Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/119158
Type: | Thesis |
Title: | The asymmetric impact of positive and negative news on stock return synchronicity |
Author: | Lu, Sha |
Issue Date: | 2017 |
School/Discipline: | Business School |
Abstract: | I examine the asymmetric impact of positive and negative information on stock return synchronicity that occurs from a change in short-sale constraints. Using a unique Chinese dataset that segments the trading activity of individual and institutional investors, I am able to observe each investor group’s relative preference for incorporating firm-level as opposed to market-level negative information. My results show the existence of an asymmetric impact. Moreover, institutional (individual) traders impound more firm-specific (market-wide) negative information relative to market-wide (firm-specific) negative information when they are allowed to short-sell. These findings suggest that the sentiment of the information that is being released plays a significant role in determining the level of stock return synchronicity. The extent to which firm-specific negative information is impounded into prices because of the change of short-sale constraints depends on investor type. |
Advisor: | Zurbruegg, Ralph |
Dissertation Note: | Thesis (Ph.D.) -- University of Adelaide, Business School, 2017 |
Keywords: | News return synchronicity investor type short-sale constraint |
Provenance: | This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals |
Appears in Collections: | Research Theses |
Files in This Item:
File | Description | Size | Format | |
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Lu2017_PhD.pdf | 1.33 MB | Adobe PDF | View/Open |
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