Artikel

Swimming ducks forecast the coming of spring: The predictability of aggregate insider trading on future market returns in the Chinese market

This study systematically examines the ability of aggregate insider trading to predict future market returns in the Chinese A-share market. After controlling for the contrarian investment strategy, aggregate executive (large shareholder) trading conducted over the past six months can predict 66% (72.7%) of market returns twelve months in advance. Aggregate insider trading predicts future market returns very accurately and is stronger for insiders who have a greater information advantage (e.g., executives and controlling shareholders). Corporate governance also affects the predictability of insider trading. The predictability of executive trading is weakest in central state-owned companies, probably because the 'quasi-official' status of the executives in those companies effectively curbs their incentives to benefit from insider trading. The predictive power of large shareholder trading in private-owned companies is higher than that in state-owned companies, probably due to their stronger profit motivation and higher involvement in business operations. This study complements the literature by examining an emerging market and investigating how the institutional context and corporate governance affect insider trading.

Language
Englisch

Bibliographic citation
Journal: China Journal of Accounting Research ; ISSN: 1755-3091 ; Volume: 7 ; Year: 2014 ; Issue: 3 ; Pages: 179-201 ; Amsterdam: Elsevier

Classification
Management
Subject
Aggregate insider trading
Large shareholder trading
Information hierarchy
Corporate governance
Emerging market

Event
Geistige Schöpfung
(who)
Zhu, Chafen
Wang, Li
Yang, Tengfei
Event
Veröffentlichung
(who)
Elsevier
(where)
Amsterdam
(when)
2014

DOI
doi:10.1016/j.cjar.2014.08.001
Handle
Last update
10.03.2025, 11:43 AM CET

Data provider

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ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. If you have any questions about the object, please contact the data provider.

Object type

  • Artikel

Associated

  • Zhu, Chafen
  • Wang, Li
  • Yang, Tengfei
  • Elsevier

Time of origin

  • 2014

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