A Hybrid LSTM based Intelligent Model for Financial Market Forecasting

The stock market is highly dynamic and involved in financial things. Making accurate predictions is a challenging task. In this study, we present a deep learning-based model for Stock Market Analysis and Prediction using Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM). The model is trained using a historical dataset containing open, high, low, close and volume (OHLCV). The experimental result shows that the BiLSTM has better performance than traditional LSTM. This research highlights the potential of BiLSTM architecture in financial forecasting and foundation for developing decision support systems for investors and analysts.