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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.

Environmental, social and governance (ESG) issues have become the cornerstone of investment decisions in firms today. With that, publicly traded ESG indices (like the BSE ESG 100 index in India) have come into existence. The existing literature signifies that ESG generates financial implications and induces stability. The current study aims to test whether the firms listed on the ESG index (ESG-sensitive firms) face less financial distress than those not listed on such an index.

The study applies panel data difference-in-differences (DID) regression by considering ESG as an unstaggered treatment to 74 non-financial firms listed on India's Bombay Stock Exchanges (BSE) 100 index. In total, 42 firms are ESG treated as they got listed on the BSE ESG 100 index, formed in 2017. The remaining 32 firms form the control group. The confidence intervals and standard errors are estimated using clustered robust errors and the Donald and Lang method.

Listing on the ESG index matters for financial stability; differences in financial distress are significant on financial distress. ESG-sensitive firms face less financial distress than non-ESG firms (or firms not perceived as ESG-sensitive). The results are consistent across two financial distress measures, Altman z-scores for emerged and emerging markets. Thus, the DID in distress status between ESG-sensitive and non-ESG firms matter.

The study creates vibrant implications for practitioners using ESG to reduce financial distress.

The study is one of its kind to test the treatment effects of ESG on firm value and quantify treatment effects on financial distress.

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.

The primary objective of this research is to conceptualize the integration of enterprise risk management (ERM) as a mechanism to enhance the connection between corporate sustainability (CS) reporting and financial performance. This study suggests that future researchers should validate the proposed conceptualization by conducting a comprehensive content analysis of sustainability reports of Malaysian oil and gas companies. This analysis will allow for the collection of pertinent data regarding CS reporting and ERM implementation. The present study takes a comprehensive approach by integrating legitimacy, stakeholder, and resource-based view (RBV) theories, proposing a robust conceptual design that emphasizes the role of ERM in the connection between CS reporting and firm performance. Drawing on theoretical foundations, this study proposes that CS reporting will have a direct effect on financial performance. Moreover, the integration of ERM serves to strengthen the nexus between CS reporting and financial performance. This study offers valuable insights for stakeholders in the oil and gas sector by providing strategic guidance to enhance financial performance not only through CS reporting but also by implementing ERM. Moreover, the framework proposed in this study is expected to bring tangible and intangible benefits to corporations, including reducing information asymmetry, improving the quality of disclosure, and creating value within the field of CS. The proposed conceptual framework holds great significance as it enhances the applicability of legitimacy, stakeholder, and RBV theories, while also creating value for stakeholders through CS reporting and the adoption of risk management practices to enhance financial performance.

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.

The current study uses an advanced machine learning method and aims to investigate whether auditors perceive financial statements that are principles-based as less risky. More specifically, this study aims to explore the association between principles-based accounting standards and audit pricing and between principles-based accounting standards and the likelihood of receiving a going concern opinion.

The study uses an advanced machine-learning method to understand the role of principles-based accounting standards in predicting audit fees and going concern opinion. The study also uses multiple regression models defining audit fees and the probability of receiving going concern opinion. The analyses are complemented by additional tests such as economic significance, firm fixed effects, propensity score matching, entropy balancing, change analysis, yearly regression results and controlling for managerial risk-taking incentives and governance variables.

The paper provides empirical evidence that auditors charge less audit fees to clients whose financial statements are more principles-based. The finding suggests that auditors perceive financial statements that are principles-based less risky. The study also provides evidence that the probability of receiving a going-concern opinion reduces as firms rely more on principles-based standards. The finding further suggests that auditors discount the financial numbers supplied by the managers using rules-based standards. The study also reveals that the degree of reliance by a US firm on principles-based accounting standards has a negative impact on accounting conservatism, the risk of financial statement misstatement, accruals and the difficulty in predicting future earnings. This suggests potential mechanisms through which principles-based accounting standards influence auditors’ risk assessments.

The authors recognize the limitation of this study regarding the sample period. Prior studies compare rules vs principles-based standards by focusing on the differences between US generally accepted accounting principles (GAAP) and international financial reporting standards (IFRS) or pre- and post-IFRS adoption, which raises questions about differences in cross-country settings and institutional environment and other confounding factors such as transition costs. This study addresses these issues by comparing rules vs principles-based standards within the US GAAP setting. However, this limits the sample period to the year 2006 because the measure of the relative extent to which a US firm is reliant upon principles-based standards is available until 2006.

The study has major public policy suggestions as it responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US Securities and Exchange Commission (SEC), to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the International Accounting Standards Board (IASB) Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks such as climate change.

