Stock Market Prediction Using Data Mining Techniques

A stock market is the aggregation of buyers and sellers of stocks (shares), which represent ownership claims on businesses which may include securities listed on a public stock exchange as well as those traded privately. We have seen through the years that people have incurred high losses which have led to devastations of lives and hence a need for prediction system arises which can be trusted and consistent throughout the life cycle. Also predicting stock prices is an important task of financial time series forecasting, which is of primary interest to stock investors, stock traders and applied researchers. Precisely predicting stocks is essential for investors to gain enormous profits. However the volatility of the market makes this kind of prediction is highly difficult. We show that Data Mining and Machine Learning could be used to guide an investor’s decisions. The main aim is to build a model with the help of Data Mining techniques such as Knn which can be used for classification and regression combined with Machine Learning techniques like Genetic algorithm, SVR along with Sentiment Analysis based social media text, which forecast’s stock price for companies. The system if correctly implemented will help investors and new users to kick start the investment process and can provide undue benefits. The system can be enhanced by considering the input parameters and the data considered overtime.