Forecasting Revenue Growth Rate Using Fundamental Analysis: A Feature Selection Based Rough Sets Approach
You‐Shyang Chen, Ching‐Hsue Cheng · 2007
This paper mainly forecasts revenue growth rate (RGR) of firms in stock trading systems using rough set theory. It is very important instrument for investors that correctly predict future growing firms from data of fundamental analysis in trading systems, because of the more accurate prediction, the more gain profit. This paper proposes a new approach, a feature selection based method, to enhance accuracy of classifier. This approach uses revenues, profit, earnings, return, and other data to determine the potential for future growth of its revenue. Therefore, the paper provides an empirical comparison of five major feature selection methods for classification. The actual RGR dataset is employed in this empirical case study to illustrate the proposed approach. From the results, the proposed approach selects fewer attributes to improve accuracy. As a result, the performance is superior to the listing methods.