AN INTERVAL ANALYSIS APPROACH TO MULTI-PERIOD PORTFOLIO OPTIMIZATION
DOI:
https://doi.org/10.54783/v674dc53Keywords:
Economic and Financial Modelling; Multi-Period Portfolio Optimization; Interval Analysis; Risk–Return Modeling; Portfolio Diversification Entropy.Abstract
The limited availability and reliability of historical data in emerging financial markets present major challenges in estimating asset returns, measuring risk exposure, and evaluating liquidity conditions accurately. As a consequence, investment decisions are often made in environments characterized by uncertainty, ambiguity, and incomplete information. Addressing this issue, this study proposes a fuzzy multi-period portfolio optimization framework that represents key financial variables, including returns, risks, and liquidity, as interval-valued parameters in order to better capture real market uncertainty. The proposed model integrates multiple investment objectives, namely expected return, risk tolerance, liquidity preference, and portfolio diversification, within a unified multi-period optimization structure. The main objective of the framework is to maximize terminal wealth while ensuring that each investment period satisfies predefined requirements related to minimum returns, acceptable risk levels, and diversification constraints. Portfolio diversification is quantitatively measured using proportional entropy, which provides a comprehensive and theoretically sound representation of allocation balance. To improve computational tractability, the interval-based fuzzy optimization problem is transformed into a crisp nonlinear programming model through the application of fuzzy decision-making theory and multi-objective optimization techniques. Furthermore, this study develops an enhanced Particle Swarm Optimization (PSO) algorithm equipped with adaptive adjustment mechanisms to improve convergence efficiency and solution accuracy in solving constrained optimization problems. A numerical simulation is conducted to evaluate the applicability of the proposed framework under realistic market conditions. The results indicate that the model effectively manages uncertainty, integrates subjective and objective information, and generates more adaptive and reliable portfolio allocation strategies. Therefore, this research contributes to the advancement of portfolio optimization methodologies by providing a flexible and practical decision-making framework for investors operating in uncertain and data-limited financial environments.
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