Short Sales, Long Sales, and the Lee-Ready Trade Classification Algorithm Revisited

Asquith, Oman, and Safaya (2010) conclude that short sales are often misclassified by the Lee-Ready algorithm. The algorithm identifies most short sales as buyer-initiated, whereas the authors posit that short sales should be overwhelmingly seller-initiated. Using order data to identify true trade initiator, we document that short sales are, in fact, predominantly buyer-initiated and that the Lee-Ready algorithm correctly classifies most of them. Misclassification rates for short and long sales are near zero at the daily level. At the trade level, misclassification rates are 31% using contemporaneous quotes and trades and decline to 21% when quotes are lagged one second

Shorting at Close Range: A Tale of Two Types

Abstract We examine stock returns, order flow, and market conditions in the minutes before, during, and after recent short sales on the NYSE and Nasdaq. We find two very distinct types of short sales: those that provide liquidity, and those that demand it. Shorts that supply liquidity do so when spreads are unusually wide. These short sellers are also strongly contrarian, stepping in to initiate or increase a short position after fairly sharp share price rises over the past hour or so, and they tend to face greater adverse selection than other liquidity suppliers. In contrast, shorts that demand liquidity tend to be shortterm momentum traders. However, there is no evidence that liquidity-demanding short sellers are any different from other liquidity demanders. Overall, liquidity-providing short sales are important contributors to stock market quality, and regulators and policymakers should keep these salutary effects in mind. JEL classification: G14, G1

Non-Standard Errors

In statistics, samples are drawn from a population in a data-generating process (DGP). Standard errors measure the uncertainty in estimates of population parameters. In science, evidence is generated to test hypotheses in an evidence-generating process (EGP). We claim that EGP variation across researchers adds uncertainty: Non-standard errors (NSEs). We study NSEs by letting 164 teams test the same hypotheses on the same data. NSEs turn out to be sizable, but smaller for better reproducible or higher rated research. Adding peer-review stages reduces NSEs. We further find that this type of uncertainty is underestimated by participants

Every Cloud Has a Silver Lining: Fast Trading, Microwave Connectivity, and Trading Costs

Modern markets are characterized by speed differentials, with some traders being fractions of a second faster than others. Theoretical models suggest that such differentials may have both positive and negative effects on liquidity and gains from trade. We examine these effects by studying a series of exogenous weather episodes that temporarily remove the speed advantages of the fastest traders by disrupting their microwave networks. The disruptions are associated with lower adverse selection and lower trading costs. In additional analysis, we show that the long-term removal of speed differentials results in similar effects and also increases gains from trade

Factor models for binary financial data

Researchers are often interested in modeling binary decisions made by firms (e.g., the yes or no decisions to split the shares, initiate a dividend, or acquire another firm) as functions of economy-wide variables (common factors). Although factor models for continuous dependent variables are used widely, the toolkit of a financial researcher does not contain a generally accepted methodology that allows estimating factor models for binary dependent variables. In this paper, we study such a methodology. Using simulations, we identify data characteristics that allow for reliable estimates of factor parameters and conclude that the methodology is appropriate for the panel datasets of the type often used in finance. As an illustration, we use the methodology to address a currently debated issue of common factors in firms\u27 decisions to split their shares