Stock Market Prediction Using Machine Learning

Abstract

Predicting the stock market has been an area of interest not only for traders but also for the computer engineers. Predictions can be performed by mainly two means, one by using previous data available against the stock and the other by analyzing the social media information. In Stock Market Prediction, the aim is to predict the future value of the financial stocks of a company. The recent trend in stock market prediction technologies is the use of machine learning which makes predictions based on the values of current stock market indices by training on their previous values. Machine learning itself employs different models to make prediction easier and authentic. The paper focuses on the use of Regression and LSTM based Machine learning to predict stock values. Factors considered are open, c lose, low, high and volume. Prediction plays a very important role in stock market business which is very complicated and challenging process. Employing traditional methods like fundamental and technical analysis may not ensure the reliability of the prediction. This paper use to evaluate and compare LSTM deep learning architectures for short- and long-term prediction of financial time series. The evaluations are conducted using a publicly available dataset for stock market closing prices. This paper proposes to use machine learning algorithm to predict the future stock price for exchange by using open source libraries and preexisting algorithms to help make this unpredictable format of business a little more predictable. Based on temporal characteristics of stock and LSTM neural network algorithm, this paper uses the LSTM recurrent neural networks to filter, extract feature value and analyze the stock data, and set up the prediction model of the corresponding stock transaction.