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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.

This paper aims to synthesize the diverse literature on nomination and remuneration committees and provide avenues for future research.

This study provides a comprehensive literature review of theoretical and empirical studies published in reputable international journals indexed by Scopus.

The literature review reveals several aspects of the nomination and remuneration committee. These aspects have been classified into the definition of the nomination and remuneration committee, dimensions of the nomination and remuneration committee, measurement and research review results, reasons for conflict empirical findings, company dynamics and research on moderators, as well as recommending future research.

Our literature review shows that nomination and remuneration committees play a role in improving board performance and company performance, reducing agency conflicts and improving corporate governance to provide implications for companies, regulators and investors and pave the way for future research.

This paper identifies issues related to nomination and remuneration committees, their theoretical and practical implications and avenues for future research.

This study aims to explore how earnings management techniques are affected by corporate financial debt risk (FDR), internal control (IC) effectiveness and CEO education.

The study uses a sample from listed firms in China from 2010 to 2017, comprising different industries, including agriculture, forestry, livestock farming and fishing; mining; manufacturing; electric power, gas and water production and supply; construction; transport and storage; information technology; the real estate industry; social services; and communication and cultural. The regression analysis is used to test the hypotheses. The two-stage least squares technique is used to check for endogeneity issues.

The study finds that firms are less likely to manage real earnings when they have more robust IC and FDR. Likewise, companies with weak ICs are more likely to manipulate real earnings. Besides, the study finds an influence of CEO education on the relationship between IC, FDR and real earnings management (REM). These results can be applied to the sectors in the sample covered by the research, and the authors do not overlook the energy industry sector for the importance of its role in the economy.

There are some limitations for the researcher when performing any research, and this study is no exception. Researchers are urged to take these circumstances into consideration when generalizing or comparing the results because the methods used to calculate the measurement variables in each study may differ somewhat from those used in other research. In addition, expanding the current research design to incorporate additional nations may be an area of interest for future research and could aid in evaluating the effects of nation-specific elements (such as inflation, culture, legal systems and political considerations) on the usefulness of IC and decreasing FDR. Second, the current study focuses on the impact of IC and FDR on REM; this paper does not dissect the “black box” of IC and consider how each element affects earnings management. Future research may need to focus specifically on how effective IC would affect earnings management and precisely what IC mechanisms would discourage the management of earnings.

Helping companies listed in China to make decisions and improve investors’ vision of the results of real companies’ businesses, as well as helping management to avoid falling into debt risk and the consequent effects and manipulation of earnings.

By highlighting the significance of IC and debt risk in enhancing information quality in China, the results contribute to the body of work examining the relationship between IC, FDR and REM. In addition, this study uses a CEO’s education to moderate this link.

The purpose of this paper is to examine the impact of bankruptcy reform in the year 2016 on the relation between leverage and firm performance for Indian firms, separately for business group and standalone firms.

Fixed effects panel regression is used to understand the role of bankruptcy reform on firm-level data to examine the relationship between leverage and firm performance after controlling for size, growth, age, liquidity and promoter shareholding. The authors also apply the generalized method of moments (GMM) to control for the endogeneity concerns.

The authors show that the introduction of the insolvency and bankruptcy code (IBC) positively moderates the relation between leverage and firm performance such that the extent of negative relation between leverage and firm performance is less in the post-IBC period. The positive impact of IBC on the relation between leverage and firm performance holds only for firms not affiliated to business groups and for firms with higher debt in their capital structure.

The study’s findings will help the regulators appreciate the effectiveness of bankruptcy reforms resulting from IBC implementation in terms of sound bankruptcy process and leading to safeguard the interests of minority shareholders.

The authors provide the only study to examine the role of bankruptcy law in moderating the relation between leverage and firm performance across a sample of business group and standalone firms.

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.

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 examine the impact of intellectual capital (IC) on default risk in Indian companies listed on the National Stock Exchange.

This study applies panel data regression analysis to derive a relationship between IC and default risk for the sample period 2013–2022. The value-added intellectual coefficient (VAIC) of Pulic (2000) has been applied to measure IC performance, and default risk is estimated using the revised Z-score model of Altman (2000).

