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Trading on Long-term Information

Author

Listed:
  • Corey Garriot
  • Ryan Riordan

Abstract

Predatory trading discourages informed investors from gathering information and trading on it. However, using 11 years of equity trading data, we do not find evidence that informed investors are being discouraged. They have roughly constant volumes and profits through the sample. They are sophisticated, trading patiently over weeks and timing their trading to achieve negative price impacts, leaving price efficiency unchanged. We identify shorter-term traders and, in contrast to theory, find that they supply liquidity by trading in the opposite direction of the informed. Inefficient prices may be the result of informed investors' sophisticated trading and not of predatory short-term trading.

Suggested Citation

  • Corey Garriot & Ryan Riordan, 2020. "Trading on Long-term Information," Staff Working Papers 20-20, Bank of Canada.
    • Handle: RePEc:bca:bocawp:20-20
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    References listed on IDEAS

    as
    1. Liyan Yang & Haoxiang Zhu, 2020. "Back-Running: Seeking and Hiding Fundamental Information in Order Flows," Review of Finance, European Finance Association, vol. 33(4), pages 1484-1533.
    2. Chakravarty, Sugato, 2001. "Stealth-trading: Which traders' trades move stock prices?," Journal of Financial Economics, Elsevier, vol. 61(2), pages 289-307, August.
    3. Cornell, Bradford & Sirri, Erik R, 1992. "The Reaction of Investors and Stock Prices to Insider Trading," Journal of Finance, American Finance Association, vol. 47(3), pages 1031-1059, July.
    4. Meulbroek, Lisa K, 1992. "An Empirical Analysis of Illegal Insider Trading," Journal of Finance, American Finance Association, vol. 47(5), pages 1661-1699, December.
    5. O'Hara, Maureen & Ye, Mao, 2011. "Is market fragmentation harming market quality?," Journal of Financial Economics, Elsevier, vol. 100(3), pages 459-474, June.
    6. JULES H. van BINSBERGEN & CHRISTIAN C. OPP, 2019. "Real Anomalies," Journal of Finance, American Finance Association, vol. 74(4), pages 1659-1706, August.
    7. Pierre Collin-Dufresne & Vyacheslav Fos, 2015. "Do Prices Reveal the Presence of Informed Trading?," Journal of Finance, American Finance Association, vol. 70(4), pages 1555-1582, August.
    8. Pierre Collin‐Dufresne & Vyacheslav Fos, 2016. "Insider Trading, Stochastic Liquidity, and Equilibrium Prices," Econometrica, Econometric Society, vol. 84, pages 1441-1475, July.
    9. Ron Kaniel & Hong Liu, 2006. "So What Orders Do Informed Traders Use?," The Journal of Business, University of Chicago Press, vol. 79(4), pages 1867-1914, July.
    10. Dugast, Jérôme & Foucault, Thierry, 2018. "Data abundance and asset price informativeness," Journal of Financial Economics, Elsevier, vol. 130(2), pages 367-391.
    11. Peter Koudijs, 2016. "The Boats That Did Not Sail: Asset Price Volatility in a Natural Experiment," Journal of Finance, American Finance Association, vol. 71(3), pages 1185-1226, June.
    12. Chen, Marie & Garriott, Corey, 2020. "High-frequency trading and institutional trading costs," Journal of Empirical Finance, Elsevier, vol. 56(C), pages 74-93.
    13. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    14. Peter Koudijs, 2015. "Those Who Know Most: Insider Trading in Eighteenth-Century Amsterdam," Journal of Political Economy, University of Chicago Press, vol. 123(6), pages 1356-1409.
    15. Robert A Korajczyk & Dermot Murphy, 2019. "High-Frequency Market Making to Large Institutional Trades," Review of Financial Studies, Society for Financial Studies, vol. 32(3), pages 1034-1067.
    16. Brian J. Bushee & Theodore H. Goodman, 2007. "Which Institutional Investors Trade Based on Private Information About Earnings and Returns?," Journal of Accounting Research, Wiley Blackwell, vol. 45(2), pages 289-321, May.
