A study of a hybrid evolutionary fuzzy model for stock selection

Stock selection has long been a challenging and important task in finance. 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 fuzzy models as well as genetic algorithms (GA). We first devise a stock scoring mechanism using fundamental variables and apply fuzzy membership functions to re-scale the scores properly. The scores are then used to obtain the relative rankings of stocks. Top ranked stocks can thus 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 return. Based upon the promising results obtained, we expect this hybrid fuzzy-GA methodology to advance the research in soft computing for finance and provide an effective solution to stock selection in practice.