Trading Costs and Returns for US Equities: The Evidence from Daily Data

This study examines various measures of trading costs estimated from highfrequency data, the extent to which these measures can be estimated from daily data, and finally the relation between the daily-based proxies and stock returns (where trading cost is viewed as a characteristic). The high-frequency estimates of trading cost achieve partial agreement. Posted spreads and effective costs are highly correlated. Price impact measures and other statistics from dynamic models, however, are only modestly correlated with each other. Among the set of proxies constructed from daily data, a Gibbs estimate of the effective cost stands out, achieving a correlation of 0.944 with the corresponding TAQ estimate. Both the Gibbs estimate of effective cost and the illiquidity ratio covary positively with risk-adjusted returns, but the relations exhibit marked seasonality and are not robust to the use of alternative measures of correlation. JEL classification codes: C15, G12, G20