Search results

The novel coronavirus (COVID-19) has caused financial stress and limited their lending agility, resulting in more non-performing loans (NPLs) and lower performance during the II wave of the coronavirus crisis. Therefore, it is essential to identify the risky factors influencing the financial performance of Indian banks spanning 2018–2022.

Our sample consists of a balanced panel dataset of 75 scheduled commercial banks from three different ownership groups, including public, private and foreign banks, that were actively engaged in their operations during 2018–2022. Factor identification is performed via a fixed-effects model (FEM) that solves the issue of heterogeneity across different with banks over time. Additionally, to ensure the robustness of our findings, we also identify the risky drivers of the financial performance of Indian banks using an alternative measure, the pooled ordinary least squares (OLS) model.

Empirical evidence indicates that default risk, solvency risk and COVAR reduce financial performance in India. However, high liquidity, Z-score and the COVID-19 crisis enhance the financial performance of Indian banks. Unsystematic risk and systemic risk factors play an important role in determining the prognosis of COVID-19. The study supports the “bad-management,” “moral hazard” and “tail risk spillover of a single bank to the system” hypotheses. Public sector banks (PSBs) have considerable potential to achieve financial performance while controlling unsystematic risk and exogenous shocks relative to their peer group. Finally, robustness check estimates confirm the coefficients of the main model.

This study contributes to the knowledge in the banking literature by identifying risk factors that may affect financial performance during a crisis nexus and providing information about preventive measures. These insights are valuable to bankers, academics, managers and regulators for policy formulation. The findings of this paper provide important insights by considering all the risk factors that may be responsible for reducing the probability of financial performance in the banking system of an emerging market economy.

The empirical analysis has been done with a fresh perspective to consider unsystematic risk, systemic risk and exogenous risk (COVID-19) with the financial performance of Indian banks. Furthermore, none of the existing banking literature explicitly explores the drivers of the I and II waves of COVID-19 while considering COVID-19 as a dependent variable. Therefore, the aim of the present study is to make efforts in this direction.

This study aims to investigate whether auditors compromise their independence for economically important clients in countries with a secrecy culture.

The authors empirically examine the research question based on a data set of 33 countries for the period from 1995 to 2018. The dependent variable is the auditors’ propensity to issue modified audit opinions, which is a proxy for auditor independence. The authors use relative client size as a proxy for client importance. The authors adopt the Heckman (1979) two-stage model to mitigate the potential endogeneity issue involved in the selection of Big-N auditors.

Using a large sample of firms and controlling for the firm- and country/region-level factors, this study reveals that both Big-N and non-Big-N auditors are more likely to issue modified audit opinions to clients located in countries with a strong secrecy culture relative to those located in other countries. However, Big-N auditors are more likely to issue modified audit opinions for their economically important clients with a secrecy culture relative to their other clients, while no or weaker evidence is found for non-Big-N auditors. The results are consistent and robust to endogeneity tests and sensitivity analyses.

This study enriches the literature by providing a new perspective on auditor independence that an auditor’s reporting behavior can vary depending on the client’s importance and auditor type, even under the same secrecy culture.

The purpose of this study is to analyze the effect of off-balance sheet activities on the credit risk of African banks.

The theory about the relationship between off-balance sheet activities and bank risk was used to construct a conceptual model of the effect of off-balance sheet on credit risk in an African context. The accounting approach is chosen by collecting accounting data extracted from the annual reports of 24 private and conventional African banks during the period 2010–2019. Both statistical and empirical studies are conducted. The statistical study aims to give a description of sample banks in terms of off-balance sheet activities and key financial indicators. The empirical study has the goal of exploring the correlations between, on the one hand, credit risk and, on the other hand, off-balance sheet ratio and control variables (bank- and country-specific variables). This study is based on dynamic panels using the two-step generalized method of moments technique to estimate regressions between credit risk and the explanatory variables.

