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Is market impact a measure of the information value of trades? Market response to liquidity vs. informed metaorders

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  • C. Gomes
  • H. Waelbroeck

Abstract

We examine a data-set of institutional trades where approximately one-fourth of the trades were labelled as having been created for cash flow purposes. We aggregate near-overlapping trades into metaorders and consider information, market impact and metaorder size. We find that during the execution, the functional form and scale of market impact are similar for cash flows and other metaorders. Differences arise in the price reversion following the end of a metaorder. For cash flows, presumed to have no true information content, the impact reverts almost completely on average in two to five days. For other metaorders, we find that reversion erases about one-third of the peak impact: for each size, price reverts to the average execution price, leaving no immediate profits after accounting for trading costs. Observed mark-to-market profits on metaorders that aggregate multiple portfolio manager orders, new metaorders and Nasdaq-listed stocks suggest that these metaorders are more informed than the average. Vice-versa, we find mark-to-market losses are more likely to occur on cash flows, metaorders in large-cap stocks, metaorders that follow momentum and additions to a prior position seeking to take advantage of an improved price. The complete price reversion for cash flows suggests that the mechanical permanent impact that is considered in no-quasi-arbitrage arguments would be much smaller than the information in typical institutional metaorders.

Suggested Citation

  • C. Gomes & H. Waelbroeck, 2015. "Is market impact a measure of the information value of trades? Market response to liquidity vs. informed metaorders," Quantitative Finance, Taylor & Francis Journals, vol. 15(5), pages 773-793, May.
    • Handle: RePEc:taf:quantf:v:15:y:2015:i:5:p:773-793
    DOI: 10.1080/14697688.2014.963140
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    References listed on IDEAS

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    1. Sanford Grossman, 1989. "The Informational Role of Prices," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262572141, December.
    2. Hasbrouck, Joel, 2007. "Empirical Market Microstructure: The Institutions, Economics, and Econometrics of Securities Trading," OUP Catalogue, Oxford University Press, number 9780195301649, Decembrie.
    3. Anna Obizhaeva, 2009. "Portfolio Transitions and Stock Price Dynamics," Working Papers w0224, Center for Economic and Financial Research (CEFIR).
    4. Anna Obizhaeva, 2009. "Portfolio Transitions and Stock Price Dynamics," Working Papers w0224, New Economic School (NES).
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    Citations

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    Cited by:

    1. van Kervel, Vincent & Kwan, Amy & Westerholm, P. Joakim, 2023. "Order splitting and interacting with a counterparty," Journal of Financial Markets, Elsevier, vol. 66(C).
    2. Fabrizio Lillo, 2021. "Order flow and price formation," Papers 2105.00521, arXiv.org.
    3. Emilio Said, 2022. "Market Impact: Empirical Evidence, Theory and Practice," Working Papers hal-03668669, HAL.
    4. Emilio Said & Ahmed Bel Hadj Ayed & Damien Thillou & Jean-Jacques Rabeyrin & Frédéric Abergel, 2020. "Market Impact: A Systematic Study of the High Frequency Options Market," Post-Print hal-02014248, HAL.
    5. Emilio Said & Ahmed Bel Hadj Ayed & Alexandre Husson & Frédéric Abergel, 2018. "Market Impact: A Systematic Study of Limit Orders," Post-Print hal-01561128, HAL.
    6. Shanshan Wang, 2017. "Trading strategies for stock pairs regarding to the cross-impact cost," Papers 1701.03098, arXiv.org, revised Jul 2017.
    7. Emilio Said & Ahmed Bel Hadj Ayed & Alexandre Husson & Frédéric Abergel, 2018. "Market Impact: A systematic study of limit orders," Working Papers hal-01561128, HAL.
    8. Mathias Pohl & Alexander Ristig & Walter Schachermayer & Ludovic Tangpi, 2017. "The amazing power of dimensional analysis: Quantifying market impact," Papers 1702.05434, arXiv.org, revised Sep 2017.
    9. Yamamoto, Ryuichi, 2019. "Dynamic Predictor Selection And Order Splitting In A Limit Order Market," Macroeconomic Dynamics, Cambridge University Press, vol. 23(5), pages 1757-1792, July.
    10. Paul Jusselin & Mathieu Rosenbaum, 2020. "No‐arbitrage implies power‐law market impact and rough volatility," Mathematical Finance, Wiley Blackwell, vol. 30(4), pages 1309-1336, October.
    11. Fr'ed'eric Bucci & Michael Benzaquen & Fabrizio Lillo & Jean-Philippe Bouchaud, 2019. "Slow decay of impact in equity markets: insights from the ANcerno database," Papers 1901.05332, arXiv.org, revised Jan 2019.
    12. Emilio Said, 2022. "Market Impact: Empirical Evidence, Theory and Practice," Papers 2205.07385, arXiv.org.
    13. Paul Jusselin & Mathieu Rosenbaum, 2018. "No-arbitrage implies power-law market impact and rough volatility," Papers 1805.07134, arXiv.org.
    14. Emilio Said & Ahmed Bel Hadj Ayed & Alexandre Husson & Fr'ed'eric Abergel, 2018. "Market Impact: A Systematic Study of Limit Orders," Papers 1802.08502, arXiv.org, revised May 2022.
    15. Frédéric Bucci & Michael Benzaquen & Fabrizio Lillo & Jean-Philippe Bouchaud, 2019. "Slow Decay of Impact in Equity Markets: Insights from the ANcerno Database," Post-Print hal-02323357, HAL.
    16. Emilio Said & Ahmed Bel Hadj Ayed & Damien Thillou & Jean-Jacques Rabeyrin & Frédéric Abergel, 2019. "Market Impact: A Systematic Study of the High Frequency Options Market," Working Papers hal-02014248, HAL.
    17. Emilio Said & Ahmed Bel Hadj Ayed & Damien Thillou & Jean-Jacques Rabeyrin & Fr'ed'eric Abergel, 2019. "Market Impact: A Systematic Study of the High Frequency Options Market," Papers 1902.05418, arXiv.org, revised May 2022.

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