Stock Movement Prediction Using Mamba and Ensemble Learning

Abstract

The stock market is influenced by various factors such as national policies, economic conditions, and global events. Accurately predicting stock price changes has long been a critical challenge for investors and economists, as it can significantly reduce investment risks. Accurate forecasts can enhance investment strategies, allowing for maximized returns. However, the volatile and non-linear nature of financial markets makes this task particularly complex. Recently state space models, like Mamba, have shown promising results in sequence modeling. In this work, we apply Mamba to predict the percentage changes in daily stock closing prices. By forecasting these changes, we frame the problem as a classification task, where the goal is to determine whether the stock price will increase or not. By training models with different hyperparameters and combining them through ensemble learning, the prediction accuracy is further improved. To evaluate the model, we analyze stock movements over a series of trading days for six Nasdaq-listed companies. Our model demonstrates notable performance, achieving an average F1 score of 60.5% in predicting the direction of next-day price movement. The model generated an average profit of $4150 over 120 test transaction days with an initial capital of $10,000. These insights can help investors make more informed decisions, optimizing returns while minimizing risks. © 2024 IEEE.