Stock Price Prediction Using LSTM on Indian Share Market

TLDR

Stock market prediction is notoriously difficult because volatile prices are influenced by numerous physical, psychological, and informational factors, so only historical spot prices are used to capture all embedded market events. The study aims to demonstrate that data analysis can transform stock forecasting by proposing a framework that combines an LSTM model with a net‑growth calculation algorithm to predict a company’s future growth. The authors apply machine‑learning techniques to historical price data, using an LSTM network and a net‑growth algorithm to infer future trends. They find that machine‑learning methods can uncover previously unseen patterns, enabling highly accurate predictions.

Abstract

Predicting stock market is one of the most difficult tasks in the field of computation. There are many factors involved in the prediction – physical factors vs. physiological, rational and irrational behavior, investor sentiment, market rumors,etc. All these aspects combine to make stock prices volatile and very difficult to predict with a high degree of accuracy. We investigate data analysis as a game changer in this domain.As per efficient market theory when all information related to a company and stock market events are instantly available to all stakeholders/market investors, then the effects of those events already embed themselves in the stock price. So, it is said that only the historical spot price carries the impact of all other market events and can be employed to predict its future movement. Hence, considering the past stock price as the final manifestation of all impacting factors we employ Machine Learning (ML) techniques on historical stock price data to infer future trend. ML techniques have the potential to unearth patterns and insights we didn’t see before, and these can be used to make unerringly accurate predictions. We propose a framework using LSTM (Long Short- Term Memory) model and companies’ net growth calculation algorithm to analyze as well as prediction of future growth of a company.