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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Syed Hasan Jafar & Shakeb Akhtar & Hani El-Chaarani & Parvez Alam Khan & Ruaa Binsaddig, 2023. "Forecasting of NIFTY 50 Index Price by Using Backward Elimination with an LSTM Model," JRFM, MDPI, vol. 16(10), pages 1-23, September.
    • Handle: RePEc:gam:jjrfmx:v:16:y:2023:i:10:p:423-:d:1247225
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    References listed on IDEAS

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    1. Andreas Maniatopoulos & Alexandros Gazis & Nikolaos Mitianoudis, 2023. "Technical analysis forecasting and evaluation of stock markets: the probabilistic recovery neural network approach," International Journal of Economics and Business Research, Inderscience Enterprises Ltd, vol. 25(1), pages 64-100.
    2. Akshit Kurani & Pavan Doshi & Aarya Vakharia & Manan Shah, 2023. "A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting," Annals of Data Science, Springer, vol. 10(1), pages 183-208, February.
    3. Vanshu Mahajan & Sunil Thakan & Aashish Malik, 2022. "Modeling and Forecasting the Volatility of NIFTY 50 Using GARCH and RNN Models," Economies, MDPI, vol. 10(5), pages 1-20, April.
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