Title: Indian stock market analysis and prediction using LSTM model during COVID-19

Authors: Deepika Saravagi; Shweta Agrawal; Manisha Saravagi

Addresses: Department of Computer Application, SAGE University, Indore, M.P., 452012, India ' CSE, SIRT, SAGE University, Indore, M.P., 452012, India ' Physiotherapy Department, Railway Hospital, Kota, Rajasthan, 324002, India

Abstract: In this study, a computational approach was introduced for predicting the stock prices and statistically analyse the impact of COVID-19 on Indian stock market from 30 January 2020 to 17 July 2020. Long short-term memory model is applied to predict the stock prices of selected companies by comparing the daily stock price movement and returns of various sectors based on historical prices. Results indicate that the stock market fell quickly after the virus outbreak but in the long run, the stock market recovered itself. Finally, we have visualised and compared the predicted values with the actual values. This research helps investors to study the effect of COVID-19 crises at different company's profile and with this analysis, we can assume that in the coming weeks the stock market will recover from the 2020 losses.

Keywords: COVID-19; deep learning; long short-term memory; LSTM; national stock exchange.

DOI: 10.1504/IJESMS.2021.115532

International Journal of Engineering Systems Modelling and Simulation, 2021 Vol.12 No.2/3, pp.139 - 147

Received: 30 Aug 2020
Accepted: 28 Oct 2020

Published online: 28 May 2021 *

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