With the development of artificial intelligence technology, machine learning has achieved very good results in the fieldof stock selection. This paper mainly studies the application of linear model, clustering, support vector machine, randomforest, neural network and deep learning methods in the field of stock selection. The main contribution of this paper isto provide a new idea for traditional quantitative investors, so that they can build a more efficient stock selection modelin practical application. The experimental results show that the stock selection model constructed by these six machinelearning methods can obtain higher return and stability
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