Research on the Returns of SSE STAR Market Chip Index Based on the ARMA Model
DOI:
https://doi.org/10.54691/bcpbm.v30i.2498Keywords:
ARMA model; Time series; Chip indexAbstract
Since 2021, China’s semiconductor industry and chip industry have been in the hot stage. While the scale of the two industries has continued to expand, there are also problems of bubble economy and excessive competition. In the first half of 2022, the stock market has entered an adjustment cycle. In order to predict the returns trend of the adjustment cycle and help investors make reasonable decisions. This paper uses AIC and BIC criteria to determine the order of the model, test the significance of the model and its parameters, then build the ARMA(2,1) model to predict the closing price of the SSE STAR Market Chip Index from January 2nd, 2020 to February 21st, 2022. According to the results, this paper finds out that both fitting effect and forecasting effect are good. The index will maintain a downward trend in the next 30 periods. Through the above empirical analysis, this paper puts forward two suggestions: firstly, in the face of industry differentiation, people need to invest rationally; secondly, semiconductor companies and chip companies should attach importance to mergers and acquisitions.
Downloads
References
Zhu Jing. The current status of semiconductor parts industry and suggestions for China. China Integrated Circuit, 2022, 31(04): 10-17+36.
Xie Shenkun, Wang Zhizhe, Ren Yan et al. Challenges and opportunities of automotive chip shortage. Electronic Product Reliability and Environmental Testing, 2022, 40(03): 115-119.
Yao Yuqing. Prediction of stock return based on ARMA model. Scientific Journal of Economics and Management Research, 2021, 3(9): 183-191.
Jeffrey E Jarrett, Eric Kyper. ARIMA modeling with intervention to forecast and analyze Chinese stock prices. International Journal of Engineering Business Management, 2011, 3(3): 716-723.
Zhang Huilei. Empirical analysis of short-term and long-term volatility of SSE 50 ETF——based on the ARMA-GARCH model. Kunming, China, 2021.
Yang Yuyuan, Zhang Mei. Empirical analysis of stock price based on ARIMA model. Science & Technology Information, 2021, 19(29): 121-123+127.
Sun Yi, Zhou Longlong. Teaching of financial time series ARIMA model based on Python. Modern Information Technology, 2021, 5(10): 192-195.
Li Chenggang, Yang Bing, Li Min. Forecasting analysis of Shanghai stock index based on ARIMA model. MATEC Web of Conferences, 2017, 100: 02029.
K Murali, Sk Nafeez Umar, D Chandrakesavulu Naidu et al. Analysis and forecast of index monthly time series based on ARIMA model. Journal of Innovation and Social Science Research, 2021, 8(7): 20.
Paulo Rotela Junior, Fernando Luiz Riêra Salomon, Edson de Oliveira Pamplona. ARIMA: an applied time series forecasting model for the Bovespa stock index. Applied Mathematics, 2014, 5(21): 3383-3391.
Wang Yan, Times series analysis with R. Beijing: Renmin University of China Press, 2005.






