Abstract
In the process of stock price forecasting, there are the following problems: how to find the more effective factors for stock price forecasting, and how to calculate the weight of the constructed stock correlation factor sets. To solve the above problems, this paper proposes a method of factor construction in the field of stock price prediction based on genetic programming. The method can automatically construct the factor by reading the original data set of the stock, and calculate the weight of each factor. In addition, this paper also proposes a new crossover operator, which can dynamically adjust the selection of crossover nodes by using the information in the execution process of genetic programming algorithm, so as to improve the quality of the constructed factor set. A lot of experiments have been carried out with this method. The results show that the factors constructed by this method can improve the accuracy of the stock price prediction algorithm in most cases.
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Acknowledgement
This study was supported by the Key Project of the National Natural Science Foundation of China (U1908212),Central government guided local science and Technology Development Fund Project(1653137155953), the Taking Lead Science and Technology Research Project of Liaoning (2021jh1/10400006), and the General Project of Liaoning Provincial De-partment of Education (LJKMZ20220613).
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Bao, H., Zhang, C., Zhang, C., Zhang, B. (2023). An Improved Genetic Programming Based Factor Construction for Stock Price Prediction. In: Zhang, S., Zhang, Y. (eds) Artificial Intelligence Logic and Applications. AILA 2023. Communications in Computer and Information Science, vol 1917. Springer, Singapore. https://doi.org/10.1007/978-981-99-7869-4_18
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DOI: https://doi.org/10.1007/978-981-99-7869-4_18
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