Application of Logistic Regression in Assessing Stock Performances

Stock market prediction pertains to predicting the future value of stock of a company or other financial instrument traded on an exchange. Though the successful prediction of a stock's future price is a complex phenomenon, even a reasonable prediction would yield significant profit and this study attempts to address this unpredictability with the help of a data mining technique. In this study, the companies listed on BSE SENSEX are chosen as representative set of companies that are most actively traded. Logistic Regression is used on various important financial ratios of these companies and certain macro financial variables to analyze which ratios are important and how they are affecting the stock prices. The proposed model results in better classification accuracy when compared to a similar study in the literature.