Predicting future trends in stock market by decision tree rough-set based hybrid system with HHMM

Around the world, trading in the stock market has gained huge attractiveness as a means through which, one can obtain vast profits. Attempting to profitably and precisely predict the financial market has long engrossed the interests and attention of bankers, economists and scientists alike. Stock market prediction is the act of trying, to determine the future value of a company’s stock or other financial instrument traded on a financial exchange. Accurate stock market predictions are important for many reasons. Chief among all is the need for investors, to hedge against potential market risks and the opportunities for arbitrators and speculators, to make profits by trading indexes. Stock Market is a place, where shares are issued and traded. These shares are either traded through Stock exchanges or Overthe-Counter in physical or electronic form. Data mining, as a process of discovering useful patterns, correlations has its own role in financial modeling. Data mining is a discipline in computational intelligence that deals with knowledge discovery, data analysis and full and semi-autonomous decision making. Prediction of stock market by data mining techniques has been receiving a lot of attention recently. This paper presents a hybrid system based on decision treerough set, for predicting the trends in the Bombay Stock Exchange (BSESENSEX) with the combination of Hierarchical Hidden Markov Model. In this paper we present future trends on the bases of price earnings and dividend. The data on accounting earnings when averaged over many years help to predict the present value of future dividends.