Deep Learning-Based Stock Price Prediction: A Comprehensive Approach Using Bi-LSTM

This study delves into the prediction of stock prices in the Bangladesh stock market using advanced deep learning frameworks, with a particular focus on the Bidirectional Long Short-Term Memory (Bi-LSTM) model. Traditional statistical models often struggle to effectively capture the complex, non-linear characteristics of financial data, leading to inconsistent results. The Bi-LSTM model, however, addresses this challenge by processing data in both forward and backward sequences, making it particularly suitable for analyzing the intricate dynamics of the Bangladesh stock market. A rigorous feature engineering process is undertaken, incorporating 64 distinct features that include market trends, stock-specific indicators, technical and fundamental variables, as well as crucial macroeconomic factors like gold prices and foreign exchange reserves. The dataset spans historical stock market data from 2012 to 2022, with comprehensive preprocessing steps taken to ensure data quality and consistency. Hyperparameter tuning is executed through a grid search to optimize model performance. The evaluation of the Bi-LSTM model is carried out using metrics such as Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), Mean Squared Error (MSE), and the R2 score, which demonstrate its enhanced predictive power compared to traditional models. This research contributes to the growing field of deep learning in financial forecasting, offering new perspectives for stock price prediction in emerging markets, while also addressing ethical issues to minimize risks like market manipulation.