Stock Price Prediction Based on Deep Learning Using Long Short-Term Memory

Abstract

AbstractPredicting the stock market is either the easiest or the toughest task in the field of computations. There are many factors related to prediction—physical factors versus physiological, rational and irrational behaviour, capitalist sentiment, market rumours, etc. All these aspects combine to make stock costs volatile and are extremely tough to predict with high accuracy. The prices of a stock market depend very much on demand and supply. High-demand stocks will increase in price, while heavy selling stocks will decrease. Fluctuations in stock prices affect investor perception, and thus, there is a need to predict future share price and to predict stock market prices to make more acquaint and precise investment decisions. We examine data analysis in this domain as a game-changer. This paper proposes that historical value bears the impact of all other market events and can be used to predict future movement. Machine learning techniques can detect paradigms and insights that can be used to construct surprisingly correct predictions. We propose the long short-term memory (LSTM) model to examine the future price of a stock. This paper is to predict stock market prices to make more acquaint and precise investment decisions.KeywordsMachine learningDeep learningRNNLSTM