File Download
  Links for fulltext
     (May Require Subscription)
Supplementary

postgraduate thesis: How do peer firms affect a firm's earnings forecast desicion around the earnings announcement date?

TitleHow do peer firms affect a firm's earnings forecast desicion around the earnings announcement date?
Authors
Issue Date2015
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Wang, X. [王新路]. (2015). How do peer firms affect a firm's earnings forecast desicion around the earnings announcement date?. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5576753
AbstractI investigate how peer firms affect a firm’s management earnings forecast decision around its earnings announcement date. I find that the probability of a firm issuing a good news forecast is positively related to the proportion of peer firms issuing good news forecasts, but a firm’s bad news forecast is not affected by the bad news forecasts of peer firms. Furthermore, I find that the positive influence of peers’ good news forecasts on the good news forecast of the following firm is weaker with the presence of the firm’s higher transient institutional ownership, and stronger when analyst followings of the firm are larger. In addition, I find that firms do not fabricate good news to follow good news forecasts of peer firms, only choosing to reveal good news. These findings suggest that managers herd on early good news forecasts to avoid their stock prices being discounted by investors, but not on early bad news forecasts because managers hope that subsequent favorable events may bury early bad news.
DegreeDoctor of Philosophy
SubjectCorporations - Accounting
Dept/ProgramBusiness
Persistent Identifierhttp://hdl.handle.net/10722/227899
HKU Library Item IDb5576753

 

DC FieldValueLanguage
dc.contributor.authorWang, Xinlu-
dc.contributor.author王新路-
dc.date.accessioned2016-07-22T23:18:04Z-
dc.date.available2016-07-22T23:18:04Z-
dc.date.issued2015-
dc.identifier.citationWang, X. [王新路]. (2015). How do peer firms affect a firm's earnings forecast desicion around the earnings announcement date?. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5576753-
dc.identifier.urihttp://hdl.handle.net/10722/227899-
dc.description.abstractI investigate how peer firms affect a firm’s management earnings forecast decision around its earnings announcement date. I find that the probability of a firm issuing a good news forecast is positively related to the proportion of peer firms issuing good news forecasts, but a firm’s bad news forecast is not affected by the bad news forecasts of peer firms. Furthermore, I find that the positive influence of peers’ good news forecasts on the good news forecast of the following firm is weaker with the presence of the firm’s higher transient institutional ownership, and stronger when analyst followings of the firm are larger. In addition, I find that firms do not fabricate good news to follow good news forecasts of peer firms, only choosing to reveal good news. These findings suggest that managers herd on early good news forecasts to avoid their stock prices being discounted by investors, but not on early bad news forecasts because managers hope that subsequent favorable events may bury early bad news.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.subject.lcshCorporations - Accounting-
dc.titleHow do peer firms affect a firm's earnings forecast desicion around the earnings announcement date?-
dc.typePG_Thesis-
dc.identifier.hkulb5576753-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineBusiness-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b5576753-
dc.identifier.mmsid991011254209703414-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats