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Two essays on stock markets


This thesis contains two pieces of empirical study on market efficiency. The first essay tests the semi-strong form of market efficiency in the U.S. We use sell-side analyst target prices as publically available information and test the performance of a mean-variance optimized portfolio which is based on the Treynor and Black model. We focus on constituents of S&P 500 index as our sample universe. During the period of beck-testing from 2004 to 2010, we find that the dynamically rebalanced portfolio beats the market in 6 out of 7 years and that the strategy generates significant risk-adjusted abnormal returns.

In the second essay we study the post-earnings-announcement drift (PEAD) phenomenon, a well-documented market anomaly, on the French stock market. Our empirical study devises a difference-in-difference policy experiment to test if trading activities by individual investors contribute to the magnitude of PEAD. We exploit a recent policy reform on the French stock market, which significantly increased speculative trading costs of individual investors and reduced their trading activities. The impact of reform is found twice as large on individual contrarian traders than momentum traders. Using a group of unaffected stocks to control for potential non-experimental factors, we find magnitude of PEAD dropped significantly after the reform in the experimented group but not in the experimented group but not in the control group. / published_or_final_version / Economics and Finance / Doctoral / Doctor of Philosophy

Identiferoai:union.ndltd.org:HKU/oai:hub.hku.hk:10722/191196
Date January 2013
CreatorsDong, Wei, 董炜
ContributorsGao, X, Zhou, X
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Source SetsHong Kong University Theses
LanguageEnglish
Detected LanguageEnglish
TypePG_Thesis
Sourcehttp://hub.hku.hk/bib/B50662211
RightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works., Creative Commons: Attribution 3.0 Hong Kong License
RelationHKU Theses Online (HKUTO)

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