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Price and size discovery in financial markets: evidence from the U.S. Treasury securities market

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Abstract

We study the workup protocol, an important size discovery mechanism in the U.S. Treasury securities market. We find that shocks in workup order flow explain 6-8 percent of the variation of returns on benchmark notes and, across maturities, contribute 10 percent to the variation of the yield curve level factor. Information related to proprietary client order flow is more likely to show up in workup trades, whereas information derived from public announcements is more likely to come through pre-workup (or “lit”) trades. Our findings highlight how the nature of information affects the trade-off between speed and execution price as informed traders choose between the lit and workup channels.

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  • Michael J. Fleming & Giang Nguyen, 2013. "Price and size discovery in financial markets: evidence from the U.S. Treasury securities market," Staff Reports 624, Federal Reserve Bank of New York.
    • Handle: RePEc:fip:fednsr:624
    Note: Revised August 2018. Previous title: “Order Flow Segmentation and the Role of Dark Trading in the Price Discovery of U.S. Treasury Securities.” For a published version of this report, see Michael J. Fleming and Giang Nguyen, “Price and Size Discovery in Financial Markets: Evidence from the U.S. Treasury Securities Market,” Review of Asset Pricing Studies, Vol. 9, No. 2 (December 2019): 256–295.
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    Cited by:

    1. Adrian, Tobias & Capponi, Agostino & Fleming, Michael & Vogt, Erik & Zhang, Hongzhong, 2020. "Intraday market making with overnight inventory costs," Journal of Financial Markets, Elsevier, vol. 50(C).
    2. Indriawan, Ivan & Jiao, Feng & Tse, Yiuman, 2021. "The FOMC announcement returns on long-term US and German bond futures," Journal of Banking & Finance, Elsevier, vol. 123(C).
    3. Pierre Collin‐Dufresne & Benjamin Junge & Anders B. Trolle, 2020. "Market Structure and Transaction Costs of Index CDSs," Journal of Finance, American Finance Association, vol. 75(5), pages 2719-2763, October.
    4. Han, Seung-Oh & Huh, Sahn-Wook & Park, Jeayoung, 2023. "Detecting jumps amidst prevalent zero returns: Evidence from the U.S. Treasury securities," Journal of Empirical Finance, Elsevier, vol. 70(C), pages 276-307.
    5. He, William Peng & Lepone, Andrew, 2014. "Determinants of liquidity and execution probability in exchange operated dark pool: Evidence from the Australian Securities Exchange," Pacific-Basin Finance Journal, Elsevier, vol. 30(C), pages 1-16.
    6. Corey Garriott & Adrian Walton, 2016. "Retail Order Flow Segmentation," Staff Working Papers 16-20, Bank of Canada.
    7. Papavassiliou, Vassilios G. & Kinateder, Harald, 2021. "Information shares and market quality before and during the European sovereign debt crisis," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 72(C).

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    More about this item

    Keywords

    liquidity; fixed-income markets; price impact; dark pool; information share;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation

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