Stock trading is a popular approach for money investment. Prediction of stock market trends has been an area of great interest both to researchers attempting to uncover the information hidden in the stock market data and for those who wish to profit by trading stocks. The extremely nonlinear nature of the stock market data makes it very difficult to design a system that can predict the future direction&n of the stock market with sufficient accuracy. The prediction of stock markets is an important and widely research issue since it could be had significant benefits and impacts, and the fuzzy rule based systems have been often utilized to forecast reasonably accurate predictions. For promoting the forecasting performance of fuzzy systems, this paper proposed a new model, which incorporates the concept of the decision tree,genetic algorithm and fuzzy systems . The Stock Market follows a Random Walk, which implies that the best prediction you can have about tomorrow''s value is today''s value. Here we are trying to predict the stock market close values by using a combinatorial method of vertical partion based decision tree and the genetic fuzzy rule based systems . The idea is to first apply the genetic algorithm to divide it a number of clusters and then by making a decision tree on the basis of the dataset, so that the prediction of the values is maximal.
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