The classical linear multi-factor stock selection model is widely used for long-term stock price trend prediction. However, the stock market is chaotic, complex, and dynamic, for which reasons the linear model assumption may be unreasonable, and it is more meaningful to construct a better-integrated stock selection model based on different feature selection and nonlinear stock price trend prediction methods. In this paper, the features are selected by various feature selection algorithms, and the parameters of the machine learning-based stock price trend prediction models are set through time-sliding window cross-validation based on 8-year data of Chinese A-share market. Through the analysis of different integrated models, the model performs best when the random forest algorithm is used for both feature selection and stock price trend prediction. Based on the random forest algorithm, a long-short portfolio is constructed to validate the effectiveness of the best model. INDEX TERMS Stock, trend prediction, machine learning, feature selection, long-term investment. QURAT UL AIN received the master's degree in accounting and finance and the M.S. degree in finance from the
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