My research interest: empirical asset pricing and financial econometrics.
Please send me your comments, suggestions, and criticisms by e-mail: pg297 [at] cornell.edu
Do Hedge Funds Exploit Rare Disaster Concerns? (with Paul Gao and Zhaogang Song) [Data]
We develop a rare disaster index (RIX) to measure market expectations of future rare disasters (rare disaster concerns). Smart hedge funds systematically exploit RIX and earn positive returns.
The Sound of Silence: What Do We Know When Insiders Do Not Trade (with Qingzhong Ma)
Insider silence, a frequent phenomenon in corporate insider trading, has strong information content for future stock performance.
Institutional Ownership and Return Predictability Across Economically Unrelated Stocks (with Pam Moulton and David Ng) [Internet Appendix]
Institutional portfolio reallocations can induce strong weekly return predictability among otherwise unrelated stocks (e.g., a copper wire firm and a funeral service firm).
Does Inventory Productivity Predict Future Stock Returns? A Retailing Industry Perspective (with Yasin Alan and Vishal Gaur)
From an operations perspective, inventory management is critical for a retailer. The stock market well rewards operationally efficient retailers with high inventory productivity.
Pre-Earnings Announcement Drift (under revision, with Peter Easton and Paul Gao)
We document a predictable drift in stock prices before the earnings announcements of firms that announce their earnings later than other firms in their industry. We dynamically form portfolios for late announcers based on the cross section information of early announcers (in terms of historical pairwise covariances of earnings announcement day returns).
Rare Disaster Concerns Everywhere (with Zhaogang Song)
Securities Transaction Tax, Trading Behavior, and Market Quality (with Paul Gao and Yonxiang Wang)
ProQuest/UMI Dissertations and Theses
Chapter 1: Firm Characteristics, Covariance, and Portfolio Optimization
The (abnormal) return covariance pattern of S&P 500 stocks is explicitly linked to firm characteristics. This type of return co-movement (I call a spatial covariance pattern) cannot be fully characterized by a few pervasive factors. In comparison to factor-based covariance models, an investor exploring the characteristic-based spatial covariance structure has substantial diversification benefits under the case of global minimum variance portfolio, and has substantial utility gains under the case of optimal tangency portfolio.
Chapter 2: Characteristic-Based Covariances and Cross-Sectional Expected Returns
I propose a characteristic-based covariance model that directly links the predetermined firm characteristics to time-varying covariance risk. Using a large cross section of individual stock-level data, I parsimoniously estimate both conditional expected returns and conditional covariances as functions of firm characteristics. Main results: (i) I find a strong and positive intertemporal risk-return relationship on individual stocks; (ii) Two conditional covariance variables largely help explain the anomalous returns associated with size, BM, asset growth, accruals, investment-to-assets, return-on-assets, net stock issues, financial distress, and momentum; (iii) Portfolio test as in Daniel and Titman (1997) suggests that firm characteristics, mainly through the characteristic-based covariance structure of returns, appear to explain the cross-sectional average returns; and (iv) the characteristic-based covariance structure of stock returns is systematically related to fundamental risk in the macro economy.