US Stocks Market Movements Prediction: Classification of SP-500 Using Machine Learning Technology
DOI:
https://doi.org/10.54691/bcpbm.v26i.2068Keywords:
component; Stocks Market Movements Prediction; Multiclass Classification; Feature Engineering; Machine LearningAbstract
In the field of quantified investment, risk quantification and maximum expected return are the problems focused on by the investors. Besides, a powerful toolkit for predicting the stock price movement is also very important for investors. In this paper, five stocks that are components of the SP-500 Index are selected, and the Mean-Variance method is used to optimize the portfolio of the above stocks. Moreover, five machine learning methods are compared to evaluate the performance in the application of stock price movement prediction. The results show that the combination of “AMZN”, “MSFT” and “AAPL” can achieve a good expected return within a low risk. In addition, the Artificial Neural Network method has the highest accuracy in predicting the multiclass stock price movement. Our research has a reference significance for the investors in the application of risk quantification and stock price prediction.
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