the study adopted the mixed approach of quantitative (bibliometric analysis) and qualitative (intensive review of influential articles) assessment to analyse 1,483 articles published in the Scopus and Web of Science indexed journals during 1992–2022. The bibliographic data was processed and analysed using VOSviewer and R software.

The results revealed the proliferation of articles since 2018, with China as the dominant country, Wang J as the most prolific author, “Expert Systems with Applications” as the leading journal, “computer science” as the dominant subject area, and “stock price forecasting” as the predominantly explored research theme in the field. Furthermore, “portfolio optimization”, “sentiment analysis”, “algorithmic trading”, and “crisis prediction” are found as recently emerged research areas.

To the best of the authors’ knowledge, the current study is a novel attempt that holistically assesses the existing literature on ANN applications throughout the entire domain of stock market. The main contribution of the current study lies in discussing the challenges along with the viable methodological solutions and providing application area-wise knowledge gaps for future studies.