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Financial ratios are often used to classify firms into different clusters of financial performance. This study aims to classify firms using financial ratios with advanced techniques and identify the transition matrix of firms moving clusters during the COVID-19 period.

This study uses compositional data (CoDa) analysis based on existing clustering methods with transformed data by weighted logarithms of financial ratios. The data include 66 listed firms in Vietnam’s food and beverage and fishery sectors over a three-year period from 2019 to 2021, including the COVID-19 period.

These firms can be classified into three clusters of distinctive characteristics, which can serve as benchmarks for solvency and profitability. The results also show the migration from one cluster to another during the COVID-19 pandemic, allowing for the calculation of the transition probability or the transition matrix.

The findings indicate three distinct clusters (good, average and below-average firm performance) that can help financial analysts, accountants, investors and other strategic decision-makers in making informed choices.

Clustering firms with their financial ratios often suffer from various limitations, such as ratio choices, skewed distributions, outliers and redundancy. This study is motivated by a weighted CoDa approach that addresses these issues. This method can be extended to classify firms in multiple sectors or other emerging markets.

This paper aims to assess the rank of Shariah compliance (SC) and its impact on the financial performance of non-financial companies listed on the Saudi Stock Exchange. It seeks to understand the relationship between adherence to Shariah principles and the financial success of these companies, providing insights into the importance of SC in the Saudi Arabian context.

The study adopts a quantitative research approach, using financial and SC data from non-financial companies listed on the Saudi Stock Exchange. SC is measured using the Accounting and Auditing Organization for Islamic Financial Institutions standards. Financial performance is evaluated using various financial indicators, including return on assets (ROA), return on equity (ROE) and return on investments (ROI). Statistical analysis, including regression analysis, is conducted to examine the relationship between SC and financial performance.

The findings indicate a positive association between SC and financial performance in non-financial companies listed on the Saudi Stock Exchange. Companies with higher ranks of SC demonstrate superior financial performance, as evidenced by higher ROA, ROE and ROI. This suggests that adhering to Shariah principles can contribute to improved financial outcomes for companies operating in the Saudi Arabian market.

The study highlights the practical implications of maintaining SC for non-financial companies in Saudi Arabia. It emphasizes the importance of aligning business practices with Shariah principles to enhance financial performance. The findings suggest that companies can benefit from implementing Shariah-compliant strategies and practices, potentially attracting investors and improving their overall competitiveness in the market.

The social implications of SC in the Saudi Arabian context are significant. Adhering to Shariah principles not only ensures compliance with religious and cultural norms but also promotes ethical and responsible business behaviour. Companies that prioritize SC contribute to the development of a socially responsible and sustainable business environment.

To the best of the authors’ knowledge, this study represents the first investigation into the impact of SC rank on financial performance. By examining non-financial companies listed on the Saudi market, it contributes significantly to existing literature by providing empirical evidence supporting a positive correlation between SC rank and financial outcomes. The findings offer valuable insights for companies, investors and policymakers in Saudi Arabia, enhancing their understanding of the unique dynamics between SC rank and financial performance. This research enriches the body of knowledge in Islamic finance and business, making a notable contribution to the field and opening avenues for further exploration.

This research aims to examine the relations between Shariah compliance and earnings quality.

The authors study three Shariah features: Shariah compliance status, level of Shariah compliance (H-Score) and Shariah compliance persistence. The sample consists of 463 firms from the Middle East and North Africa from 2011 to 2018. A variable determining the level of Shariah compliance was created in accordance with the methodology of S&P 500 Shariah and its underlying index, S&P 500. Then, a probate relapse study was created to identify the link between Shariah compliance and earnings quality.

Results show that Shariah-compliant firms engage in lower earnings management compared to their Shariah-non-compliant counterparts. This paper reveals that Shariah compliance status and high level of Shariah compliance have significant positive association with earnings quality. The authors also find novel evidence that persistence of the Shariah-compliant status has a significant negative association with earnings quality.

This study only examines firms listed on MENA stock markets. It is recommended to further study different markets in addition to the emerging Arab markets in order to compare and contrast the results. Further, larger sample observations from a greater date range can be used.

