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Behavioral Finance is a branch of finance which deals with the investor behavior and the biases they are subjected to while investing. Classical finance theories suggested building a portfolio of assets with personalized risk-return profile and investing funds, instead of buying a single security, as a portfolio of stocks can spread the risk factor and will act as a hedge against systemic risk. Though traditional finance theories suggest that maximizing return taking the least risky path should be a universally acceptable investment goal, empirical evidence reveals that other variables are also determinants of a portfolio choice (Prokopczuk et al., 2011). Existing decision-making systems do not take into account the customized criteria of investors/managers to provide a decision regarding which portfolio to invest in an automated manner. Building a Portfolio is an individualistic decision as each investor has different investment objectives and goals at different times in his life span (Khatwani & Srivastava, 2015; Ognjanovic, Gasevic, & Dawson, 2016; Taylor, 2015). In view of this notion the paper considered various key decision making parameters: stock return, earnings yield, brand image, management fees (payable to the fund manager), and risk and market capitalization as important and evaluated how the factors are analyzed by the consumer based on his biases and how the investor weighs the importance of the factors while choosing the best portfolio. The simulation model develops various permutations and combinations to examine the possibilities. It is envisaged that investment managers would benefit from the study for building an automated portfolio development system to meet the growing demand from investors to have personalized portfolios without compromising on the basic tenets of investment management. We discuss below an outline of the methodology.
A personalized equity portfolio selection system is constructed for NSE Stocks by simulating individual preferences. Since there are multiple factors/criteria, multi-criterion (Vincke et al., 1994; Govindan, Rajendran, Sarkis, & Murugesan, 2015; Rabbani, Davoudkhani, & Farrokhi-Asl,2017) model is formulated. The model evaluates the interaction between different factors impacting portfolio selection decisions to provide suitable weightage in the order of priority to the factors and to identify an optimum portfolio (Emelichev et al., 2014) that matches the investor choices. The order of priority of factors may vary from investors to investor; and hence, the alternatives are modeled keeping in mind the investor.
Thus, in the process a Hybrid multi-criteria model AHP-DEA-FTOPSIS/ EDA is simulated (Tajadod et al., 2016; Jatoth et al., 2019; Kalsi et al., 2013). The model evaluates the set criteria (Khatwani & Srivastava, 2015; Tajadod et al., 2016) and the available alternatives, after considering the preference of investors extracted and assigns order of priorities to the criteria that possibly influence the decision-making and for ranking of portfolios.
The rest of the paper is structured as follows: The literature review illustrates the existing studies on the factors affecting portfolio decision. Thereafter the formulated hybrid multi-criteria model is discussed, followed by the methodology adopted for the multi-criteria study (Goedhart et al., 1989). Finally, the results and implications for different investor scenarios are illustrated along with a discussion of the scope for future research.