A genetic-based stock selection model using investor sentiment indicators

In this paper, we present a study of stock selection using genetic algorithms (GA). We first devise a stock scoring model using indicators of investor sentiment arising from behavioral finance literature. The scores are then used to obtain the relative rankings of stocks. Top-ranked stocks can be selected to form a portfolio. Furthermore, we employ GA for optimization of model parameters and feature selection for input variables to the stock scoring model. We will show that the investment returns provided by our proposed methodology significantly outperform the benchmark returns. Based upon the promising results obtained, we expect this GA-based methodology to advance the research in soft computing for behavioral finance and provide an effective solution to stock selection in practice.