Portfolio Optimization of Indonesian Banking Stocks Using Robust Optimization
Visca Tri Winarty, Sena Safarina
TL;DR
This study addresses robust portfolio optimization for Indonesian banking stocks under uncertainty in mean returns and covariances. It reformulates the mean-variance objective into a robust framework using interval uncertainty sets $U_{\mu}$ and $U_{\Sigma}$ defined around baseline estimates with bounds derived from two methods: moving-window and bootstrapping. Through empirical analysis on 45 Indonesian banking stocks (247 trading days), the moving-window approach consistently yields a more favorable risk–return trade-off (lower $f_{val}$) than bootstrapping, and, for low risk-aversion ($\gamma=5$), generates higher profits across market conditions. The findings suggest moving-window robust optimization as a practical tool for risk-tolerant investors seeking resilient portfolios amid evolving market dynamics, with implications for application to other markets and asset classes.
Abstract
Since the COVID-19 pandemic, the number of investors in the Indonesia Stock Exchange has steadily increased, emphasizing the importance of portfolio optimization in balancing risk and return. The classical mean-variance optimization model, while widely applied, depends on historical return and risk estimates that are uncertain and may result in suboptimal portfolios. To address this limitation, robust optimization incorporates uncertainty sets to improve portfolio reliability under market fluctuations. This study constructs such sets using moving-window and bootstrapping methods and applies them to Indonesian banking stock data with varying risk-aversion parameters. The results show that robust optimization with the moving-window method, particularly with a smaller risk-aversion parameter, provides a better risk-return trade-off compared to the bootstrapping approach. These findings highlight the potential of the moving-window method to generate more effective portfolio strategies for risk-tolerant investors.