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An Improved Data Driven Differential Evolution Algorithm For Band Optimized Portfolio Optimization

Published: 23 April 2024 Publication History

Abstract

This paper presents a portfolio optimization model that incorporates expected shortfall, skewness, and kurtosis under the assumption of a t-distribution. The expected shortfall is chosen as the primary risk measure, while skewness and kurtosis serve as auxiliary risk factors set as constraints. This approach provides a comprehensive understanding of the distribution characteristics of assets and aids in effective risk management. The asset pool considered in this study consists of assets listed in the SSE 50 Index and possesses an ample amount of historical data for forecasting. To tackle the problem at hand, a data-driven operator, inspired by Euler's theorem, has been proposed and integrated into the LSHADE44-IEpsilon algorithm. Empirical results confirm that the new model assists investors in effectively managing risk and increasing wealth. Moreover, the improved algorithm (EU-LSHADE44ɛ) outperforms LSHADE44-IEpsilon and other 2 state-of-the-art evolutionary algorithms, significantly accelerating the evolutionary process and delivering superior final solutions.

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      ICCIP '23: Proceedings of the 2023 9th International Conference on Communication and Information Processing
      December 2023
      648 pages
      ISBN:9798400708909
      DOI:10.1145/3638884
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      Published: 23 April 2024

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      Author Tags

      1. Euler's theorem
      2. differential evolution
      3. portfolio optimization
      4. risk decomposition

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