Improving Stock Price Prediction Accuracy with StacBi LSTM

Abstract

This research aimed to enhance stock price prediction accuracy using the Stacked Bidirectional Long Short-Term Memory (StacBi LSTM) model. The study addressed the challenge of capturing long-term dependencies and temporal patterns inherent in stock price data. The research objectives were to evaluate the model's performance across different input sequence lengths and identify the optimal length for prediction. Leveraging a dataset from the Indonesian Stock Exchange, the model's predictions were evaluated using key metrics such as RMSE, MAE, MAPE, and R2. Results indicated that the StacBi LSTM model excelled in capturing stock price trends and demonstrated strengths over traditional methods. The optimal input sequence length was identified, balancing computational efficiency and prediction accuracy. This research contributes valuable insights into improving stock price prediction techniques and offers practical implications for traders and investors. Future research directions encompass hybrid models and integrating external factors to enhance predictive capabilities further.

Keywords

• Stock Price Prediction
• Stacked Bidirectional LSTM
• Time Series Analysis
• Indonesian Stock Exchange
• Input Sequence Length