George Gao

Does Inventory Productivity Predict Future Stock Returns? A Retailing Industry Perspective

We find that inventory productivity strongly predicts future stock returns among a sample of publicly listed U.S. retailers during the period from 1985 to 2010. A zero-cost portfolio investment strategy, which consists of buying from the two highest and selling from the two lowest quintiles formed on inventory turnover, earns more than 1% average monthly abnormal return benchmarked to the Fama-French-Carhart four-factor model. Our results are robust to different measures of inventory productivity, distinct from the well-known firm characteristics known to generate abnormal returns, and not driven by a particular sub-sample period. A longitudinal analysis of portfolio returns over longer holding periods shows that, while inventory productivity is predictive of stock returns, its information dissipates about 1-2 years after release.

Rare Disaster Concerns Everywhere

We propose an empirical framework of disaster concerns to explain cross-sectional return variation both within and across asset classes. Using a large set of out-of-the-money options on international equity indices, foreign currencies, and global government bonds, we measure the global …nancial market’s rare disaster concerns under only no-arbitrage conditions. Assets that have low return covariations with such concerns earn high excess returns in the future. The return predictability driven by rare disaster concerns is distinct from that driven by exposures to realized disaster shocks such as macroeconomic downturns and liquidity crunches, and is not

Pre-Earnings Announcement Drift

We present evidence of a predictable drift in stock prices before the earnings announcements of firms that announce their earnings later than other firms in their industry. We form portfolios based on the returns of later announcers that are implied by the abnormal returns of earlier announcers and the historical pair-wise covariance of the abnormal earnings announcement date returns of earlier and later announcers. A long-short trading strategy based on these implied returns generates monthly returns of more than 100 basis points. The drift is neither due to the well-known momentum effect nor a manifestation of post-earnings announcement drift; it is evident both between the earlier announcers’ earnings announcement dates and the later announcers’ earnings announcement dates and at the later announcers’ earnings announcement dates. The continued under-reaction after later announcers’ earnings announcements is shown to be an under-reaction to the later announcers’ own earnings announcements (i.e., post-earnings announcement drift) rather than a continued under-reaction to the earnings news of earlier announcers (i.e., pre-earnings announcement drift). We show that transaction costs explain the predictability of later announcers’ returns.

The Informativeness of Short Sellers: An Insider's Perspective

Among the vocal critics of short sellers are corporate insiders, who allege that short sellers beat down their stock prices. Many corporations even engage in stock repurchases to show confidence that the stock will perform well going forward despite the short sellers’ actions. In this paper, we analyze insiders’ trading actions in their personal portfolios. We document a strong inverse relation between short selling and subsequent insider trading, which is partially due to common private information and same target firm characteristics. Additionally, we find that insiders extract information from shorts. This information extraction effect is more pronounced for firms whose insiders have stronger incentives to extract shorts information (insider purchases, higher short-sale constraints, and better information environments). During the September 2008 shorting ban, the information extraction effect disappeared among the large banned firms, whose shorting activities were distorted. Our empirical evidence contradicts the oft-cited accusations corporate executives hold against short sellers.

Characteristic-Based Covariances and Cross-Sectional Expected Returns

I suggest a characteristic-based covariance model that directly links the predetermined fi rm 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 fi rm characteristics. I fi nd a strong and positive intertemporal risk-return relation on individual stocks. In comparison to the Fama-French three-factor model, the characteristic-based covariance model substantially reduces the pricing errors of characteristic-sorted portfolios on size, book-to-market, accruals, asset growth, investment-to-assets, return-on-assets, net stock issues, financial distress, and momentum. Portfolio tests similar to Daniel and Titman (1997) suggest that firm characteristics, mainly through the characteristic-based covariance structure of returns, appear to explain the cross section of average stock returns.