Enhancing Portfolio Diversification: Linguistic Fuzzy and Absolute Difference Approaches to Stock Assignment for Varied Investor Risk Profiles
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Abstract
Matching stocks to investors based on their risk preferences, like "high suitability" or "moderate risk," can be tricky in portfolio management. This study explores two new methods—the Linguistic Fuzzy Assignment Method (LFAM) and the Absolute Difference Calculation Algorithm (ADCA)—to improve how stocks are assigned across large-cap, mid-cap, and small-cap groups. Using a 6x6 cost matrix built from fuzzy linguistic ratings, these methods pair six stocks with six investors, each with distinct risk preferences. Both approaches produce the same optimal stock assignments, achieving a low total unsuitability score of 2.3875, which shows effective portfolio diversification. These results highlight the methods' ability to handle uncertainty, providing useful insights for financial advisors.
MATLAB simulations further confirm the solutions' reliability, indicating potential for use in fluctuating markets.
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