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Evolutionary Approaches to Computational Economics: Application to Portfolio Optimization

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Economic Modeling Using Artificial Intelligence Methods

Part of the book series: Advanced Information and Knowledge Processing ((AI&KP))

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Abstract

This chapter examines the use of genetic algorithms (GA) to perform the task of continuously rebalancing a portfolio, targeting specific risk and return characteristics. The portfolio is comprised of a number of arbitrarily performing trading strategies, plus a risk-free strategy in order to rebalance in a way similar to the traditional Capital Asset Pricing Model (CAPM) method of rebalancing portfolios. A format is presented for the design of a fitness function appropriate to the task, which is evaluated by examining the final results. The results of targeting both risk and return were investigated and compared, as well as optimizing the non-targeted variable to create efficient portfolios. The findings showed that GA is, indeed, a viable tool for optimizing a targeted portfolio using the presented fitness function.

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Authors and Affiliations

  1. Faculty of Engineering and the Built Environment, University of Johannesburg, Johannesburg, South Africa

    Tshilidzi Marwala

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  1. Tshilidzi Marwala
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Marwala, T. (2013). Evolutionary Approaches to Computational Economics: Application to Portfolio Optimization. In: Economic Modeling Using Artificial Intelligence Methods. Advanced Information and Knowledge Processing. Springer, London. https://doi.org/10.1007/978-1-4471-5010-7_9

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  • DOI: https://doi.org/10.1007/978-1-4471-5010-7_9

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  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5009-1

  • Online ISBN: 978-1-4471-5010-7

  • eBook Packages: Computer ScienceComputer Science (R0)

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