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Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model

Syed Hasan Jafar, Shakeb Akhtar, Hani El-Chaarani (h.shaarani@bau.edu.lb), Parvez Alam Khan and Ruaa Binsaddig
Additional contact information
Syed Hasan Jafar: School of Business, Woxsen University, Hyderabad 502345, India
Shakeb Akhtar: School of Business, Woxsen University, Hyderabad 502345, India
Hani El-Chaarani: Faculty of Business Administration, Beirut Arab University, Riad El Solh, Beirut 11072809, Lebanon
Parvez Alam Khan: Department of Management and Humanities, University Technology PETRONAS, Seri Iskandar 32610, Malaysia
Ruaa Binsaddig: College of Business Administration, University of Business and Technology, 10000 Prishtina, Kosovo

JRFM, 2023, vol. 16, issue 10, 1-23

Abstract: Predicting trends in the stock market is becoming complex and uncertain. In response, various artificial intelligence solutions have emerged. A significant solution for predicting the trends of a stock’s volatile and chaotic nature is drawn from deep learning. The present study’s objective is to compare and predict the closing price of the NIFTY 50 index through two significant deep learning methods—long short-term memory (LSTM) and backward elimination LSTM (BE-LSTM)—using 15 years’ worth of per day data obtained from Bloomberg. This study has considered the variables of date, high, open, low, close volume, as well as the 14-period relative strength index (RSI), to predict the closing price. The results of the comparative study show that backward elimination LSTM performs better than the LSTM model for predicting the NIFTY 50 index price for the next 30 days, with an accuracy of 95%. In conclusion, the proposed model has significantly improved the prediction of the NIFTY 50 index price.

Keywords: backward elimination; LSTM; stock market prediction; NIFTY 50; relative strength index; accuracy score (search for similar items in EconPapers)
JEL-codes: C E F2 F3 G (search for similar items in EconPapers)
Date: 2023
References: Add references at CitEc
Citations: View citations in EconPapers (1)

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