Publication: Corporate integrity and hostile takeover threats: Evidence from machine learning and “CEO luck”
Issued Date
2021-12-01
Resource Type
ISSN
22146369
22146350
22146350
Other identifier(s)
2-s2.0-85116824959
Rights
Mahidol University
Rights Holder(s)
SCOPUS
Bibliographic Citation
Journal of Behavioral and Experimental Finance. Vol.32, (2021)
Suggested Citation
Viput Ongsakul, Pattanaporn Chatjuthamard, Pornsit Jiraporn, Sirithida Chaivisuttangkun Corporate integrity and hostile takeover threats: Evidence from machine learning and “CEO luck”. Journal of Behavioral and Experimental Finance. Vol.32, (2021). doi:10.1016/j.jbef.2021.100579 Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/76872
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Title
Corporate integrity and hostile takeover threats: Evidence from machine learning and “CEO luck”
Abstract
Exploiting an innovative measure of corporate integrity based on machine learning and textual analysis, this paper explores the effect of hostile takeover exposure on corporate integrity. Using a measure of takeover vulnerability principally based on state legislation, we find that a more active takeover market raises corporate integrity, corroborating the notion that the disciplinary mechanism associated with the takeover market induces managers to enhance corporate integrity. Specifically, a rise in takeover exposure by one standard deviation results in an improvement in integrity by 4.00%. Further analysis confirms the conclusion including propensity score matching, entropy balancing, and instrumental-variable analysis. Our study is among the first to employ this novel text-based measure of corporate integrity. Finally, additional analysis based on ”CEO luck” validates the conclusion.