Abstract
With the rapid rise of economic globalization and digital economics, the rapid development of the global economy has been promoted. As the most important part of the financial industry, the stock market has an important impact on economic fluctuations. How to improve the changing trend of stock market fluctuations has become a hot topic that many scholars and investors pay most attention to. This paper uses the XGBoost model to train the opening price, closing price, highest price, lowest price, trading volume, change, adjusted closing price, and converted time data information in the processed stock historical data set, and train it The results are saved. Then input each attribute into the LSTM model for prediction, and use the prediction result of each attribute as the test set of prediction after XGBoost training, and continuously adjust the parameters of each model, and finally get the optimal stock fluctuation prediction model, LSTM_XGBoost model. The LSTM_XGBoost model is applied to the five stocks ES = F, YM = F, AAPL, SI = F, and CL = F to predict the rise and fall of five stocks. The model is compared and verified by five evaluation indexes: the root mean square error RMSE, the average absolute error MAE, the coefficient of determination R2, the accuracy rate, and the f1-score. It is found that the LSTM-XGBoost model proposed in this paper has risen and fallen in stocks. There is a certain degree of stability and feasibility in the forecast.
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Acknowledgement
The paper is funded by Dongguan social science and technology development (general) project (No. 2020507154645) in 2020 and the key platform construction leap up program project of Guangdong University of science and technology: network engineering application technology research center of Guangdong University of science and technology.This work is supported by Natural Science Foundation of Guangdong Province of China with No. 2020A1515010784.
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Yu, S., Tian, L., Liu, Y., Guo, Y. (2021). LSTM-XGBoost Application of the Model to the Prediction of Stock Price. In: Sun, X., Zhang, X., Xia, Z., Bertino, E. (eds) Artificial Intelligence and Security. ICAIS 2021. Lecture Notes in Computer Science(), vol 12736. Springer, Cham. https://doi.org/10.1007/978-3-030-78609-0_8
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