Forecasting the stock price of vaccine manufacturers in China Using Machine Learning and Econometrics Model

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. © 2022 SPIE.

COVID-19 Has Negative Impacts on Urban Traffic Recovery: A Statistical Analysis Study Based on Thirty Cities' Data in China

To ensure traffic safety, many related works have been done to avoid traveler injury during trips. However, new public health issues threaten traffic safety because travelers might get ill during trips. The more people infected by COVID-19, the more unsafe urban traffic becomes. This paper aims to verify whether COVID-19 has negative impacts on urban traffic recovery. Based on thirty Chinese cities' data, robust fixed-effects (within) regression was adopted to analyze impacts with a linear regression method. The regression results suggest that Urban Traffic Activity Index (UTAI) was positively associated with UTAI itself with short-term effect, meaning that UTAI could recover by itself, and new confirmed cases (NC) were negatively associated with UTAI with long-term effect, meaning that NC would prevent UTAI recovery. The findings also suggest that it is better for city governments to eliminate outbreaks before restarting economies. Future directions include improving models, grouping cities, and expanding data. © 2021 CICTP 2021: Advanced Transportation, Enhanced Connection - Proceedings of the 21st COTA International Conference of Transportation Professionals. All rights reserved.