    17. Kothari, S. P., 2001. "Capital markets research in accounting," Journal of Accounting and Economics, Elsevier, vol. 31(1-3), pages 105-231, September.
    18. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2019. "Price Discovery without Trading: Evidence from Limit Orders," Journal of Finance, American Finance Association, vol. 74(4), pages 1621-1658, August.
    19. Lee, Charles M. C. & Watts, Edward M., 2018. "Tick Size Tolls: Can a Trading Slowdown Improve Price Discovery?," Research Papers 3732, Stanford University, Graduate School of Business.
    20. Vincent Van Kervel & Albert J. Menkveld, 2019. "High‐Frequency Trading around Large Institutional Orders," Journal of Finance, American Finance Association, vol. 74(3), pages 1091-1137, June.
    21. Brian M. Weller, 2018. "Does Algorithmic Trading Reduce Information Acquisition?," Review of Financial Studies, Society for Financial Studies, vol. 31(6), pages 2184-2226.
    22. Marcin Kacperczyk & Emiliano S Pagnotta, 2019. "Chasing Private Information," Review of Financial Studies, Society for Financial Studies, vol. 32(12), pages 4997-5047.
    23. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    24. Markus Baldauf & Joshua Mollner, 2020. "High‐Frequency Trading and Market Performance," Journal of Finance, American Finance Association, vol. 75(3), pages 1495-1526, June.
    25. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," The Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    26. Olivier Dessaint & Thierry Foucault & Laurent Frésard & Adrien Matray, 2019. "Noisy Stock Prices and Corporate Investment," Review of Financial Studies, Society for Financial Studies, vol. 32(7), pages 2625-2672.
    27. Barclay, Michael J. & Warner, Jerold B., 1993. "Stealth trading and volatility : Which trades move prices?," Journal of Financial Economics, Elsevier, vol. 34(3), pages 281-305, December.
    28. Chakravarty, Sugato & McConnell, John J., 1999. "Does Insider Trading Really Move Stock Prices?," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(2), pages 191-209, June.
    29. Terrence Hendershott & Charles M. Jones & Albert J. Menkveld, 2011. "Does Algorithmic Trading Improve Liquidity?," Journal of Finance, American Finance Association, vol. 66(1), pages 1-33, February.
    30. Alex Boulatov & Thomas J. George, 2013. "Hidden and Displayed Liquidity in Securities Markets with Informed Liquidity Providers," Review of Financial Studies, Society for Financial Studies, vol. 26(8), pages 2096-2137.
    31. Liyan Yang & Haoxiang Zhu, 2020. "Back-Running: Seeking and Hiding Fundamental Information in Order Flows [Arbitraging arbitrageurs]," Review of Financial Studies, Society for Financial Studies, vol. 33(4), pages 1484-1533.
    32. Pierre Collin-Dufresne & Vyacheslav Fos, 2013. "Do Prices Reveal the Presence of Informed Trading?," Swiss Finance Institute Research Paper Series 13-69, Swiss Finance Institute, revised Sep 2015.
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    Cited by:

    1. Roberto Riccò & Barbara Rindi & Duane J. Seppi, 2020. "Information, Liquidity, and Dynamic Limit Order Markets," Working Papers 660, IGIER (Innocenzo Gasparini Institute for Economic Research), Bocconi University.
    2. Josef Schroth, 2020. "Outside Investor Access to Top Management: Market Monitoring versus Stock Price Manipulation," Staff Working Papers 20-43, Bank of Canada.

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    Keywords

    Financial institutions; Financial markets; Market structure and pricing;
    All these keywords.

    JEL classification:

    • G20 - Financial Economics - - Financial Institutions and Services - - - General
    • L1 - Industrial Organization - - Market Structure, Firm Strategy, and Market Performance

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