Deep learning stock price prediction system based on feedforward neural network

Cheng Cheng; Liying Zhu; Hongsheng Cheng

doi:10.1117/12.2646822

7 September 2022 Deep learning stock price prediction system based on feedforward neural network

Cheng Cheng, Liying Zhu, Hongsheng Cheng

Proceedings Volume 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022); 123290H (2022) https://doi.org/10.1117/12.2646822
Event: Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 2022, Changsha, China

Abstract

Stock price prediction has sparked the interest of financial investors and scholars, and it is also a research topic for academics. Because of the non-linearity and fluctuation of stock prices, classic statistical methods for stock price prediction are less than ideal. When it comes to analyzing time series data, the deep learning model provides a lot of advantages. In this paper, we crawl the historical stock price data of GOOGLE and provide a stock prediction system based on a feedforward neural network to further optimize the prediction model. We use RMSE, MAE and MAPE to verify the model’s prediction accuracy. The empirical results indicate that the feedforward neural network provides good effectiveness and feasibility.

Citation Download Citation

Cheng Cheng, Liying Zhu, and Hongsheng Cheng "Deep learning stock price prediction system based on feedforward neural network", Proc. SPIE 12329, Third International Conference on Artificial Intelligence and Electromechanical Automation (AIEA 2022), 123290H (7 September 2022); https://doi.org/10.1117/12.2646822

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