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A study of risk-adjusted stock selection models using genetic algorithms

Chien-Feng Huang (Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC)
Tsung-Nan Hsieh (Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC)
Bao Rong Chang (Department of Computer Science and Information Engineering, National University of Kaohsiung, Kaohsiung, Taiwan, ROC)
Chih-Hsiang Chang (Department of Finance, National University of Kaohsiung, Kaohsiung, Taiwan)

Engineering Computations

ISSN: 0264-4401

Article publication date: 28 October 2014

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Abstract

Purpose

Stock selection has long been identified as a challenging task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. The purpose of this paper is to employ the methods from computational intelligence (CI) to solve this problem more effectively.

Design/methodology/approach

The authors develop a risk-adjusted strategy to improve upon the previous stock selection models by two main risk measures – downside risk and variation in returns. Moreover, the authors employ the genetic algorithm for optimization of model parameters and selection for input variables simultaneously.

Findings

It is found that the proposed risk-adjusted methodology via maximum drawdown significantly outperforms the benchmark and improves the previous model in the performance of stock selection.

Research limitations/implications

Future work considers an extensive study for the risk-adjusted model using other risk measures such as Value at Risk, Block Maxima, etc. The authors also intend to use financial data from other countries, if available, in order to assess if the method is generally applicable and robust across different environments.

Practical implications

The authors expect this risk-adjusted model to advance the CI research for financial engineering and provide an promising solutions to stock selection in practice.

Originality/value

The originality of this work is that maximum drawdown is being successfully incorporated into the CI-based stock selection model in which the model's effectiveness is validated with strong statistical evidence.

Keywords

Acknowledgements

This work is fully supported by the National Science Council, Taiwan, Republic of China, under grant number NSC 100-2221-E-390-033.

Citation

Huang, C.-F., Hsieh, T.-N., Rong Chang, B. and Chang, C.-H. (2014), "A study of risk-adjusted stock selection models using genetic algorithms", Engineering Computations, Vol. 31 No. 8, pp. 1720-1731. https://doi.org/10.1108/EC-11-2012-0293

Publisher

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Emerald Group Publishing Limited

Copyright © 2014, Emerald Group Publishing Limited