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Judgement Day: Algorithmic Trading Around the Swiss Franc Cap Removal

Author

Listed:
  • Francis Breedon
  • Louisa Chen
  • Angelo Ranaldo

    ()

  • Nicholas Vause

Abstract

A key issue raised by the rapid growth of computerised algorithmic trading is how it responds in extreme situations. Using data on foreign exchange orders and transactions that includes identification of algorithmic trading, we find that this type of trading contributed to the deterioration of market quality following the removal of the cap on the Swiss franc on 15 January 2015, which was an event that came as a complete surprise to market participants. In particular, we find that algorithmic traders withdrew liquidity and generated uninformative volatility in Swiss franc currency pairs, while human traders did the opposite. However, we find no evidence that algorithmic trading propagated these adverse effects on market quality to other currency pairs.

Suggested Citation

  • Francis Breedon & Louisa Chen & Angelo Ranaldo & Nicholas Vause, 2018. "Judgement Day: Algorithmic Trading Around the Swiss Franc Cap Removal," Working Papers on Finance 1808, University of St. Gallen, School of Finance.
    • Handle: RePEc:usg:sfwpfi:2018:08
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    References listed on IDEAS

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    1. Hasbrouck, Joel, 1991. " Measuring the Information Content of Stock Trades," Journal of Finance, American Finance Association, vol. 46(1), pages 179-207, March.
    2. Breckenfelder, Johannes, 2013. "Competition between high-frequency traders, and market quality," MPRA Paper 66715, University Library of Munich, Germany, revised Dec 2013.
    3. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    4. Andrei A. Kirilenko & Andrew W. Lo, 2013. "Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents," Journal of Economic Perspectives, American Economic Association, vol. 27(2), pages 51-72, Spring.
    5. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    6. Rosu , Ioanid, 2016. "Fast and Slow Informed Trading," HEC Research Papers Series 1123, HEC Paris.
    7. Giovanni Cespa & Xavier Vives, 2015. "The Beauty Contest and Short-Term Trading," Journal of Finance, American Finance Association, vol. 70(5), pages 2099-2154, October.
    8. Thierry Foucault & Johan Hombert & Ioanid Roşu, 2016. "News Trading and Speed," Journal of Finance, American Finance Association, vol. 71(1), pages 335-382, February.
    9. Mirkov, Nikola & Pozdeev, Igor & Soderlind, Paul, 2016. "Toward Removal of the Swiss Franc Cap: Market Expectations and Verbal Interventions," Working Papers on Finance 1614, University of St. Gallen, School of Finance.
    10. 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.
    11. Markus Baldauf & Joshua Mollner, 2015. "High-Frequency Trading and Market Performance," Discussion Papers 15-017, Stanford Institute for Economic Policy Research.
    12. Hasbrouck, Joel, 1991. "The Summary Informativeness of Stock Trades: An Econometric Analysis," Review of Financial Studies, Society for Financial Studies, vol. 4(3), pages 571-595.
    13. Jonathan Brogaard & Terrence Hendershott & Ryan Riordan, 2014. "High-Frequency Trading and Price Discovery," Review of Financial Studies, Society for Financial Studies, vol. 27(8), pages 2267-2306.
    14. Hasbrouck, Joel & Saar, Gideon, 2013. "Low-latency trading," Journal of Financial Markets, Elsevier, vol. 16(4), pages 646-679.
    15. Conrad, Jennifer & Wahal, Sunil & Xiang, Jin, 2015. "High-frequency quoting, trading, and the efficiency of prices," Journal of Financial Economics, Elsevier, vol. 116(2), pages 271-291.
    16. Albert J. Menkveld, 2016. "The Economics of High-Frequency Trading: Taking Stock," Annual Review of Financial Economics, Annual Reviews, vol. 8(1), pages 1-24, October.
    17. Fischer, Andreas M, 2004. "Price Clustering in the FX Market: A Disaggregate Analysis Using Central Bank Intervention," CEPR Discussion Papers 4529, C.E.P.R. Discussion Papers.
    18. 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.
    19. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    20. Michael Goldstein & Michael A. Goldstein & Pavitra Kumar & Frank C. Graves, 2014. "Computerized and High-Frequency Trading," The Financial Review, Eastern Finance Association, vol. 49(2), pages 177-202, May.
    21. Menkveld, Albert J., 2013. "High frequency trading and the new market makers," Journal of Financial Markets, Elsevier, vol. 16(4), pages 712-740.
    22. Alain P. Chaboud & Benjamin Chiquoine & Erik Hjalmarsson & Clara Vega, 2014. "Rise of the Machines: Algorithmic Trading in the Foreign Exchange Market," Journal of Finance, American Finance Association, vol. 69(5), pages 2045-2084, October.
    23. Jonathan Brogaard & Björn Hagströmer & Lars Nordén & Ryan Riordan, 2015. "Trading Fast and Slow: Colocation and Liquidity," Review of Financial Studies, Society for Financial Studies, vol. 28(12), pages 3407-3443.
    24. Gara Afonso & Anna Kovner & Antoinette Schoar, 2011. "Stressed, Not Frozen: The Federal Funds Market in the Financial Crisis," Journal of Finance, American Finance Association, vol. 66(4), pages 1109-1139, August.
    25. Roberto Rigobon, 2003. "Identification Through Heteroskedasticity," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 777-792, November.
    26. Kyle, Albert S, 1985. "Continuous Auctions and Insider Trading," Econometrica, Econometric Society, vol. 53(6), pages 1315-1335, November.
    27. Hoffmann, Peter, 2014. "A dynamic limit order market with fast and slow traders," Journal of Financial Economics, Elsevier, vol. 113(1), pages 156-169.
    28. Eric Budish & Peter Cramton & John Shim, 2015. "Editor's Choice The High-Frequency Trading Arms Race: Frequent Batch Auctions as a Market Design Response," The Quarterly Journal of Economics, Oxford University Press, vol. 130(4), pages 1547-1621.
    29. Michael R. King & Carol Osler & Dagfinn Rime, 2011. "Foreign exchange market structure, players and evolution," Working Paper 2011/10, Norges Bank.
    30. O'Hara, Maureen & Ye, Mao, 2011. "Is market fragmentation harming market quality?," Journal of Financial Economics, Elsevier, vol. 100(3), pages 459-474, June.
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    Cited by:

    1. Corsetti, Giancarlo & Lafarguette, Romain & Mehl, Arnaud, 2019. "Fast trading and the virtue of entropy: evidence from the foreign exchange market," Working Paper Series 2300, European Central Bank.
    2. Angelo Ranaldo & Fabricius Somogyi, 2018. "Heterogeneous Information Content of Global FX Trading," Working Papers on Finance 1820, University of St. Gallen, School of Finance, revised Apr 2019.

    More about this item

    Keywords

    Swiss franc; algorithmic trading; liquidity; volatility; price discovery; arbitrage opportunities;

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors

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