Trading Costs and Returns for U.S. Equities: Estimating Effective Costs from Daily Data

Abstract

The effective cost of trading is usually estimated from transaction-level data. This study proposes a Gibbs estimate that is based on daily closing prices. In a validation sample, the daily Gibbs estimate achieves a correlation of 0.965 with the transactionlevel estimate. When the Gibbs estimates are incorporated into asset pricing specifications over a long historical sample (1926 to 2006), the results suggest that effective cost (as a characteristic) is positively related to stock returns. The relation is strongest in January, but it appears to be distinct from size effects.INVESTIGATIONS INTO THE ROLE of liquidity and transaction costs in asset pricing must generally confront the fact that while many asset pricing tests make use of U.S. equity returns from 1926 onward, the high-frequency data used to estimate trading costs are usually not available prior to 1983. Accordingly, most studies either limit the sample to the post-1983 period of common coverage or use the longer historical sample with liquidity proxies estimated from daily data. This paper falls into the latter group. Specifically, I propose a new approach to estimating the effective cost of trading and the common variation in this cost. These estimates are then used in conventional asset pricing specifications with a view to ascertaining the role of trading costs as a characteristic in explaining expected returns. 1 * Hasbrouck is with the Stern School of Business, New York University. For comments and suggestions I am grateful to the editor, the referee, Yakov Amihud,Lubos Pástor, Bill Schwert, Jay Shanken, Kumar Venkataraman, Sunil Wahal, and seminar participants at the University of Rochester, the NBER Microstructure Research Group, the Federal Reserve Bank of New York, Yale University, the University of Maryland, the University of Utah, Emory University, and Southern Methodist University. All errors are my own responsibility. Earlier versions of this paper and an SAS data set containing the long-run Gibbs sampler estimates are available on my web site at www.stern.nyu.edu/∼jhasbrou.1 Recent asset pricing analyses based on samples in which high-frequency data are available include Brennan and Subrahmanyam (1996), Easley, Hvidkjaer, and O'Hara (2002), Sadka (2004), and Korajczyk and Sadka (2008). Analyses that use proxies based on daily data include Amihud (2002), Pástor and Stambaugh (2003), Acharya and Pedersen (2005), and Spiegel and Wang (2005). Closing daily or annual bid-ask quotes are sometimes available over samples longer than those of the high-frequency data. Studies that use closing spreads include Stoll and Whalley (1983), Amihud and Mendelson (1986), Eleswarapu and Reinganum (1993), Reinganum (1990), andChalmers andKadlec (1998). provide a broad survey of the links between asset pricing and microstructure. 1446The Journal of Finance R For a buy order executed in a single trade, the effective cost is the execution price less the midpoint of the prevailing bid and ask quotes (and vice versa for a sale). In the simplest setting...

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