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.
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Deep learning stock price prediction system based on feedforward neural network
Semantic Scholar · Computer Science · 2022
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.