Individual Stock Price Prediction Using Stacking Method

This paper explores the application of stacking in machine learning to predict the price of a single stock. The complexity of financial markets and the high noise in data make stock price prediction a challenging task. To improve prediction accuracy, this paper combines multiple machine learning models, including linear regression, decision trees, and random forests, using stacking to integrate the predictions of these base models. Experimental results indicate that the stacking model performs exceptionally well in predicting the stock price of Apple Inc. (AAPL), significantly outperforming individual models. This paper evaluates the model using mean squared error (MSE) and root mean squared error (RMSE) and demonstrated the models prediction accuracy and robustness. The findings demonstrate that the stacking model not only reduces prediction errors but also enhances robustness against the volatile nature of stock prices. This study underscores the high potential of stacking in financial time series prediction, providing valuable insights and references for investment decisions. By integrating multiple predictive models, stacking offers a powerful tool for navigating the complexities of financial markets and making informed investment choices.