Stock price prediction model based on machine learning

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Machine learning methods improve stock price prediction accuracy compared to traditional models like ARIMA.
Support vector regression and LSTM effectively capture market nonlinearities and enhance prediction accuracy.
Bayesian optimization and attention mechanisms are introduced to refine stock price prediction systems.
Challenges remain in data quality and model interpretability, indicating a need for further research.

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This review highlights machine learning and deep learning techniques for stock price prediction, implying improved accuracy over traditional methods.

Jiajun Sun (2025) studied this question.

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This review highlights machine learning and deep learning techniques for stock price prediction, implying improved accuracy over traditional methods.