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Trade-Time Measures of Liquidity

Author

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  • Yashar H Barardehi
  • Dan Bernhardt
  • Ryan J Davies

Abstract

Dramatic microstructure changes in equity markets have made standard liquidity measures less accurate proxies for trading costs. We develop trade-time liquidity measures that reflect per-dollar price impacts of fixed-dollar volumes. Our measures better capture institutional trading costs and better explain the cross-section of returns than do standard measures, especially in recent years. Despite improvements in measures of market quality, expected trading costs have explanatory power for the cross-section of expected returns: we obtain monthly liquidity premium estimates of 5.3 bp for expected returns and 2.4 bp for risk-adjusted returns. Estimated premiums rise after the financial crisis and remain high thereafter. Received April 15, 2016; editorial decision December 24, 2017 by Editor Andrew Karolyi.

Suggested Citation

  • Yashar H Barardehi & Dan Bernhardt & Ryan J Davies, 2019. "Trade-Time Measures of Liquidity," The Review of Financial Studies, Society for Financial Studies, vol. 32(1), pages 126-179.
    • Handle: RePEc:oup:rfinst:v:32:y:2019:i:1:p:126-179.
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    File URL: http://hdl.handle.net/10.1093/rfs/hhy012
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    Citations

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    Cited by:

    1. Barardehi, Yashar H. & Bernhardt, Dan & Ruchti, Thomas G., 2019. "A test of speculative arbitrage: is the cross-section of volatility invariant?," The Warwick Economics Research Paper Series (TWERPS) 1204, University of Warwick, Department of Economics.
    2. Jeffrey R. Black & Pankaj K. Jain & Wei Sun, 2023. "Trade-time clustering," Review of Quantitative Finance and Accounting, Springer, vol. 60(3), pages 1209-1242, April.
    3. Eduardo Bered Fernandes Vieira & Tiago Pascoal Filomena, 2020. "Liquidity Constraints for Portfolio Selection Based on Financial Volume," Computational Economics, Springer;Society for Computational Economics, vol. 56(4), pages 1055-1077, December.
    4. Chaeshick Chung & Sukjin Park, 2021. "Deep Learning Market Microstructure: Dual-Stage Attention-Based Recurrent Neural Networks," Working Papers 2108, Nam Duck-Woo Economic Research Institute, Sogang University (Former Research Institute for Market Economy).
    5. David Easley & Marcos López de Prado & Maureen O’Hara & Zhibai Zhang & Wei Jiang, 2021. "Microstructure in the Machine Age [The risk of machine learning]," The Review of Financial Studies, Society for Financial Studies, vol. 34(7), pages 3316-3363.
    6. Suchismita Mishra & Le Zhao, 2021. "Order Routing Decisions for a Fragmented Market: A Review," JRFM, MDPI, vol. 14(11), pages 1-32, November.
    7. Narayan Bulusu & Sermin Gungor, 2021. "The life cycle of trading activity and liquidity of Government of Canada bonds: Evidence from cash, repo and securities lending markets," Canadian Journal of Economics/Revue canadienne d'économique, John Wiley & Sons, vol. 54(2), pages 557-581, May.

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