Liquor industry stock return prediction based on LSTM model

Junning Chen; Yuwen Cui; Yan Ma

doi:10.1117/12.2646659

23 August 2022 Liquor industry stock return prediction based on LSTM model

Junning Chen, Yuwen Cui, Yan Ma

Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 123301O (2022) https://doi.org/10.1117/12.2646659
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China

Abstract

China has thousands of years of liquor culture, and almost 69 percent of Chinese alcohol consumption is liquor. At the same time, many liquor companies are listed companies, and the stock price is changing. So, stock forecasts for the liquor industry are very important. This article will predict the changes in stock prices in the liquor industry since the epidemic based on the long-short term memory (LSTM) model. First, the data of Moutai, Wuliangye, and Yanghe from June 2020 to June 2021 was selected as a reference. The return rate was calculated according to the close price, and the training set and test set were divided in a ratio of 4 to 1, and then the LSTM model can be constructed to make predictions after data normalization and convert it into the supervised problem. Finally, the results of the neural network prediction model show that the LSTM neural network method can well fit the fluctuation trend of the stock price of the companies, and the mean square error is small, which shows the prediction accuracy is relatively high, and it can reflect the fluctuation trend of stock price to a certain extent. So, this article can be a reference about the stock price for investors who preparing to invest in the liquor industry, mainly in leading liquor companies.

Citation Download Citation

Junning Chen, Yuwen Cui, and Yan Ma "Liquor industry stock return prediction based on LSTM model", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 123301O (23 August 2022); https://doi.org/10.1117/12.2646659

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KEYWORDS

Data modeling

Neural networks

Data conversion

Error analysis

Analytical research

Machine learning

Optimization (mathematics)

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