Feature selection for stock market analysis

Abstract

The analysis of the financial market always draws a lot of attention from investors and researchers. The trend of stock market is very complex and is influenced by various factors. Therefore to find out the most significant factors to the stock market is very important. Feature Selection is such an algorithm that can remove the redundant and irrelevant factors, and figure out the most significant subset of factors to build the analysis model. This paper analyzes a series of technical indicators used in conventional studies of the stock market and uses various feature selection algorithms, such as principal component analysis, genetic algorithms, and sequential forward search, to find out the most important indicators. © Springer-Verlag 2013.