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

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