Stock Price and Nifty 50 Index Forecasting Using LNN and BiLSTM-Based Deep Learning Models

This paper presents two complementary deep learning approaches for financial market forecasting in the Indian context: a Liquid Neural Network (LNN) model for individual stock price prediction and a Bidirectional Long Short-Term Memory (BiLSTM) model for NIFTY 50 index forecasting. The LNN model leverages time-varying parameters to predict stock prices for a 7-day horizon, adapting dynamically to market volatility and temporal patterns. For NIFTY 50 index prediction, we propose a novel hybrid approach combining technical indicators (OHLC-derived) with financial fundamentals of constituent companies. Principal Component Analysis (PCA) is applied to extract significant financial features from each NIFTY 50 company, which are then merged using a union-based approach and combined with technical indicators to train the BiLSTM model.Both implementations demonstrate robustness across various market conditions, including high-volatility periods and significant market events. Stock Price Prediction, Nifty 50 Forecasting, Liquid Neural Network (LNN), Bidirectional LSTM (BiLSTM), Deep Learn- ing, Technical Indicators, Financial Fundamentals, PCA, Time Series Forecasting, Indian Stock Market.