Multivariate Stock Price Forecasting Using Bidirectional Long Short-Term Memory (BiLSTM) with Technical Indicators
TL;DR
The findings indicate that the BiLSTM architecture has strong potential for financial time-series forecasting and can effectively capture important sequential patterns in stock market data.
Abstract
Stock price forecasting has become an important research area in financial market analysis due to the highly dynamic and volatile nature of stock market movements. Accurate forecasting models can assist investors and financial analysts in making better investment decisions and managing financial risks more effectively. This study proposes a Bidirectional Long Short-Term Memory (BiLSTM)-based deep learning model for forecasting stock prices using multivariate financial time-series data. The dataset used in this study consists of historical stock market data combined with several technical indicators, including High, Low, Open, Volume, Moving Average (MA10 and MA20), and Relative Strength Index (RSI). Prior to model training, the data underwent preprocessing stages including data cleaning, normalization, and sequence generation using a sliding window approach. The proposed architecture employs a Bidirectional LSTM layer followed by additional LSTM and dense layers to capture complex temporal dependencies and nonlinear patterns in stock price movements. The experimental results demonstrate that the proposed model achieved moderate forecasting performance with an MAE value of 15.5805, RMSE of 21.0883, and R² score of 0.4954. Furthermore, the prediction results show that the model was able to follow the general trend of actual stock price movements despite several deviations during periods of high market volatility. The findings indicate that the BiLSTM architecture has strong potential for financial time-series forecasting and can effectively capture important sequential patterns in stock market data. This study also highlights the challenges of stock price prediction due to the stochastic and nonlinear behavior of financial markets. Future research is recommended to incorporate additional external factors and more advanced deep learning architectures to further improve forecasting accuracy and robustness.