Abstract:
This thesis proposes a study on the relationship between credit cycles and sectoral risks in the Indian context. The research will use a novel credit cycle index and a novel sectoral risk indicator based on firm-level data to provide more accurate and dynamic indicators of credit and market risks. Natural language processing (NLP) methods will also be applied to create
a firm-specific sentiment index related to credit risk. The primary objective of this research is to develop a novel sectoral risk indicator for eleven sectors, to investigate the relationship between credit cycle index and sectoral risks, and to develop investment strategies based on these studies. In addition to shedding light on the impact of macroprudential regulations and other credit risk mitigating variables, the study also aims to develop an NLP-based firm-specific sentiment index for credit risk. The outcomes of this study may give investors and regulators with vital information for making educated investment choices.
