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Stock Price Prediction Using LSTM on Indian Share Market

10 pagesPublished: September 26, 2019


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.

Keyphrases: LSTM, prediction model, Share Market

In: Quan Yuan, Yan Shi, Les Miller, Gordon Lee, Gongzhu Hu and Takaaki Goto (editors). Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering, vol 63, pages 101--110

BibTeX entry
  author    = {Achyut Ghosh and Soumik Bose and Giridhar Maji and Narayan Debnath and Soumya Sen},
  title     = {Stock Price Prediction Using LSTM on Indian Share Market},
  booktitle = {Proceedings of 32nd International Conference on Computer Applications in Industry and Engineering},
  editor    = {Quan Yuan and Yan Shi and Les Miller and Gordon Lee and Gongzhu Hu and Takaaki Goto},
  series    = {EPiC Series in Computing},
  volume    = {63},
  pages     = {101--110},
  year      = {2019},
  publisher = {EasyChair},
  bibsource = {EasyChair,},
  issn      = {2398-7340},
  url       = {},
  doi       = {10.29007/qgcz}}
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