Stock Price Prediction Based on LSTM Deep Learning Model

Abstract

Predicting 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 vs. physiological, rational and irrational , capitalist sentiment, market , 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 prices 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 LSTM (Long Short Term Memory) 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.

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