Download pdf
The Impact of China's Pandemic Deregulation Policy on SSEC and SZSE Indexes
- 1 Shanghai Lixin University of Accounting and Finance
* Author to whom correspondence should be addressed.
Abstract
In early stages of the COVID-19 outbreak, Chinese stock market experienced a sharp decline as investors became increasingly concerned about the economic consequences of the pandemic. As the pandemic continued to spread globally, the Chinese government implemented strict measures to control its spread, which included lockdowns, travel restrictions, and other forms of social distancing. Although these measures worked, economy was drastically affected with many stores closing down and consumer expenditures significantly declining. This, meanwhile, led to a decrease in corporate profits and a reduction in investor confidence. However, on December 8, 2022, the Chinese government issued a pandemic deregulation policy identifying people would return to their normal life. In this paper, prices of the Shanghai Stock Exchange Composite (SSEC) index and Shenzhen Securities Component (SZSE) index were retrieved and ARIMA method was adopted to predict the stock prices for a period after the pandemic. The author compared forecast prices with the actual stock prices and then analyzed the implications of the deregulation policy on the stock market. These two indexes are only a snapshot of the Chinese economy, and certain informative feedback can be obtained through this study, which is helpful to relevant investors and policy makers.
Keywords
COVID-19 pandemic, SSEC index, SZSE index, Arima model, forecast
[1]. H Ding, C Fan, L Qiang (2014). Research of the construction of generalized price index and its implication for monetary policy in China. China Economic Issues:88-98.
[2]. Y Zhou (2014). Research of the construction of generalized price index and its implication for monetary policy in China. China Economic Issues:88-98.
[3]. Z Xu, W Jiang, S Yang (2015). The impact of interest rates on China's stock market--an empirical study based on the SSEC and SZSE. Credit, 33(02):79-83.
[4]. Y Liu, Y Song (2012). Research on the impact of financial crisis on stock market predictability--analysis of Shenzhen Cheng Index and Dow Jones Index as an example. China Securities and Futures (04):63-64.
[5]. S Zhu (2009). Predictability analysis of SSEC and SZSE. Mathematical Theory and Applications, 29(02):124-128.
[6]. Y Feng, D Wang (2021). The impact of the COVID-19 pandemic on the global economy and China's response. Low Carbon World,11(09):255-256.
[7]. J Sun, H Li, J Yang (2022). The impact of the COVID-19 pandemic on global macroeconomics and value chain structure. Research on Finance and Economics (01):52-62.
[8]. Z Xia (2022). Research on the impact of the COVID-19 pandemic on the inter-market spillover effect of the stock market. Yunnan Normal University.
[9]. L Wu, Z Jiang, F Wang (2022). An empirical study on the volatility trend of US and Chinese stock markets under the new "black swan"-based GARCH model. Journal of Hubei University of Economics (Humanities and Social Sciences Edition), 19(02):51-55.
[10]. J Chen, Y Chen (2022). China can prepare to end its zero-COVID policy. Nature Medicine, 28(6): 1104-1105.
[11]. Q Wang, L Liu (2022). Pandemic or panic? A firm-level study on the psychological and industrial impacts of COVID-19 on the Chinese stock market. Financial Innovation, 8(1): 36.
[12]. Burki T (2022). Dynamic zero COVID policy in the fight against COVID. The Lancet Respiratory Medicine, 10(6): e58-e59.
[13]. Dorlach T (2023). Social policy responses to Covid-19 in the global south: Evidence from 36 countries. Social Policy and Society, 22(1): 94-105.
[14]. Mistur E M, Givens J W, Matisoff D C (2023). Contagious COVID‐19 policies: Policy diffusion during times of crisis. Review of Policy Research, 40(1): 36-62.
[15]. Y Du (2018). Application and analysis of forecasting stock price index based on combination of ARIMA model and BP neural network[C]//2018 Chinese control and decision conference (CCDC). IEEE: 2854-2857.
[16]. Fortune Times (2023). The road to economic recovery after the pandemic (01):17.
[17]. Christopher Pissarides (2023). Analysis of the world and China in the post-pandemic era from a labor market perspective. International Finance:6-8.
[18]. H Cui, M Li, J Chu (2023). An asymmetric study of herding effect in Chinese stock market. Journal of Reading River,15(02):123-134+174.
[19]. Phillips P C B, Perron P (1998). Testing for a unit root in time series regression. Biometrika, 75(2): 335-346.
[20]. Mushtaq R (2011). Augmented dickey fuller test.
[21]. S Ho, M Xie (1998). The use of ARIMA models for reliability forecasting and analysis. Computers & industrial engineering, 35(1-2): 213-216.
[22]. Mondal P, Shit L, Goswami S (2014). Study of effectiveness of time series modeling (ARIMA) in forecasting stock prices. International Journal of Computer Science, Engineering and Applications, 4(2): 13.
[23]. Newbold P (1983). ARIMA model building and the time series analysis approach to forecasting. Journal of forecasting, 2(1): 23-35.
[24]. Hong H, Stein J C (1999). A unified theory of underreaction, momentum trading, and overreaction in asset markets. The Journal of finance, 54(6): 2143-2184.
Cite this article
Chen,H. (2023). The Impact of China's Pandemic Deregulation Policy on SSEC and SZSE Indexes. Advances in Economics, Management and Political Sciences,45,18-25.
Data availability
The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
Disclaimer/Publisher's Note
The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of EWA Publishing and/or the editor(s). EWA Publishing and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
About volume
Volume title: Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
© 2024 by the author(s). Licensee EWA Publishing, Oxford, UK. This article is an open access article distributed under the terms and
conditions of the Creative Commons Attribution (CC BY) license. Authors who
publish this series agree to the following terms:
1. Authors retain copyright and grant the series right of first publication with the work simultaneously licensed under a Creative Commons
Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this
series.
2. Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the series's published
version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial
publication in this series.
3. Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and
during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See
Open access policy for details).