Stock assessment using Cumulative Prospect Theory in DEA cross-efficiency model

Abstract

Market volatility is becoming increasingly common as numerous factors are implemented in the financial system. As a result, portfolio managers and individual investors require reliable methods to assess stock performance. This study examines stock assessments using cross-efficiency evaluations in cases where negative data is present. An alternative approach to achieve this goal is to use an RDM DDF-based cross-efficiency model which oversees the negative data. We expand the RDM-based cross-efficiency analysis, which uses row and column average values to select portfolios and identify different groups for stock management. To explore the psychological factors that influence the choices made by stock market investors, we incorporate the cumulative prospect theory value for each stock as an output and the variance as an input to evaluate the overall efficiency of the assets. For the empirical analysis, our study focuses on a sample of 30 stocks listed on the Nifty-50 on India's National Stock Exchange. The results of our empirical study verify that the proposed method can serve as an effective tool for stock selection. This demonstrates how the chosen portfolio gives companies a more diversified and well-balanced approach for selecting stocks, thus improving logical decision-making.