The statistical study reveals that sample banks use moderately off-balance sheet activities; most of them use essentially guarantees and letters of credit, have satisfactory financial indicators and are slightly exposed to credit risk. The empirical results from the two-step generalized method of moments technique disclose that off-balance sheet activities have an intensifying effect on the credit risk of African banks. However, the increasing effect can be minimized when African banks use moderately off-balance sheet activities.

Using judiciously off-balance sheet activities does not exacerbate the exposure of African banks to credit risk. Therefore, managers of African banks are recommended to maintain a moderate level of off-balance sheet activities, especially guarantees and letters of credit.

The findings of this study eliminate the opacity about the effect of off-balance sheet activities on credit risk. Moreover, this study fulfills the huge gap in the related literature by completing the scarcity of recent studies, considering all items of the off-balance sheet, focusing on the African context, describing off-balance sheet activities and financial indicators of sample banks due to a statistical study and estimating regressions of dynamic panels between credit risk and both bank-specific and country-specific variables due to a two-step generalized method of moments technique.

The study aims to investigate the presence of herding behavior in the stock market of UK with a special emphasis on news sentiment regarding the economy. The authors focus on the news sentiment because in the current digital era, investors take their decision making on the basis of current trends projected by news and media platforms.

For empirical modeling, the authors use machine learning models to investigate the presence of herding behavior in UK stock market for the period starting from 2006 to 2021. The authors use support vector regression, single layer neural network and multilayer neural network models to predict the herding behavior in the stock market of the UK. The authors estimate the herding coefficients using all the models and compare the findings with the linear regression model.

The results show a strong evidence of herding behavior in the stock market of the UK during different time regimes. Furthermore, when the authors incorporate the economic uncertainty news sentiment in the model, the results show a significant improvement. The results of support vector regression, single layer perceptron and multilayer perceptron model show the evidence of herding behavior in UK stock market during global financial crises of 2007–08 and COVID’19 period. In addition, the authors compare the findings with the linear regression which provides no evidence of herding behavior in all the regimes except COVID’19. The results also provide deep insights for both individual investors and policy makers to construct efficient portfolios and avoid market crashes, respectively.

In the existing literature of herding behavior, news sentiment regarding economic uncertainty has not been used before. However, in the present era this parameter is quite critical in context of market anomalies hence and needs to be investigated. In addition, the literature exhibits varying results about the existence of herding behavior when different methodologies are used. In this context, the use of machine learning models is quite rare in the herding literature. The machine learning models are quite robust and provide accurate results. Therefore, this research study uses three different models, i.e. single layer perceptron model, multilayer perceptron model and support vector regression model to investigate the herding behavior in the stock market of the UK. A comparative analysis is also presented among the results of all the models. The study sheds light on the importance of economic uncertainty news sentiment to predict the herding behavior.

This paper aims to investigate whether executive compensation, corporate social responsibility (CSR)-based incentives, environmental social and governance (ESG) performance and firm performance are the significant predictors of CSR committees, in addition to CEO, firm and corporate governance characteristics, from the tenet of stakeholder and managerial power theories.

Switzerland is an exemplary country from the perspective of corporate governance and executive compensation. This empirical study includes a panel data set of listed Swiss companies, so fixed-effect logistic regression has been used.

It has been found that the companies that offer CSR-based incentives and higher compensation to their CEOs and have better ESG performance are more likely to have CSR committees.

This empirical paper fills the gap in the literature, guides practitioners about the factors that influence the creation and efficiency of CSR committees, and inspires regulatory bodies to ponder on a mandatory CSR committee to form resilient and sustainable organizations worldwide.

COVID-19 has re-emphasized the prominence of sustainability and the stakeholder approach. Thus, this paper indicates that CSR committees require the adaption and implementation of a holistic sustainability policy that integrates both external and internal factors and thereby provides a whole process for sustainability issues.

The impact of CSR committees on corporate social performance (CSP) has already been investigated. However, the predictors of CSR committees have been less scrutinized in the literature.

We propose a risk factor for idiosyncratic entropy and explore the relationship between this factor and expected stock returns.

