Abstract
The changes of stock market and the predictions of the price have become hot topics. When machine learning emerged, it has been used in the stock market forecast research. In recent years, the vertical development of machine learning has led to the emergence of deep learning. Therefore, this paper proposes and realizes the CNN and LSTM forecasting model with financial news and historical data of stock market, which uses deep learning methods to quantify text and mine the laws of stock market changes and analyze whether they can predict changes. According to the results from this paper, this method has certain accuracy in predicting the future changes of the stock market, which provides help to study the inherent laws of stock market changes.
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Zhao, L., Zhao, Q., Yang, J., Wang, T., Li, Q.: Quantitative analysis of the impact of financial news on Chinese stock market. J. Shandong Univ.: Sci. Ed. 47(7), 70–75 (2012)
Chong, E., Han, C., Park, F.: Deep learning networks for stock market analysis and prediction. Expert Syst. Appl. 83, 187–205 (2017)
Guo, D., Liu, X., Zheng, Q.: Internet short text classification method based on convolutional neural network. Comput. Modernization 4, 78–81 (2017)
Chang, Q.: Research on application of web text mining based on machine learning algorithm. Master thesis, Tianjin University, Tianjin (2010)
Chen, W., Yan, Z., Chai, K., Lau, C.: Stock market prediction using neural network through news on online social networks. In: International Smart Cities Conference (2017)
Meng, X., Yang, Y., Zhao, X.: Financial news and stock market investment strategy research: text mining based on financial website. Invest. Res. 35(8), 29–37 (2016)
Vargas, M., Lima, B., Evsukoff, A.: Deep learning for stock market prediction from financial news articles. In: IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications, pp. 60–65 (2017)
Li, Y., Gai, K., Ming, Z., Zhao, H., Qiu, M.: Intercrossed access controls for secure financial services on multimedia big data in cloud systems. ACM Trans. Multimed. Comput. Commun. Appl. 12(4), 67:1–67:18 (2016)
Li, Y., Dai, W., Ming, Z., Qiu, M.: Privacy protection for preventing data over-collection in smart city. IEEE Trans. Comput. 65(5), 1339–1350 (2016)
Qiu, M., Ming, Z., Chen, Z., Qin, X.: Security-aware optimization for ubiquitous computing systems with SEAT graph approach. J. Comput. Syst. Sci. 79(5), 518–529 (2013)
Shun, R.: Research on price trend prediction model of US stock index based on LSTM neural network. Master thesis, Capital University of Economics and Business, Beijing (2016)
Acknowledgements
The research in this paper was supported by the National Natural Science Foundation of China (Nos. 61672358 and Nos. 61836005).
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Cai, S., Feng, X., Deng, Z., Ming, Z., Shan, Z. (2018). Financial News Quantization and Stock Market Forecast Research Based on CNN and LSTM. In: Qiu, M. (eds) Smart Computing and Communication. SmartCom 2018. Lecture Notes in Computer Science(), vol 11344. Springer, Cham. https://doi.org/10.1007/978-3-030-05755-8_36
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DOI: https://doi.org/10.1007/978-3-030-05755-8_36
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