Stock Market Trend Prediction Using Hybrid Machine Learning Algorithms

Abstract

Machine learning and feature extractions are playing very vital role in health sector and internet sectors also. Stock market prediction is a major exertion in the field of finance and establishing businesses. Stock market is totally uncertain as the prices of stocks keep fluctuating on a daily basis because of numerous factors that influence it. One of the traditional ways of predicting stock prices was by using only historical data. But with time it was observed that other factors such as peoples' sentiments and other news events occurring in and around the country affect the stock market, for e.g. national elections, natural calamity etc. Investors in the stock market seek to maximize their profits for which they require tools to analyze the prices and trend of various stocks. Machine learning algorithms have been used to devise new techniques to build prediction models that can forecast the prices of stock and tell about the market trend with good accuracy. Many prediction models have been proposed to incorporate all the major factors affecting the price of stocks. This paper focuses on portraying distinct machine learning algorithms such as support vector machine random forest boosted decision trees assemble methods and few hybrid method, which have been used to build prediction model and predict the stock prices for different stock exchanges. This paper also covers the various challenges that are encountered while building prediction models. In this case study by using simple machine learning algorithm linear regression we predict the stock market closing amount price. In this case we used 7 different types of real-time datasets also.