STOCK MARKET PREDICTION USING MACHINE LEARNING METHODS

Abstract

Stock price forecasting is a popular and important topic in financial and academic studies. Share market is an volatile place for predicting since there are no significant rules to estimate or predict the price of a share in the share market. Many methods like technical analysis, fundamental analysis, time series analysis and statistical analysis etc. are used to predict the price in tie share market but none of these methods are proved as a consistently acceptable prediction tool. In this paper, we implemented a Random Forest approach to predict stock market prices. Random Forests are very effectively implemented in forecasting stock prices, returns, and stock modeling. We outline the design of the Random Forest with its salient features and customizable parameters. We focus on a certain group of parameters with a relatively significant impact on the share price of a company. With the help of sentiment analysis, we found the polarity score of the new article and that helped in forecasting accurate result. Although share market can never be predicted with hundred per-cent accuracy due to its vague domain, this paper aims at proving the efficiency of Random forest for forecasting the stock prices.