Few studies have examined the earnings management behavior of Shariah-compliant firms vs Shariah-non-compliant ones in emerging markets; however, no study has focused on Shariah-compliant firms and their level of Shariah compliance. To the best of our knowledge, this is the first study which uses all four proxies for earnings quality in association with Shariah compliance and used new Shariah variables such as Level of Shariah Compliance and Persistent Shariah Compliance status.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

This paper aims to investigate the effect of CEO social media celebrity status on credit ratings and to determine whether potential threats on the CEO celebrity status negatively moderate the above association.

The authors collected tweets for 874 CEOs from 513 unique S&P 1500 firms. A panel data analysis was conducted on a panel with 4,235 observations from 2009 to 2020. We then tested the hypothesis with the ordinal logit model.

The empirical findings confirmed that CEO social media celebrity status is positively associated with corporate credit rating outcomes. Our path analyses revealed that CEOs with higher social media celebrity status have less incentive to conduct risk-taking behaviors and thus benefit credit ratings. When the rating agencies perceive potential threats to CEO celebrity status, including CEO myopia and CEO overconfidence, the association between CEO social media celebrity status and credit rating is weakened.

This study provides an in-depth understanding of CEO social media perception on credit ratings for firms' managers and capital market participants. Findings can help managers and firms improve their strategies for leveraging social media to release credit constraints. The debt market participants could adopt the CEO social media celebrity status and its concerned threats to setting debt contracts with an adequate price.

This is likely to be the first study that examines the effect of CEO social media celebrity status on credit ratings. The findings of this study also reveal that social media certificated celebrity CEOs tend to be capable of enhancing firm revenue and have lower risk-taking incentives, unlike mass media certificated celebrity CEOs.

While the Chinese securities market is booming, the phenomenon of listed companies falling into financial distress is also emerging, which affects the operation and development of enterprises and also jeopardizes the interests of investors. Therefore, it is important to understand how to accurately and reasonably predict the financial distress of enterprises.

In the present study, ensemble feature selection (EFS) and improved stacking were used for financial distress prediction (FDP). Mutual information, analysis of variance (ANOVA), random forest (RF), genetic algorithms, and recursive feature elimination (RFE) were chosen for EFS to select features. Since there may be missing information when feeding the results of the base learner directly into the meta-learner, the features with high importance were fed into the meta-learner together. A screening layer was added to select the meta-learner with better performance. Finally, Optima hyperparameters were used for parameter tuning by the learners.

An empirical study was conducted with a sample of A-share listed companies in China. The F1-score of the model constructed using the features screened by EFS reached 84.55%, representing an improvement of 4.37% compared to the original features. To verify the effectiveness of improved stacking, benchmark model comparison experiments were conducted. Compared to the original stacking model, the accuracy of the improved stacking model was improved by 0.44%, and the F1-score was improved by 0.51%. In addition, the improved stacking model had the highest area under the curve (AUC) value (0.905) among all the compared models.

Compared to previous models, the proposed FDP model has better performance, thus bridging the research gap of feature selection. The present study provides new ideas for stacking improvement research and a reference for subsequent research in this field.

This study mainly explores how ESG performance (ESG stands for Environment, Social, and Governance) affects corporate downside risk through innovation input and innovation output, thereby promoting sustainable development of enterprises.

Using Chinese A-share listed companies from 2014 to 2022 as research samples, a stepwise regression method is used to empirically test the impact of ESG performance on corporate innovation and downside risk by constructing multiple multivariate primary regression models.

ESG performance is beneficial for obtaining external resources and alleviating principal-agent problems. It can promote enterprises to increase innovation input and improve innovation output, thereby enhancing their core competitiveness, and suppressing their downside risk. This inhibitory effect is more significant in non-state-owned enterprises, non-high-tech enterprises, and enterprises where the chairman and the general manager are not combined in one. Further additional analysis has found that equity concentration weakens the inhibitory effect of ESG performance on corporate downside risk, equity balance strengthens the inhibitory effect of ESG performance on corporate downside risk, indicating that a mutually restrictive equity structure is conducive to promoting enterprises to actively fulfill ESG responsibility, thereby improving corporate innovation level and resolving their downside risk.

