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Related Experiment Videos

A multi-period robust portfolio optimization framework using yager's entropy.

Arman Khosravi¹, Seyed Jafar Sadjadi¹

¹Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran.

|May 14, 2026

Summary

This summary is machine-generated.

This study introduces the Generalized Robust Mean-Variance-Entropy (GRMVE) framework, a new model for adaptive investing. The GRMVE framework enhances portfolio diversification and risk management, outperforming traditional methods in dynamic market conditions.

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Area of Science:

Quantitative Finance
Computational Economics
Financial Engineering

Background:

Modern portfolio theory exhibits sensitivity to input parameters and concentration risk.
Existing models often underperform out-of-sample due to these limitations.
Adaptive, multi-period investment strategies are crucial for robust portfolio management.

Purpose of the Study:

Introduce the Generalized Robust Mean-Variance-Entropy (GRMVE) framework.
Address limitations of traditional portfolio optimization, including parameter sensitivity and concentration.
Develop a computationally tractable model for institutional portfolio management.

Main Methods:

Developed a novel Mixed-Integer Quadratic Programming (MIQP) model: GRMVE.
Integrated budgeted uncertainty to mitigate estimation error risk.
Incorporated Yager's entropy for structural diversification and to prevent over-concentration.
Employed a dynamic, expanding-window re-optimization approach over a 72-month horizon.

Main Results:

GRMVE framework demonstrated highly competitive risk-adjusted returns.
Outperformed Naive (1/n) and Mean-CVaR models in empirical validation.
Overcame severe portfolio concentration and high trading turnover issues of other models.
Achieved significantly lower drawdowns during market downturns and recoveries.

Conclusions:

The GRMVE framework offers a practical and structurally resilient solution for institutional portfolio management.
It effectively balances risk management, diversification, and computational tractability.
The model provides a robust alternative to traditional and state-of-the-art portfolio optimization techniques.