
This dissertation, "Informational Content of Options Trading on Equity Returns and Corporate Events" by Li, Ge,, was obtained from The University of Hong Kong (Pokfulam, Hong Kong) and is being sold pursuant to Creative Commons: Attribution 3.0 Hong Kong License. The content of this dissertation has not been altered in any way. We have altered the formatting in order to facilitate the ease of printing and reading of the dissertation. All rights not granted by the above license are retained by the author. Abstract: This dissertation consists of three empirical studies about the informational content of options trading on subsequent equity returns and around major corporate events, such as mergers and acquisitions, and bankruptcies. The first chapter examines the informational content of options trading on acquirer announcement returns. I show that implied volatility spread predicts positively on the cumulative abnormal return (CAR), and implied volatility skew predicts negatively on the CAR. The predictability is much stronger around actual merger and acquisition (M&A) announcement days, compared with pseudo-event days. The prediction is weaker if pre-M&A stock price has incorporated part of the information, but stronger if acquirer's options trading is more liquid. Finally, I find that higher relative trading volume of options to stock predicts higher absolute CARs. The relation also exists among the target firms. In the second chapter, I reassess the presence of pre- bankruptcy-filing informed and insider trades by examining the information content of options trading before bankruptcy announcements. I find that bankruptcy filing returns are not significantly related to pre-filing insider stock trading. However, filing returns are significantly negatively related to pre-filing insider and informed options trading. The informational content of options trading reduces with options illiquidity and the amount of information impounded into pre-filing sto
Page Count:
0
Publication Date:
2017-01-27
ISBN-10:
1361379731
ISBN-13:
9781361379738
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