Dissertation
Equity-based Compensation and Insider Trading
Doctor of Philosophy (PhD), Washington State University
01/2010
Handle:
https://hdl.handle.net/2376/2794
Abstract
My dissertation consists of two essays related to executive equity-based compensation and insider trading. In the first essay, I examine the economic rationale for a well-debated compensation practice: option backdating. Using a sample of backdating firms and matching firms, I provide strong evidence in support of incentive and retention-based explanations for backdating. Backdating firms tend to be younger, faster growing, and operating in a more competitive labor market where competition for highly-skilled talent is fierce. Further, rather than experiencing poor performance, backdating firms tend to outperform matching firms in both prior- and post-backdating years. These results suggest that backdating reflects a firm's demand for valuable employees rather than a manifestation of agency problems.
In the second essay, I examine the information content of prior investment announcement insider trading and investigate whether insider trading serves as a signal of the quality of the project or firm prospects. The results suggest that net managers (or other insiders) purchasing portends higher announcement period returns. The information content of managers' trades varies and the signal effect is stronger for those trades made by managers with relative higher compensation risk. Further, although managers signal firm value consistently through their trading, the market appears not to revise its valuation until large corporate event such as capital investment occurs.
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Details
- Title
- Equity-based Compensation and Insider Trading
- Creators
- Hongyan Fang
- Contributors
- David A Whidbee (Advisor)John R Nofsinger (Committee Member)Donna L Paul (Committee Member)
- Awarding Institution
- Washington State University
- Academic Unit
- Carson College of Business
- Theses and Dissertations
- Doctor of Philosophy (PhD), Washington State University
- Number of pages
- 112
- Identifiers
- 99900581756601842
- Language
- English
- Resource Type
- Dissertation