A novel methodology for perception-based portfolio management

Area of Science:

• Quantitative Finance
• Investment Management
• Decision Science

Background:

• Stock market investing presents significant challenges, often leading to capital loss for novice investors due to market uncertainty.
• Existing portfolio construction methods may not adequately address the complexities and uncertainties faced by new investors.

Purpose of the Study:

• To develop a novel framework for novice investors to construct stock portfolios.
• To utilize multicriteria decision-making (MCDM) techniques within a fuzzy environment for portfolio optimization.
• To create a systematic approach for categorizing assets and building perception-based portfolios.

Main Methods:

• Application of multicriteria decision-making techniques in a fuzzy environment to score assets.
• Development of two non-dimensional parameters for classifying risky and non-risky assets.
• Construction of three perception-based portfolios using these parameters and the fractional lion clustering algorithm.
• Comparison of portfolio performance against major index funds and plotting of Markowitz efficient frontier.

Main Results:

• The developed framework successfully categorizes assets based on risk.
• Perception-based portfolios demonstrated significantly higher returns compared to major index funds.
• The constructed portfolios maintained risk levels comparable to index funds while achieving superior returns.

Conclusions:

• The proposed fuzzy MCDM framework offers an effective tool for novice investors to build efficient stock portfolios.
• The novel non-dimensional parameters and clustering algorithm aid in systematic portfolio construction.
• The study validates the superiority of perception-based portfolios over traditional index funds in terms of return and risk efficiency.