Dynamic Portfolio Optimization: A Two-Stage Hybrid Approach Integrating Selection and Allocation

Abstract

This study focuses on a two-stage hybrid framework for dynamic portfolio optimization. While existing two-stage approaches often rely mainly on price data, symmetric downside measures, or static allocation, they provide limited insight into fundamental efficiency and how assets behave during severe market downturns. In the first stage, Data Envelopment Analysis (DEA) is used to pre-select efficient stocks based on fundamental financial indicators. In the second stage, several risk-based allocation strategies are applied to the selected assets, including mean-standard deviation, semi-deviation, conditional value at risk, maximum Sharpe ratio, and a proposed mean-asymmetric downside risk model. This proposed model introduces an exponential penalty function to emphasize larger losses and better capture downside risk during market downturns. Portfolio performance is evaluated using a rolling-window approach with weekly rebalancing over a 65-week out-of-sample period. The DEA-based selection effectively reduced the investment universe from 36 to 9 efficient stocks. Empirical results show that the simple DEA+1/N portfolio outperformed the SET50 benchmark, and the proposed DEA+mAsymRisk delivered higher returns with lower downside risk compared to alternative strategies. These findings highlight that combining fundamental screening with downside-sensitive allocation strengthens portfolio robustness and offers practical value for dynamic risk management in financial markets.