Data mining driven computational analysis of stock markets, methods and strategies

Abstract

The stock market is a complex, dynamic and non-linear environment. The prediction of any future market reaction is further complicated by huge amounts of often unstructured financial data and uncertainty due to the effects of unforeseen market events. The application of correlation analysis to significant market events is still seen as a useful tool in the prediction of future trends on the stock market in a global sense.

This paper proposes the application of data mining computation correlation analysis to the stock market to enhance the durability of predictions. This method of correlation analysis is a combination of cross correlation, auto correlation and fundamental analysis that is further enhanced by Channel correlation, Weighted Pearson’s correlation and added correlation Support Vector Regression. Channel correlation traces the similarity of trends while the weighted Pearson’s correlation acts as a noise filter during the correlation extraction process