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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.

The goal is to investigate the relationship between financial performance and environmental, social and governance (ESG) indicators and disclosures for a sample of Latin American firms.

Dynamic panel data regressions are used to analyze a sample of 114 companies listed on the Latin American Integrated Market, MILA (Chile, Colombia, Mexico and Peru) for the period 2011–2020. The Altman Z-score and Piotroski F-score are used as indicators of the probability of default and comprehensive financial strength. Models are developed in which the relationship between economic value added (EVA) and Jensen’s alpha are evaluated against firms’ ESG practices.

A direct relationship between ESG strategies and financial performance was found. Better practices and transparency in ESG are related to lower probability of bankruptcy, greater financial strength, greater EVA and superior risk-adjusted returns.

ESG data were obtained from the Bloomberg system based on a methodology that may differ from other sources. The sample covers four Latin American countries and large corporations. Independent variables were selected for their perceived validity, given their frequent use in previous studies.

Evidence for company management regarding the importance of strengthening ESG practices and reporting should be part of their balanced scorecards. For investors, the results support the importance of evaluating ESG practices in asset selection.

The present study is the first research to present empirical evidence on the relationship between ESG scores and disclosures for MILA countries, using a comprehensive set of financial performance indicators (Altman Z-scores, Piotroski F-scores, EVA and Jensen’s alpha).

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.

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.

The crux of this paper is to unveil efficient features and practical tools that can predict credit default.

Annual data of non-financial listed companies were taken from 2000 to 2020, along with 71 financial ratios. The dataset was bifurcated into three panels with three default assumptions. Logistic regression (LR) and k-nearest neighbor (KNN) binary classification algorithms were used to estimate credit default in this research.

The study’s findings revealed that features used in Model 3 (Case 3) were the efficient and best features comparatively. Results also showcased that KNN exposed higher accuracy than LR, which proves the supremacy of KNN on LR.

Using only two classifiers limits this research for a comprehensive comparison of results; this research was based on only financial data, which exhibits a sizeable room for including non-financial parameters in default estimation. Both limitations may be a direction for future research in this domain.

This study introduces efficient features and tools for credit default prediction using financial data, demonstrating KNN’s superior accuracy over LR and suggesting future research directions.

This study aims to establish a robust evaluation framework for suppliers within the automotive supply chain, specifically in the stamping sector. The primary objectives are to elucidate the performance criteria of suppliers, identify indicators and scales for measuring these criteria and find the importance of the criteria.

The evaluation framework comprises a criteria hierarchy and indicators developed based on the evaluation criteria of major automotive manufacturers. Specific indicators and measurement scales are recommended for assessing suppliers. Importance weights for the criteria are assigned based on the input of nine experts using the Analytic Hierarchy Process (AHP). Finally, four sheet metal stamping tooling (SMST) suppliers are evaluated by four specialists using the proposed evaluation framework.

The study introduces a novel classification of criteria, encompassing financial and commercial perspectives, delivery capability, supplier facility and cultural approaches and business process necessities. The findings underscore the significance of financial and commercial stability in the selection of SMST suppliers, emphasizing their role in mitigating risks associated with disruptions, bankruptcies and unforeseen events. Additionally, several SMST evaluation factors identified in this study contribute to the development of resilience capabilities, highlighting the crucial importance of their inclusion and assessment in the proposed evaluation framework.

This research presents a comprehensive model for evaluating SMST suppliers, which tackles the multidisciplinary challenges within the automotive supply chain. Given the inadequacy or nonexistence of current SMTS selection models, this study bridges the gap by exploring potential and necessary criteria, alongside 116 specific indicators and measurement scales.

This study aims to present an innovative predictive methodology that transitions from traditional efficiency assessment techniques to a forward-looking strategy for evaluating working capital management(WCM) and its determinants by integrating data envelopment analysis (DEA) with artificial neural networks (ANN).

A slack-based measure (SBM) within DEA was used to evaluate the WCME of 1,388 firms in the Indian manufacturing sector across nine industries over the period from April 2009 to March 2024. Subsequently, a fixed-effects model was used to determine the relationships between selected determinants and WCME. Moreover, the multi-layer perceptron method was applied to calculate the artificial neural network (ANN). Finally, sensitivity analysis was conducted to determine the relative significance of key predictors on WCME.

Manufacturing firms consistently operate at around 50% WCME throughout the study period. Furthermore, among the selected variables, ability to create internal resources, leverage, growth, total fixed assets and productivity are relatively significant vital predictors influencing WCME.

The integration of SBM-DEA and ANN represents the primary contribution of this research, introducing a novel approach to efficiency assessment. Unlike traditional models, the SBM-DEA model offers unit invariance and monotonicity for slacks, allowing it to handle zero and negative data, which overcomes the limitations of previous DEA models. This innovation leads to more accurate efficiency scores, enabling robust analysis. Furthermore, applying neural networks provides predictive insights by identifying critical predictors for WCME, equipping firms to address WCM challenges proactively.

Under the trend of global economic integration and the new context of stagflation, frequent fluctuations in international interest rates are exerting far-reaching impacts on the world economy. In this paper, the transmission mechanism of the impact of fluctuations in international interest rates (specifically, the American interest rate) on the bankruptcy risk in China's pillar industry, the construction industry (which is also sensitive to interest rates), is examined.

Using an improved contingent claims analysis, the bankruptcy risk of enterprises is calculated in this paper. Additionally, an individual fixed-effects model is developed to investigate the mediating effects of international interest rates on the bankruptcy risk in the Chinese construction industry. The heterogeneity of subindustries in the industrial chain and the impact of China's energy consumption structure are also analysed in this paper.

