Abstract
Stock price forecasting is one of the hottest research fields in recent years because it can help policymakers and investors make appropriate decisions. The method of deep learning can make the prediction more accurate. Therefore, in this paper, a Generative Adversarial Network (GAN)model is proposed for the prediction of stock price. In this model, the Gated Recurrent Units (GRU) are used as the generator to predict the future stock price by training historical data. Convolutional neural networks (CNN) are used as discriminators to distinguish real data from predicted data. We selected Apple's stock price as the research object, extracted the highest price, lowest price, opening price, S&P 500 index, and other indicators as characteristics, and added the feature of the news index into the research, which can increase the accuracy of the forecast. Finally, the accuracy of the prediction results was determined by calculating the value of RMSE.
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Wang, T., Yu, M., Wang, P. (2023). Using Generative Adversarial Network to Forecast Stock Price. In: Gupta, R., Bartolucci, F., Katsikis, V.N., Patnaik, S. (eds) Recent Advancements in Computational Finance and Business Analytics. CFBA 2023. Learning and Analytics in Intelligent Systems, vol 32. Springer, Cham. https://doi.org/10.1007/978-3-031-38074-7_24
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DOI: https://doi.org/10.1007/978-3-031-38074-7_24
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