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Research on Stock Price Rise and Fall Prediction Based on Optimization Random Forest
- 1 Hebei University of Economics and Business, No.47 Xuefu Road, Xinhua District, Shijiazhuang City, Hebei Province, 050061
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Abstract
Random forest algorithm is an effective machine learning algorithm in stock return classification prediction, with high accuracy, but it has problems such as parameter optimization defects and difficulty in feature selection. To this end, based on the traditional random forest algorithm, a new algorithm is proposed by combining the feature selection particle swarm algorithm with the parameter grid search algorithm - the particle swarm parameter grid search random forest algorithm. Using particle swarm optimization algorithm for feature selection of input data, reducing the dimensionality of input data by removing redundant features, and introducing grid search algorithm to optimize some parameters of random forest, not only reduces the computational complexity of random forest algorithm, but also improves the classification and prediction accuracy of random forest. The experimental results were compared with the original random forest, decision tree, and support vector machine classification models, confirming that the parameter optimized random forest stock prediction model has higher accuracy and AUC values in model evaluation than other models.
Keywords
random forest, Technical indicators, Parameter optimization, Grid search, Stock price prediction
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Cite this article
Wang,J. (2025). Research on Stock Price Rise and Fall Prediction Based on Optimization Random Forest. Applied and Computational Engineering,133,61-67.
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Volume title: Proceedings of the 5th International Conference on Signal Processing and Machine Learning
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