Document Type
Article
Date of Original Version
6-1-2020
Abstract
Driven by the advancement of technology, emergence of financial institutional bodies superseding geographical constraints, as well as cross-regional liberalization paired with the removal of restrictions, global stock markets tend to become increasingly interconnected. On the one hand, it is believed that the globalization has made stock markets more efficient and alleviated the inherent risk thereof, resulting from greater access to financial assets, and thus the possibility to diversify therein. On the other hand, this may, however, lead to increased stock price volatility and trading instability, due to the major stock markets being increasingly correlated. Increasing interconnectedness between companies leads to the assumption that stock prices especially depend on the business sector and industry in which they operate. Thus, the interest in correlation network models is on the rise. However, despite the large number of literature providing network models, there is still uncertainty about their validity as well as true predictive power. Thus, this paper aims to identify stock return correlations between companies of selected industries. The quantitative analysis of historical daily stock returns is encompassing the correlation of the Japanese and Chinese corporate data from pharmaceutical, energy and banking sectors for the time period from 2009 until 2015 with relevant external events. The results show that the Japanese market reacts strongly to specific events during the observation and does not affect the Chinese market in any way, while events relating to the Chinese market have an immediate impact on the Japanese market behaviour.
Publication Title, e.g., Journal
Asia Pacific Management Review
Volume
25
Issue
2
Citation/Publisher Attribution
Schuenemann, Jan H., Natalia Ribberink, and Natallia Katenka. "Japanese and Chinese Stock Market Behaviour in Comparison – an analysis of dynamic networks." Asia Pacific Management Review 25, 2 (2020): 99-110. doi: 10.1016/j.apmrv.2019.10.002.
Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.