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

摘要

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.