The findings show that fluctuations in international interest rates, which affect the bankruptcy risk of China's construction industry, are mainly transmitted through two major pathways, namely, commodity price effects and exchange rate effects. In addition, the authors examine the important impact of China's energy consumption structure on risk transmission and assess the transmission and sharing of risks within the industrial chain.

First, in the research field, the study of international interest rate risk is extended to domestic-oriented industries. Second, in terms of the research content, this paper is focused on China-specific issues, including the significant influence of China's energy consumption structure characteristics and the risk contagion (and risk sharing) as determined by the current development of the Chinese construction industry. Third, in terms of research methods a modified contingent claim analysis approach to bankruptcy risk indicators is adopted for this study, thus overcoming the problems of data frequency, market sentiment and financial data fraud, which are issues that are ignored by most relevant studies.

The purpose of this study is to investigate the impact of customer concentration on the provision of reverse trade credit at the firm level.

Utilizing unbalanced panel data of Chinese A-share listed firms from 2007 to 2022 as the study sample, this paper employs a fixed-effects model to investigate the association between customer concentration and firms’ reverse trade credit.

This study finds that firms with higher customer concentration receive less reverse trade credit. Heterogeneity tests reveal a significant amplification of reverse trade credit in high-tech firms but a detrimental impact in large-sized, competitive and high-analyst-following firms. Further studies conclude that firms’ motivations, including bargaining power, financing and transaction guarantee motivations, collectively influence the extent of reverse trade credit acquisition.

To our knowledge, this paper represents the first attempt to conduct a comprehensive investigation of reverse trade credit, specifically through the lens of customer concentration, utilizing firm-level panel data sourced from a singular country.

This study aims to analyze notable distribution dispute cases from Islamic law history. The authors will assess these alongside resolutions proposed by historical authorities, some of which evolved into established Islamic case law. In addition, the authors intend to apply classic fair division rules to these cases, providing alternative solutions. Using a game-theoretical approach, the authors plan to compare Islamic solutions with traditional division rules through axiomatic analysis. The goal of this study is to systematically explore the unique principles underpinning Islamic distributions.

In this study, the authors collate Islamic inheritance law disputes involving conflicting claims, unresolvable by primary Islamic law sources, from historical and modern texts. The authors formally model these as claims problems, surplus-sharing problems and adapted claims problems. Concurrently, the authors gather the proposed solutions and historical backgrounds offered by the era’s authorities and jurists. These solutions are axiomatically generalized into rules, while the axioms characterizing distribution rules are checked if they are aligned with Islamic norms and values. This approach facilitates a comparison between Islamic distributions and classic division rules.

The 'Awl and Radd doctrines, used in Islamic inheritance law, are axiomatically equivalent to the Proportional Rule, a prevalent non-Jewish division rule. These doctrines present solutions impervious to manipulation by legal heirs through rights transfer, unlike other possible distributions. Ibn 'Abbas' solution for Awliyya cases uses sequential priorities and diverges uniquely from classic fair division rules in the literature. In addition, it is established that Abu Yusuf's (b. 729) distribution for a legal dispute is axiomatically identical to Abraham ibn Ezra's (b. 1089) division rule.

There is a noticeable dearth of comprehensive studies investigating contentious disputes concerning resource claims within Islamic law. Many of these studies are lacking in-depth analyses of diverse cases, casting doubts on their reliability. As a result, a robust focus is needed on case collection prior to any analytical process. Future research should concentrate on collating instances of fair division problems throughout Islamic history, as well as separately collecting methods of Islamic sharing. This procedure may lead to the characterization of various Islamic regulations, thereby emphasizing distinct Islamic principles. In forthcoming studies, conducting an exhaustive axiomatic evaluation of the cases and proposed resolutions is imperative.

This research illuminates existing knowledge gaps, setting a course for novel research trajectories. It underlines the fair division literature’s oversight of disputes within Islamic law, despite the plentiful existence of contentious cases. The research underscores the relevance of cooperative game theory as a tool for dissecting Islamic legal disputes. By accounting for unique Islamic norms and principles, this study lays a foundation for a nuanced comprehension of the dynamics and outcomes of legal disputes. By integrating an interdisciplinary approach, this research strives to bridge the gap between game theory and Islamic law.

Beyond addressing a significant research lacuna, this study carries extensive societal implications. By shedding light on enduring debates within Islamic law, it encourages a rejuvenated understanding of the evolution and interpretation of legal disputes. The axiomatic disparities between rulers’ and jurists’ methods provide invaluable insights within the Islamic context, bolstering the understanding of sociocultural dynamics that influence legal decision-making. This research has the potential to shape legal discourse, guide policymaking and spur scholarly, juristic and societal dialogue. Consequently, it may foster a more comprehensive and enlightened approach toward the resolution of legal disputes in Islamic law.

To the best of the authors’ knowledge, this study is the first to examine Islamic law’s historical legal disputes from a game-theoretical standpoint. Existing studies rarely collect distribution disputes systematically, and none scrutinize the axiomatic rationales underlying authorities’ and jurists’ distributions, opting instead to focus on historical backgrounds. While the fair division literature extensively examines disputes, it often overlooks those originating from Islamic law, which presents a rich source of disputes that can be modeled as fair division problems. This research makes a distinct contribution by incorporating disputes from Islamic law into the existing body of cooperative game theory literature.