V. Sree Hari Rao . K. V. N. M. Ramesh

Foundation for Scientific Research and Technological Innovation, Hyderabad - 500102, India, E-mails: vshrao@researchfoundation.in ; vshrao@gmail.com, Department of Computer Science and Engineering, PES University E C Campus, Bangalore, India E-mail: kvnmramesh@pes.edu

Received in final form on June 25, 2023

Abstract
Investment preferences of investors depend on the risk appetite and the risks faced by them which are mainly credit and market risks. Quantification of these risks are done by assigning a credit rating (derived through probability of default (PD) to the issuer of the stock and Value at Risk (VaR) number. In this study an effort is made to reduce the cost of computation of PD with no compromise on the accuracy by the use of existing models. The simulated PD is used to make buy/sell recommendations for a stock/portfolio of any currency and predicting bankruptcy. Depending on the risk appetite, the available investment alternatives are put forth before the investors enabling them deriving the benefits of the recommendations.

Keywords
Investor Recommendations, Probability of Default, Credit Rating, Bankruptcy, VaR, Trading, Machine Learning.

Cite This Article
V. Sree Hari Rao . K. V. N. M. Ramesh, A Novel Risk Advisory Tool for Stock Market Investors and Investment Firms, J. Innovation Sciences and Sustainable Technologies, 3(4)(2023), 221 - 240. https://doie.org/10.0101/JISST.2024348372

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