Abstract
With the advent of Big Data, managing large-scale portfolios of thousands of securities is one of the most challenging tasks in the asset management industry. This study uses an evolutionary multi-objective technique to solve large-scale portfolio optimisation problems with both long-term listed and newly listed securities. The future returns of long-term listed securities are defined as random variables whose probability distributions are estimated based on sufficient historical data, while the returns of newly listed securities are defined as uncertain variables whose uncertainty distributions are estimated based on experts’ knowledge. Our approach defines security returns as theoretically uncertain random variables and proposes a three-moment optimisation model with practical trading constraints. In this study, a framework for applying arbitrary multi-objective evolutionary algorithms to portfolio optimisation is established, and a novel evolutionary algorithm based on large-scale optimisation techniques is developed to solve the proposed model. The experimental results show that the proposed algorithm outperforms state-of-the-art evolutionary algorithms in large-scale portfolio optimisation.
Original language | English |
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Number of pages | 15 |
Journal | IEEE Transactions on Evolutionary Computation |
Early online date | 01 Jan 2024 |
DOIs | |
Publication status | Early online date - 01 Jan 2024 |
Keywords
- Evolutionary computation
- Evolutionary computations
- Large-scale investment
- Mathematical models
- Multi-objective optimisation
- Optimization
- Portfolio optimisation
- Portfolios
- Random variables
- Security
- Uncertain random variable
- Uncertainty
- FinTech Application
- Artificial Intelligence (AI)
ASJC Scopus subject areas
- Finance
- Economics, Econometrics and Finance (miscellaneous)
- Artificial Intelligence
- Theoretical Computer Science
- Computational Theory and Mathematics
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Advanced Data Science Professional
Quinn, B. (Recipient), 05 May 2024
Prize: Fellowship awarded competitively