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Forecasting the Shanghai Composite Index Using the ARIMA Model
- 1 School of Public Administration and Policy, Renmin University of China, Beijing, China
* Author to whom correspondence should be addressed.
Abstract
In this study, the ARIMA (AutoRegressive Integrated Moving Average) model will be used to forecast future daily closing prices of the Shanghai Composite Index from January 1, 2017. Among the models tested — ARIMA(4,1,0), ARIMA(4,2,0) and ARIMA(4,1,1), we chose the ARIMA(4,1,1) model to be most appropriate due to having the lowest AIC and BIC values as well as it gives best residual variance based on this accuracies. The model was accurate in capturing historical trends of the Shanghai Composite Index and made sensible short-term forecasts. While the model worked well on historical data, its forecast was at odds with how the Shanghai Composite Index has dived lately in real life. The deviation is due to external economic conditions, market sentiment and the model’s inability to cope with non-linear market dynamics. To overcome these hindrances, additional investigations should aim to accommodate for exogenous variables, enable non-linear models — such as GARCH — or update the model in real time to better capture market underlying financial markets. The present study mostly contributes to the literature on financial forecasting as well as ARIMA models — also in Chinese stock market.
Keywords
Shanghai Composite Index, ARIMA model, Time series forecasting
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Cite this article
Li,Y. (2025). Forecasting the Shanghai Composite Index Using the ARIMA Model. Advances in Economics, Management and Political Sciences,144,107-115.
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The datasets used and/or analyzed during the current study will be available from the authors upon reasonable request.
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Volume title: Proceedings of ICFTBA 2024 Workshop: Finance's Role in the Just Transition
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