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