Enterprise managers, policy makers, and other practitioners can clearly see the benefits of implementing ESG measures, further strengthen their confidence in sustainable development, actively apply ESG concepts to the entire production and operation process of enterprises, increase attention and implementation of ESG elements, and promote the healthy and vigorous development of enterprises and macroeconomics.

The research conclusions reveal the inherent mechanism by which ESG performance empowers enterprises to improve their innovation level and reverse their performance decline, effectively expanding the theoretical achievements of ESG performance in enterprise innovation and risk management.

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 present a model for detecting and predicting creative accounting in companies listed on the Tehran Stock Exchange (TSE).

The authors conduct this research in three stages. First, the authors review the literature to determine the dimensions, components, indicators and techniques of creative accounting. Second, the authors conduct semi-structured interviews with experts using the fuzzy Delphi technique to obtain screening and reach a consensus. Finally, the authors develop a model to predict creative accounting by classifying the financial statements of the sample companies into two groups based on the use or non-use of creative accounting techniques, measuring the indicators determined in the previous stage, running various machine learning algorithms and choosing the superior algorithm.

The results indicate the usefulness of accounting information for detecting and predicting creative accounting and the relevance of several financial attributes as important predictors. The results also indicate the superiority of extremely randomized trees over other algorithms in predicting creative accounting and suggest that the primary purpose of creative accounting in Iran is earnings management. Contrary to the political cost hypothesis, large Iranian companies use creative accounting to inflate profits.

The present research also has several limitations that must be considered, and caution must be exercised in interpreting and generalizing the findings as specified in the revised manuscript.

This study’s implications are significant for policymakers, standard-setters and practitioners. By recognizing the detrimental effects of creative accounting on financial transparency within companies, policymakers can address existing gaps in accounting standards to minimize the potential for earnings manipulation. Consequently, strengthening internal and external mechanisms related to a firm’s financial performance becomes achievable. The study provides evidence of the need for audit firms to recognize the importance of creative accounting and consider creative accounting in their audit plans to prevent insufficient or even misleading disclosure by companies that extensively use creative accounting practices in their financial reporting. Moreover, knowledge of creative accounting techniques can help auditors assess audit and detection risks and serve as a valuable guide for reducing audit costs and improving audit quality.

Given that creative accounting practices distort the true or real accounting results, curbing creative accounting practices reduces corporate failures and could lead to the reduction of job losses and other social consequences.

This study uses a unique database in Iran to determine a model for predicting creative accounting using a mixed-method methodology, qualitative and quantitative, to identify creative accounting techniques and run various machine learning algorithms.

This study aims to examine whether there are differences between financial statements prepared in accordance with International Financial Reporting Standards (IFRS) and financial statements prepared in accordance with local accounting standards in terms of its ability to present the financial conditions of companies listed on the Saudi Stock Exchange as one of the emerging markets.

Data on study variables were obtained from the published financial statements of 67 of listed companies in the Saudi Stock Exchange during the period 2014–2019. The study addressed the research hypotheses by using Altman Z-score model. Both the T-test and Wilcoxon rank test were used to investigate the significance of differences between the values of Z-score and the individual variables included in the model in the pre- and post-IFRS mandatory adoption periods.

The results revealed a decrease in the values of Z-score as well as the values of the individual variables included in the model in the period following the adoption of IFRS than it was before the adoption of IFRS, which indicates the ability of IFRS to show the financial conditions of companies more transparently than local accounting standards. However, the results of the T-test and Wilcoxon test showed that these decreases were not statistically significant.

This study has some limitations, including the small sample size as a result of the small size of the Saudi Stock Exchange, As well as the reliance of this study only on the Altman model with its five variables in assessing financial conditions without examining the impact of other factors that may affect the financial conditions of companies.

Financial conditions of the companies have important implications for multiple parties such as management, government, investors and others as an early warning sign that enables them to take the necessary measures early before the actual bankruptcy occurs and what results in costs.

Although assessing financial conditions of the companies is one of the basic uses of accounting information, this topic has not received sufficient attention as a means to test the benefits of adopting IFRS, especially in emerging markets such as Saudi Stock Exchange. This is the first study to examine the impact of adopting IFRS on the transparency of financial reporting in assessing financial conditions in Saudi Arabia.