Paper
23 August 2022 Forecasting the stock price of vaccine manufacturers in China using machine learning and econometrics model
Jiyuan Gui, Xiaoyun Wu
Author Affiliations +
Proceedings Volume 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022); 1233029 (2022) https://doi.org/10.1117/12.2647506
Event: International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 2022, Huzhou, China
Abstract
Due to the sudden outbreak of COVID-19, there is a high volatility in stock price of vaccine manufacturers in China (Between December 15, 2020 and December 13, 2021, average monthly volatility of these companies is 986). The aim of this paper is to compare the price prediction result of four algorithms: Multivariable Regression Model (MLR), Auto Regressive Integrated Moving Average Model (ARIMA), Back Propagation Neural Network Model (BP-NN), Random Forest Regression (RF), and decide which one has a better performance. Data from December 2020 to December 2021 is collected from Wind and the 8 stocks is selected in leading companies in vaccine industry. It turns out that BP-NN Model gives the best result in predicting stock price of vaccine manufacturers, measured using commonly used indicator, i.e., root-mean-square error (RMSE) and R Square (R2). So next time in the similar situation, BP-NN can be seen as a powerful tool to help us make decision. This paper would help investors build an optimal strategy in selecting stocks in terms of pharmaceutical industry.
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Jiyuan Gui and Xiaoyun Wu "Forecasting the stock price of vaccine manufacturers in China using machine learning and econometrics model", Proc. SPIE 12330, International Conference on Cyber Security, Artificial Intelligence, and Digital Economy (CSAIDE 2022), 1233029 (23 August 2022); https://doi.org/10.1117/12.2647506
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KEYWORDS
Autoregressive models

Data modeling

Manufacturing

Machine learning

Performance modeling

Composites

Systems modeling

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