We examine the economic determinants of short-sale supply, and its consequences for future stock returns. Lendable supply increases with expected borrowing costs and decreases with financial statement constructs that indicate overvaluation. Although rising loan fees help ease supply constraints, we find shares are still least available when they are most attractive to short sellers. Using a number of firm characteristics, we derive useful instruments for real-time loan supply and demand conditions in the lending market. Further, we show that (1) when lendable supply is binding (non-binding), short-sale supply (demand) is the main predictor of future stock returns, (2) abnormal returns to the short-side of nine well-known market anomalies are attributable solely to “special” stocks, and (3) loan fees significantly reduce the profitability of the short side and several of these anomalies cease to be profitable. Overall our evidence highlights the central role played by the supply of lendable shares in equity price formation and returns prediction.

This paper evaluates alternative methods for classifying individual trades as market buy or market sell orders using intraday trade and quote data. We document two potential problems with quote‐based methods of trade classification: quotes may be recorded ahead of trades that triggered them, and trades inside the spread are not readily classifiable. These problems are analyzed in the context of the interaction between exchange floor agents. We then propose and test relatively simple procedures for improving trade classifications.

This paper evaluates alternative methods for classifying individual trades as market buy or market sell orders using intraday trade and quote data. We document two potential problems with quote-based methods of trade classification: quotes may be recorded ahead of trades that triggered them, and trades inside the spread are not readily classifiable. These problems are analyzed in the context of the interaction between exchange floor agents. We then propose and test relatively simple procedures for improving trade classifications.

We study firms that go public through reverse mergers (RMs) versus initial public offerings (IPOs) in China. Using a manually assembled data set, we show that pre-listing RM firms are larger, more profitable, and less politically connected than pre-listing IPO firms. Chinese RM firms also have superior post-listing performance, in terms of both operations and stock returns, compared to IPOs matched on industry and size. Unlike IPOs, RM firms do not underperform the market in the long run. These results are in sharp contrast to the evidence on RMs from developed countries. We trace these differences to China's stringent and potentially biased IPO policies, which appear to preclude even high-quality firms from accessing public markets.

This study compares four broadly available industry classification schemes in a variety of applications common to capital market research. Standard Industrial Classification (SIC) codes have been available since 1939 but are being replaced by North American Industry Classification System (NAICS) codes. The Global Industry Classifications Standard (GICS) SM system, jointly developed by Standard & Poor's and Morgan Stanley Capital International (MSCI), is popular among financial practitioners, whereas the Fama and French [1997] algorithm is used primarily by academics. Our results show that GICS classifications are significantly better at explaining stock return comovements, as well as cross‐sectional variations in valuation multiples, forecasted and realized growth rates, research and development expenditures, and various key financial ratios. The GICS advantage is consistent from year to year and is most pronounced among large firms. The other three methods differ little from each other in most applications.

This study investigates the investment decision and dividend policy jointly from a non-steady state to a steady state. We extend Higgins, 1977, Higgins, 1981, Higgins, 2008 sustainable growth rate model and develop a dynamic model which jointly optimizes the growth rate and payout ratio. We optimize the firm value to obtain the optimal growth rate in terms of a logistic equation and find that the steady state growth rate can be used as the benchmark for the mean-reverting process of the optimal growth rate. We also investigate the specification error of the mean and variance of dividend per share when introducing the stochastic growth rate. Empirical results support the mean-reverting process of the growth rate and the importance of covariance between the profitability and the growth rate in determining dividend payouts. The intertemporal behavior of the covariance may shed some light on the fact of disappearing dividends over decades.

We use trade-level data to examine the role of actively managed funds (AMFs) in earnings news dissemination. We find that AMFs are drawn to, and participate disproportionately more in, earnings announcements (EAs) that include bundled managerial guidance. When the two pieces of news are directionally inconsistent, AMFs trade in the direction of future guidance rather than current earnings. AMFs exhibit an ability to discern, and adapt their trading to, the bias in bundled guidance. While AMF trades at EAs are generally more profitable than their non-EA trades, this result reverses when guidance bias is extreme. Overall, we find that increased AMF trading during EAs leads to faster price adjustment. Collectively, these findings suggest that AMFs are sophisticated processors of bundled earnings news, and their trading generally improves market price discovery.

We introduce a parsimonious framework for choosing among alternative expected-return proxies (ERPs) when estimating treatment effects. By comparing ERPs’ measurement error variances in the cross-section and in the time series, we provide new evidence on the relative performance of firm-level ERPs nominated by recent studies. Generally, “implied-costs-of-capital” metrics perform best in the time series, whereas “characteristic-based” proxies perform best in the cross-section. Factor-based ERPs, even the latest renditions, perform poorly. We revisit four prior studies that use ex ante ERPs and illustrate how this framework can potentially alter either the sign or the magnitude of prior inferences.

This study investigates a particularly brazen form of corporate abuse, in which controlling shareholders use intercorporate loans to siphon billions of RMB from hundreds of Chinese listed companies during the 1996–2006 period. We document the nature and extent of these transactions, evaluate their economic consequences, examine factors that affect their cross-sectional severity, and report on the mitigating roles of auditors, institutional investors, and regulators. Collectively, our findings shed light on the severity of the minority shareholder expropriation problem in China, as well as the relative efficacy of various legal and extra-legal governance mechanisms in that country.

This study examines how an increase in tick size affects algorithmic trading (AT), fundamental information acquisition (FIA), and the price discovery process around earnings announcements (EAs). Leveraging the SEC's randomized Tick Size Pilot experiment, we show that a tick size increase results in a decline in AT and a sharp drop in absolute cumulative abnormal returns and volume around EAs. More importantly, we find increased FIA in the preannouncement period. Specifically, we show: (1) treatment firms' pre-announcement returns better anticipate next quarter's standardized unexpected earnings; (2) these firms experience an increase in EDGAR web traffic prior to EAs; and (3) they exhibit a drop in price synchronicity with index returns. Taken together, our evidence suggests that while an increase in tick size reduces AT and abnormal market reaction after EAs, it also increases FIA activities prior to EAs.