TY  - JOUR
A1  - Jafar, S.H.
A1  - Akhtar, S.
A1  - El-Chaarani, H.
A1  - Khan, P.A.
A1  - Binsaddig, R.
JF  - Journal of Risk and Financial Management
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85175824188&doi=10.3390%2fjrfm16100423&partnerID=40&md5=a365fd1c4c6723880166e68b7ad899f3
VL  - 16
Y1  - 2023///
PB  - Multidisciplinary Digital Publishing Institute (MDPI)
SN  - 19118074
IS  - 10
N2  - 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 stockâ??s volatile and chaotic nature is drawn from deep learning. The present studyâ??s objective is to compare and predict the closing price of the NIFTY 50 index through two significant deep learning methodsâ??long short-term memory (LSTM) and backward elimination LSTM (BE-LSTM)â??using 15 yearsâ?? 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.
N1  - cited By 13
ID  - scholars18142
TI  - Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model
AV  - none
ER  -