We estimate a cross-sectional model of expected entropy that uses several common risk factors to predict idiosyncratic entropy.

We find a negative relationship between expected idiosyncratic entropy and returns. Specifically, the Carhart alpha of a low expected entropy portfolio exceeds the alpha of a high expected entropy portfolio by −2.37% per month. We also find a negative and significant price of expected idiosyncratic entropy risk using the Fama-MacBeth cross-sectional regressions. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

We propose a risk factor of idiosyncratic entropy and explore the relationship between this factor and expected stock returns. Interestingly, expected entropy helps us explain the idiosyncratic volatility puzzle that stocks with high idiosyncratic volatility earn low expected returns.

This study aims to investigate whether auditors compromise their independence for economically important clients in family business settings.

The authors empirically examine the research question based on China for the years 2011 to 2020. The dependent variable is the auditors’ propensity to issue modified audit opinions, which is a proxy for auditor independence. The authors use relative client audit fees as a proxy for client importance. To address endogeneity issues in the selection of family firms, the authors use the two-stage least squares regression model and, subsequently, the propensity score matching and Hausman firm fixed effect modeling.

This study reveals that the propensity to issue modified audit opinions is positively correlated with client importance. Big-N auditors are more likely to issue modified audit opinions for their economically important family firm clients, whereas such evidence is not found for non-Big-N auditors. Results are consistent and robust to endogeneity test and sensitivity analysis.

This study enriches the literature on auditor independence and the effect of family firms’ ownership structure factors on audit reporting behavior for their economically important clients. Findings may prove useful for managers and practitioners interested in family business.

This study aims to empirically examine environment, social, and governance (ESG) as an effective strategy to reduce major impediments for a corporation in the form of costs of capital (COC) and systematic risk, especially for emerging markets such as India.

A sample of 114 Indian firms from eight prominent industries based on Thomson Reuters classification (TRBC) are used in the study. A panel regression with industry-fixed effects is carried out to account for industry heterogeneity. For robustness, the authors also carry out a matched sample analysis.

The authors observe a negative and significant relationship between ESG performance with COC and systematic risk, respectively. For the pillar-wise analysis, the authors observe that only governance performance is negatively and significantly related to COC whereas the environmental and social performances are negative and insignificant. For ESG pillar level analysis for beta, the authors observe that all pillars are negative and significant, thus making a case for how firms can fine-tune their ESG strategies according to each pillar.

As the ESG concept is still in a very nascent stage, data availability is a definite challenge in India.

As ESG is increasingly becoming relevant for multiple stakeholders, this study aims to provide evidence that can potentially guide the regulators, practitioners, and academicians to address the contemporary needs of these stakeholders, while also doing good for the firm in the traditional sense.

The transition to a sustainable economy is a challenge for emerging economies, especially for a country like India where stakeholders are not only varied but also huge in number. With this study's contribution towards an incremental understanding of ESG, Indian regulators and policymakers can bring forward mandates as to ESG compliances that are rewarding for the firms and give them enough impetus towards complying with ESG norms.

The extant literature on ESG majorly discusses the relationship between ESG performance and financial performance. This study addresses the lacuna of the relationship of ESG with COC and beta in the Indian context.

Corporate digital transformation (CDT) has challenged traditional tax administration systems. This study examines the impact of CDT on tax avoidance behavior and tests whether tax authorities can identify this behavior.

Using data on listed companies on the Shanghai and Shenzhen Stock Exchanges from 2008 to 2020, this study applies the Heckman two-stage and cross-section models.

The results show that the higher the degree of CDT, the more aggressive the tax avoidance behavior. The CDT's impact on corporate tax avoidance is more significant under strong government tax efforts.

This study expands research on the economic consequences of CDT and the factors influencing corporate tax avoidance behavior. Moreover, it has important implications for governments to monitor tax avoidance behavior under the CDT, improve digital tax systems, and pay more attention to the tax administration of digital assets.