eprintid: 18142
rev_number: 2
eprint_status: archive
userid: 1
dir: disk0/00/01/81/42
datestamp: 2024-06-04 14:10:16
lastmod: 2024-06-04 14:10:16
status_changed: 2024-06-04 14:01:31
type: article
metadata_visibility: show
creators_name: Jafar, S.H.
creators_name: Akhtar, S.
creators_name: El-Chaarani, H.
creators_name: Khan, P.A.
creators_name: Binsaddig, R.
title: Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model
ispublished: pub
note: cited By 13
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. © 2023 by the authors.
date: 2023
publisher: Multidisciplinary Digital Publishing Institute (MDPI)
official_url: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175824188&doi=10.3390%2fjrfm16100423&partnerID=40&md5=a365fd1c4c6723880166e68b7ad899f3
id_number: 10.3390/jrfm16100423
full_text_status: none
publication: Journal of Risk and Financial Management
volume: 16
number: 10
refereed: TRUE
issn: 19118074
citation:   Jafar, S.H. and Akhtar, S. and El-Chaarani, H. and Khan, P.A. and Binsaddig, R.  (2023) Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model.  Journal of Risk and Financial Management, 16 (10).   ISSN 19118074