The results revealed a positive association between Z-score and VAIC. It implies that a higher value of VAIC improves financial stability and leads to a lower likelihood of default. The findings further suggest that new default forecasting models can be experimented with IC indicators for better default prediction.

The findings can have implications for investors and banks. This paper provides evidence of IC performance in improving the financial solvency of firms. Investors and financial institutions should invest their resources in a healthy firm that effectively manages and invests in their IC. It will eventually award investors and creditors high returns through efficient value-creation processes.

This study provides evidence of IC performance in improving the financial solvency of Indian high-defaulting firms, which lacks sufficient evidence in this domain of research. Numerous studies exist examining the relationship between firm performance and IC value, but this area is inadequately focused and underresearched. This study, therefore, fills the research gap from an Indian perspective.

This study aims to analyze the effect of cognitive load and social value orientation on managers’ preferences when they face with two types of restructuring choices in financially distressed firms: the first belonging to the family of organizational restructuring (massive layoffs) and the second to the family of financial restructuring (debt increases).

The authors investigate experimentally the impact of managers’ cognitive load and social value orientation on the decision to restructure leveraged buyout (LBO) firms in financial distress by using either massive layoffs or debt increases.

By investigating the impact of managers’ cognitive load and social value orientation on the restructuring decision of an LBO firm in financial distress, the research reveals that, on average, cognitively loaded managers prefer massive layoffs over increased debt levels. The massive layoffs seemingly provide a relatively easier way to avoid conflict with influential, residual claimants. In contrast, social value–oriented managers actively avoid massive layoffs and prefer to increase debt.

These results imply that the performance mechanisms emphasized to improve agency relations, for example, in LBOs, have their own limitations during periods of financial distress. This study shows that one of these limits is related to cognitive distortions and personality traits.

In this research, the originality lies in understanding how managers’ internal factors affect their restructuring decision-making, in the case of LBO firms in financial distress.

This research aims to examine the moderating role of the existence of risk management committee between risk-taking behavior and companies’ performance.

Research sample includes 383 manufacturing company-year that listed on the Indonesian Stock Exchange period of 2017–2020. The risk-taking behavior includes the use of leverage, capital intensity, research and development intensity, and earnings uncertainty. The hypothesis test uses company fixed-effect regression.

The result shows that risk management committee moderates the effect of risk-taking behavior on companies’ performance. This research also finds the similar result when risk management committee and risk-taking behavior are examined on the future performance. In the further analysis, the result also finds that the expertise of risk management committee moderates the effect of risk-taking behavior on companies’ performance.

This research contributes to fill the previous gap of risk-taking behavior and companies’ performance by considering the existence of risk management committee to promote oversight role on risk-taking behavior. This research also contributes to give new evidence in Indonesia about the role of risk management committee to improve the benefits or to reduce the costs of risk-taking behavior.

The behavioural theory adds a new paradigm of analysis with the assumptions of the decision maker’s cognitive biases and their repercussions on financing decisions. The aim of the study is to analyse the repercussions of these biases on the adjustment speed of firm’s capital structure toward the optimal level.

Based on a partial adjustment model, the study uses the Dynamic Panel Fractional estimator to analyse panel data from 4,990 Portuguese entrepreneurial firms.

The results show that the cognitive overconfidence bias impacts the entrepreneurial firm’s capital structure. In fact, the firms run by overconfident managers adjust more slowly than their counterparts. Furthermore, the findings suggest that entrepreneurial firms make relatively fast adjustments toward the optimal debt level and follow a hierarchical financing order in the funding process.

The results of this paper are not only interesting to the academia, but also contain practical implications for corporate, institutional and business policy and governance. First, the paper introduces a new measure of cognitive bias in optimistic managers, which is useful for current and future academic research. Also, in practical terms, the findings of the paper reveal that when a company is contemplating hiring a manager, it should consider whether they need an optimistic or non-optimistic manager based on the company's present life cycle or situation.

The current analysis extends the existing literature. The study suggests that financial classical and behavioural paradigms should not be separated, which can provide evidence to help narrow the gap between these two major perspectives.