High-speed rail and stock return comovement in China

Zunxin Zheng, Zhongjie Qiu, Mengjia Li and Wenjie Ding

Research in International Business and Finance, 2024, vol. 67, issue PA

Abstract: This study explores the impact of China’s high-speed rail network on reducing local bias and fostering capital market integration. Our research reveals that implementing high-speed rail in a city decreases the return comovement of its local stocks but increases its return comovement with the national stock market. Our results are robust and consistent across various model specifications. After examining the underlying economic mechanism, we find that the impact of high-speed rail on stock return comovement is stronger for small firms, non-state-owned enterprises (non-SOEs), firms with poor corporate governance, and firms’ headquarters in more segmented and less-developed regions. This effect did not change after controlling for other information-dissemination channels and regional economic development. Overall, geographical proximity contributes to information transfer and market efficiency.

Keywords: High-speed rail; Return comovement; Information transfer; Local bias (search for similar items in EconPapers)
JEL-codes: G10 G12 G14 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0275531923002337
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:67:y:2024:i:pa:s0275531923002337

DOI: 10.1016/j.ribaf.2023.102107

Access Statistics for this article

Research in International Business and Finance is currently edited by T. Lagoarde Segot

More articles in Research in International Business and Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().