Suresh G.

Herding has a history of igniting large, irrational market ups and downs, usually based on a lack of fundamental support. Intuitively, most herds start with an external shock. This empirical study seeks to detect shock-induced herding and the creation of nascent bubbles in the Indian stock market. Initially, the multifractal form of the detrended fluctuation analysis was applied. Then the Reformulated Hurst exponent for the Bombay stock exchange (BSE) was determined using Kantelhardt’s calibration. The investigation found evidence of high-level herding and a bubble in 2012, with a high value of Hurst Exponent (0.7349). The other years of the research period (2011, 2013, 2016, 2018, 2020–2021) observed mild to significant herding with comparatively lower Hurst values. The results confirm that herding behavior occurs during a crisis and harsh situations emitting shocks. The study concludes that shock-based herding is prevalent in all six shocks: the economic meltdown, commodities and currency devaluation, geo-political problems, the Central Bank’s decision on liquidity management, and the Pandemic. Additionally, the years following the Financial Crisis and the years of the Pandemic are when herding and bubble are prominent.

Acknowledgments
We thank Dr. Bikramaditya Ghosh (Associate Professor, Symbiosis International University, Bangalore, India) for motivating us in this research. We also thank Dr. Natchimuthu N (Assistant Professor, Commerce, CHRIST (Deemed to be University), Bangalore, India) and Dr. Mahesh E. (Assistant Professor, Economics, CHRIST (Deemed to be University), Bangalore, India) for their support throughout this study.

Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a bank’s cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a bank’s credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value < 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a bank’s credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk.