Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose a new method to estimate the bid-ask spread when quote data are not available. Compared to other low-frequency estimates, this method utilizes a wider information set, namely, readily available close, high, and low prices. In the absence of end-of-day quote data, this method generally provides the highest cross-sectional and average time-series correlations with the TAQ effective spread benchmark. Moreover, it delivers the most accurate estimates for less liquid stocks. Our estimator has many potential applications, including an accurate measurement of transaction cost, systematic liquidity risk, and commonality in liquidity for U.S. stocks dating back almost one century. Received July 17, 2016; editorial decision May 23, 2017 by Editor Andrew Karolyi.