Author(s)

Tong Wang ¹, Panji Wang ¹, Menghan Yu ¹

Affiliation(s)

¹ College of Science and Technology, Wenzhou-Kean University, Wenzhou, Zhejiang Province, 325006, China

Corresponding Author

Menghan Yu

ABSTRACT

The prediction of stock price has been one of the hot research fields in recent years. Although the trend of stock price is non-linear and complex, and affected by the global situation, corporate decisions and other factors, it is difficult to accurately predict the stock price. However, since the change of stock price directly affects the interests of investors, stock price prediction can help people to provide a reference information for future decision-making. In previous studies, neural networks and deep learning have shown good results in stock price prediction. In this paper, two models of LSTM and CNN are used to forecast MRF stock price respectively, and the predicted results are analyzed and compared. We used stock data from January 1, 2013 to May 18, 2018, including four characteristic values: the highest price, the lowest price, the opening price and the closing price. The data of the first 1060 trading days were used to train the two models respectively, and the data of the last 265 trading days were used to test the models. The results show that the prediction effect of CNN model is more accurate than that of LSTM model.

KEYWORDS

Convolutional Neural Networks, Long Short-Term Memory, Stock Price Prediction

CITE THIS PAPER

Tong Wang, Panji Wang, Menghan Yu, Compare CNN and LSTM Model to Forecast the Stock Price. Financial Engineering and Risk Management (2022) Vol. 5: 44-52. DOI: http://dx.doi.org/10.23977/ferm.2022.050607.

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