Using Genetic Algorithms for Feature Selection in Predicting Financial Distresses with Support Vector Machines

Financial distresses in corporations harm both individual investors and financial institutions, and can cause social problems due to the cascading effects. Government agencies, fund managers, and even small investors need to arm themselves with devices for predicting financial distresses in corporations, even when the financial problems are shadowed by malignant window dressing. In previous work, we explored the effectiveness of making predictions based on both financial ratios, including those proposed by Altman for the Z-score models and those used in the common-size analysis, and qualitative indicators, such as corporate governance. In this paper, we report results of our attempt to select the best features from the previously proposed features with genetic algorithms and gain ratio-based methods. Experimental results indicate that the selected features outperform the features used in the Z-score models. Not surprisingly, the genetic algorithms surpass the gain ratio-based methods in the task of feature selection.