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An integrated TOPSIS and ARAS method multi-criteria decision-making approach for optimizing investment portfolios
Prajwal Pisal1, Kiran Kumar Reddy2, Jaydeep Kishore3
1Department of Computer Science, California State University (Alumni), Monterey Bay, Seaside, CA, 93955, USA.
Scientific Reports
|October 2, 2025
Summary
This study introduces a hybrid portfolio optimization method combining TOPSIS, ARAS, GP, and GA. The novel approach improves investment allocation accuracy and investor decision modeling for better financial performance.
Area of Science:
- Quantitative Finance
- Computational Economics
- Operations Research
Background:
- Classical portfolio optimization struggles with complex investor decision-making and risk tolerance integration.
- Existing methods lack efficiency in modeling the interplay between investor behavior, asset attributes, and risk.
- Accurate portfolio construction requires advanced techniques to handle multi-criteria decision-making and probabilistic elements.
Purpose of the Study:
- To develop an innovative hybrid method for enhanced portfolio optimization.
- To integrate multi-criteria decision-making techniques with optimization algorithms for improved accuracy.
- To create a flexible and computationally effective system for realistic investment modeling.
Main Methods:
- A hybrid approach combining Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and Additive Ratio Assessment (ARAS) for multi-criteria decision-making.
- Integration of Goal Programming (GP) for aligning investment decisions with investor expectations and a Genetic Algorithm (GA) for entropy-aware strategies.
- Utilizing the FAR-Trans dataset for empirical testing, including asset evaluation, investor characterization, and probabilistic portfolio construction.
Main Results:
- Achieved a Sharpe Ratio of 2.241, an annualized return of 4.6%, and a diversification score of 0.845.
- Demonstrated a 0.729 correlation between TOPSIS-ARAS rankings and GP configurations, leading to portfolio returns exceeding 30.0%.
- The system realistically depicts investor behavior across diverse transaction channels, risk factors, and geographies.
Conclusions:
- The proposed hybrid method significantly enhances portfolio ranking accuracy and allocation effectiveness.
- The integration of TOPSIS-ARAS, GP, and GA offers a flexible, computationally efficient, and realistic investment modeling solution.
- This approach effectively minimizes constraint deviation while accommodating complex investor profiles and market dynamics.
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