Advances in Economics, Management and Political Sciences
- The Open Access Proceedings Series for Conferences
Proceedings of the 2nd International Conference on Financial Technology and Business Analysis
Series Vol. 49 , 01 December 2023
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Shanghai Stock Exchange Composite Index Prediction Using Macro Variables Based on ARIMAX Model
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Abstract
Stock market prediction has always been a prevailing topic among investors and researchers. Among numerous market index, the Shanghai Stock Exchange Composite Index (SSE Index) is recognized as one of the most indicative stock indexes in China's A-share market. As it is discovered that the fluctuations of SSE index and exchange rate (USD/CNY) displays highly similar pattern since the subprime mortgage crisis, this study aims to use macro variables (including exchange rate) to predict the SSE index based on ARIMAX model. The data are collected since 2006 and based on which ARIMA (14,1,4) model is generated. Distinct macro variables are added in this ARIMA model respectively and it concludes that the incorporation of exchange rate with certain lags can significantly increase the fitting degree. Cross validations on different lengths of validation sets are implemented, which shows that the model can make accurate forecast in relatively short term. The result manifests increasing accuracy when incorporating certain explanatory regressive factors, which might provide valuable and enlightening information in short term for researchers or investors.
Keywords
SSE Index, China’s a-share market, ARIMAX model, macroeconomic variables, exchange rate
References
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Data Availability
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 the 2nd International Conference on Financial Technology and Business Analysis
- ISBN (Print)
- 978-1-83558-145-2
- ISBN (Online)
- 978-1-83558-146-9
- Published Date
- 01 December 2023
- Series
- Advances in Economics, Management and Political Sciences
- ISSN (Print)
- 2754-1169
- ISSN (Online)
- 2754-1177
- DOI
- 10.54254/2754-1169/49/20230485
- Copyright
- 01 December 2023
- Open Access
- This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited