Paper: arXiv 2510.15288

Authors: Visca Tri Winarty, Sena Safarina

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.

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 6.0/10 (capped from 8.0: transaction costs not modelled; see penalty flags)
  • Quadrant: Holy Grail — high math complexity, high empirical rigor

Why this score: The paper employs advanced robust optimization formulations with interval-based uncertainty sets and detailed algorithmic methods (moving-window, bootstrapping), reflecting high mathematical density. It also demonstrates strong empirical rigor through the use of real-world daily stock data for 45 Indonesian banks over a specific period, implementation in MATLAB with specific solvers (CPLEX), and comparative analysis with classical models.

Research Flowchart

  flowchart TD
  A["Research Goal:<br>Optimize Indonesian Banking Stock Portfolio<br>using Robust Optimization"] --> B["Data Input:<br>Indonesian Banking Stock Data<br>(Post-COVID Period)"]
  B --> C["Methodology:<br>Define Uncertainty Sets<br>using Moving-Window & Bootstrapping"]
  C --> D["Computational Process:<br>Apply Mean-Variance Optimization<br>with varying Risk-Aversion Parameters"]
  D --> E["Outcome 1:<br>Moving-Window Method<br>Superior Risk-Return Trade-off"]
  D --> F["Outcome 2:<br>Bootstrapping Method<br>Less Effective for Risk-Tolerant Investors"]
  E --> G["Conclusion:<br>Robust Optimization enhances portfolio reliability;<br>Moving-Window is recommended for risk-tolerant investors"]
  F --> G

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0 of 30 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.