Application of Blending-based Ensemble Algorithm in Stock Prediction

Stock trend prediction has long been an important research direction in the financial field, and it is also an extremely challenging task. Currently, most studies focus on a single prediction model to find a better prediction scheme by comparing the effects of different algorithms.This paper proposes a stock trend prediction method based on a Blending ensemble learning approach, which combines 55 technical indicators such as Exponential Moving Averages (EMA) and Relative Strength Index (RSI). PCA dimensionality reduction is used to further simplify the data representation of the features after SOM dimensionality reduction. The method employs two high-performing machine learning models with distinct algorithmic characteristics as base learners and Logistic Regression as the meta-learner to construct an efficient ensemble prediction framework. Using Apple Inc.'s stock (AAPL) as the research subject, the study utilises the confusion matrix as the core performance evaluation metric. Experimental results demonstrate that optimised through hyperparameter tuning. Experimental results indicate that the Blending ensemble learning model, optimized through hyperparameter tuning, outperforms the single prediction models in terms of accuracy.