The study has major public policy suggestions because it demonstrates the value of principles-based standards. The study responds to the call by Jay Clayton and Mary Jo White, the former Chairs of the US SEC, to pursue high-quality, globally accepted accounting standards to ensure that investors continue to receive clear and reliable financial information as business transactions and investor needs continue to evolve globally. The study also recognizes the notable public policy implications, particularly in light of the current Chair of the IASB Andreas Barckow’s recent public statement, which emphasizes the importance of principles-based standards and their ability to address sustainability concerns, including emerging risks like climate change. The study fills the gap in the literature that auditors perceive principles-based financial statements as less risky and further expands the literature by providing empirical evidence that the likelihood of receiving a going concern opinion is increasing in the degree of rules-based standards.

With the rapid growth of the domestic lending industry, assessing whether the borrower of each loan is at risk of default is a pressing issue for financial institutions. Although there are some models that can handle such problems well, there are still some shortcomings in some aspects. The purpose of this paper is to improve the accuracy of credit assessment models.

In this paper, three different stages are used to improve the classification performance of LSTM, so that financial institutions can more accurately identify borrowers at risk of default. The first approach is to use the K-Means-SMOTE algorithm to eliminate the imbalance within the class. In the second step, ResNet is used for feature extraction, and then two-layer LSTM is used for learning to strengthen the ability of neural networks to mine and utilize deep information. Finally, the model performance is improved by using the IDWPSO algorithm for optimization when debugging the neural network.

On two unbalanced datasets (category ratios of 700:1 and 3:1 respectively), the multi-stage improved model was compared with ten other models using accuracy, precision, specificity, recall, G-measure, F-measure and the nonparametric Wilcoxon test. It was demonstrated that the multi-stage improved model showed a more significant advantage in evaluating the imbalanced credit dataset.

In this paper, the parameters of the ResNet-LSTM hybrid neural network, which can fully mine and utilize the deep information, are tuned by an innovative intelligent optimization algorithm to strengthen the classification performance of the model.

The purpose of the study is to investigate the impact of asset redeployability on the level of corporate cash holdings.

The authors use regression analysis to examine the relation between asset redeployability and corporate cash holdings.

Using a large panel sample of US public firms from 1990 to 2020, the authors find a significant positive relation between asset redeployability and cash, which suggests that firms with more redeployable assets hold more cash.

The authors contribute to a growing literature in accounting and finance that investigates the impact of asset redeployability on firm characteristics and also contribute to the literature on the determinants of cash holdings.

The case was developed from secondary sources and interviews with a security analyst. The secondary sources include company annual reports, news reports, analyst reports, industry reports, company websites, stock exchange websites and databases such as Bloomberg and CMIE Prowess.

Increasing competition in product and capital markets has put tremendous pressure on managers to become more cost competitive. To address their firms' uncompetitive cost structures, managers may have to consider dramatic restructuring of their businesses. During 2014–2017, Tata Steel Ltd (TSL) UK considered a series of divestitures and a merger plan to nurse the company back to health. The case considers the economics of the restructuring plan. The case is designed to help students analyze a corporate downsizing program undertaken by a large Indian company in the UK and to highlight the dynamic role of the CFO and governance issues in family firms. It introduces students to issues surrounding a typical restructuring and provides students a platform to practice the estimation of value creation in a restructuring exercise. While some cases on corporate restructuring in the context of developed economies are available, there are very few cases written in an emerging market context. This case bridges that gap. TSL presents a unique opportunity to study corporate restructuring necessitated by a failed cross-border acquisition. It illustrates the potential for value loss in large, cross-border acquisitions. It shows how managerial hubris can prompt family firm owners to overbid in acquisitions and create legacy hot spots. In addition, the case can be used to discuss the causes of governance failures such as weak institutional monitoring and poor legal enforcement in emerging markets that could potentially harm minority shareholders.

The case was developed from secondary sources and interviews with a security analyst. The secondary sources include company annual reports, news reports, analyst reports, industry reports, company websites, stock exchange websites and databases such as Bloomberg and CMIE Prowess.

This case was researched using publicly available sources, including Mercury Systems financial filings and press releases, news stories about the seasoned equity offering, financial information from Bloomberg and industry information from IBISWorld Industry Reports and articles related to seasoned/secondary equity offerings, intangible asset valuation and the use of revolving lines of credit. Quotes are taken from Mercury financial reports and press releases and express the (optimistic) opinions of company executives.

Mercury Systems, a technology company in the aerospace and defense industry, announced a six million share seasoned stock offering in June 2019. This resulted in a 6% stock price decrease. A stock price decrease is a typical event when a firm announces the issuance of new common shares, but with Mercury Systems, there were concerns about how much money the firm needed to fund its strategy of growth through acquisitions. If internally generated funds were not sufficient, should the firm issue debt or have another seasoned equity issue? Students will look at the objectives and success of the most recent seasoned equity issue, determine future funds needs and how the firm should finance these needs.

This case is appropriate for undergraduate and graduate students in corporate finance electives. Typically, topics such as seasoned equity offerings are not covered in introductory courses, so this is recommended for finance electives. Even in advanced finance courses, sometimes there is insufficient time to cover seasoned equity offerings.