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.
- Sprache
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Englisch
- Erschienen in
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Journal: China Journal of Accounting Research ; ISSN: 1755-3091 ; Volume: 7 ; Year: 2014 ; Issue: 3 ; Pages: 179-201 ; Amsterdam: Elsevier
- Klassifikation
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Management
- Thema
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Aggregate insider trading
Large shareholder trading
Information hierarchy
Corporate governance
Emerging market
- Ereignis
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Geistige Schöpfung
- (wer)
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Zhu, Chafen
Wang, Li
Yang, Tengfei
- Ereignis
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Veröffentlichung
- (wer)
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Elsevier
- (wo)
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Amsterdam
- (wann)
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2014
- DOI
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doi:10.1016/j.cjar.2014.08.001
- Handle
- Letzte Aktualisierung
- 10.03.2025, 10:43 UTC
Datenpartner
ZBW - Deutsche Zentralbibliothek für Wirtschaftswissenschaften - Leibniz-Informationszentrum Wirtschaft. Bei Fragen zum Objekt wenden Sie sich bitte an den Datenpartner.
Objekttyp
- Artikel
Beteiligte
- Zhu, Chafen
- Wang, Li
- Yang, Tengfei
- Elsevier
Entstanden
- 2014