A hybrid stock selection model using genetic algorithms and support vector regression

• Published in: Applied soft computing 2012-02, Vol.12 (2), p.807-818
• Main Author: Huang, Chien-Feng
• Format: Article
• Language: English
• Subjects: Feature selection; Genetic algorithms; Model validation; Parameter optimization; Stock selection; Support vector regression
• ISSN: 1568-4946; 1872-9681
• DOI: 10.1016/j.asoc.2011.10.009

[Display omitted] ► Genetic Algorithms (GA) and Support Vector Regression (SVR) were employed to solve an important stock selection problem for investment. ► The effectiveness of the hybrid GA-SVR models was validated statistically. ► The empirical results showed that the investment return provided by our proposed methodology can significantly outperform the benchmark return. ► This hybrid GA-SVR methodology is expected to advance the research in soft computing for finance. In the areas of investment research and applications, feasible quantitative models include methodologies stemming from soft computing for prediction of financial time series, multi-objective optimization of investment return and risk reduction, as well as selection of investment instruments for portfolio management based on asset ranking using a variety of input variables and historical data, etc. Among all these, stock selection has long been identified as a challenging and important task. This line of research is highly contingent upon reliable stock ranking for successful portfolio construction. Recent advances in machine learning and data mining are leading to significant opportunities to solve these problems more effectively. In this study, we aim at developing a methodology for effective stock selection using support vector regression (SVR) as well as genetic algorithms (GAs). We first employ the SVR method to generate surrogates for actual stock returns that in turn serve to provide reliable rankings of stocks. Top-ranked stocks can thus be selected to form a portfolio. On top of this model, the GA is employed for the optimization of model parameters, and feature selection to acquire optimal subsets of input variables to the SVR model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark. Based upon these promising results, we expect this hybrid GA–SVR methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.