Forecasting the stock price of vaccine manufacturers in China using machine learning and econometrics model

Jiyuan Gui; Xiaoyun Wu

doi:10.1117/12.2647506

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.

Citation Download Citation

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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