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

Syed Hasan Jafar · Shakeb Akhtar · Hani El-Chaarani · Parvez Alam Khan · Ruaa Omar Binsaddig

33 citations0/0 files storedJournal of risk and financial management

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Stock Market Forecasting Methods Financial Markets and Investment Strategies Hydrological Forecasting Using AI

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.

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2021Generación de series de tiempo financieras sintéticas para "data augmentation" usando redes neuronales generativas adversarias (GAN)2014Stock Price Prediction Using the ARIMA Model 2017Stock market's price movement prediction with LSTM neural networks 2021A Comprehensive Comparative Study of Artificial Neural Network (ANN) and Support Vector Machines (SVM) on Stock Forecasting 2021Applications of deep learning in stock market prediction: Recent progress 2020Stock Closing Price Prediction using Machine Learning Techniques 2018Deep learning-based feature engineering for stock price movement prediction 2020Short-term stock market price trend prediction using a comprehensive deep learning system

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