This study aims to empirically examine the impact of managerial effectiveness on the credit risk of the Indian public and private sector banks. We consider the return on assets as a proxy for managerial effectiveness and gross non-performing assets (GNPA) to total advances as a proxy for credit risk. The study uses fixed effects and dynamic panel data models to examine the impact. The econometric model estimations suggest a negative impact of return on assets on credit risk. Further, we analyze the impact of return on assets by the information of microeconomic and macro-economic variables in dynamic generalized methods of moments (GMM) approach. The results remain the same after using dynamic GMM modelled with lagged credit risk and lagged return on assets. Further, the effect of macroeconomic variables such as repo rate and reverse repo rate confirms the theory. Heterogeneity checks at regions and sector levels substantiate the robustness of results.
JEL Classification Codes: G20, G21.
Traditional statistical methods pose challenges in data analysis due to irregularity in the financial data. To improve accuracy, financial researchers use machine learning architectures for the past two decades. Neural Networks (NN) are a widely used architecture in financial research. Despite the wider usage, NN application in finance is yet to be well defined. Hence, this descriptive study classifies and examines the NN application in finance into four broad categories i.e., investment prediction, credit evaluation, financial distress, and other financial applications. Likewise, the review classifies the NN methods used under each category into standard, optimized and hybrid NN. Further, accuracy measures used by the research work widely differ, in turn, pose challenges for comparison of a NN under each category and reduces the scope of formalizing a theory to choose optimum network model under each category.
JEL Classification Codes: G1, G17, M150.
The COVID-19 pandemic has impacted economies worldwide, and it has been reflected in their stock markets, as well. The effect was evident in the Indian stock markets, yet the nature and level of this impact are not very clear. This paper examines the short- and long-term spillover in the volatility between coronavirus cases on the broader market index, Nifty 50, and the indices of selected sectors: information technology, healthcare, and pharmaceuticals. Data for the period from January 2020 to July 2022 has been analyzed. The Dynamic Conditional Correlation GARCH model was used for analyzing the volatility spillover of coronavirus cases on Nifty 50, Nifty IT, Nifty Healthcare, and Nifty Pharma. The results show that there has been a significant long-term volatility spillover of infections on the broader market index, Nifty 50. However, there is no long-term persistence of COVID-19 on the sectoral indices. Only Pharma and Healthcare have exhibited significant short-term persistence. All the indices were negatively correlated with case numbers. Even though the sectoral indices did not exhibit significant long-term volatility spillover, they were positively correlated with the broader market index, Nifty 50, which in turn showed the significant long-term persistence of COVID-19. The results of the study are useful to policymakers and investors to understand the level of market impact due to the pandemic.
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