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Arbitrage bots in experimental asset markets

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  • Angerer, Martin
  • Neugebauer, Tibor
  • Shachat, Jason

Abstract

While algorithmic trading robots are a proliferating presence in asset markets, there is no consensus whether their presence improves market quality or benefits individual investors. We examine the impact of robots seeking arbitrage in experimental laboratory markets. We find that the presence of algorithmic arbitrageurs generally enhances market quality. However, the wealth of human traders suffers from the presence of algorithmic traders. These social costs can be mitigated as we find high latency algorithms harm investors less than low latency algorithms; while the improvements in market quality are indistinguishable between algorithm latency levels and whether they provide liquidity or not.

Suggested Citation

  • Angerer, Martin & Neugebauer, Tibor & Shachat, Jason, 2019. "Arbitrage bots in experimental asset markets," MPRA Paper 96224, University Library of Munich, Germany.
  • Handle: RePEc:pra:mprapa:96224
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    1. Gary Charness & Tibor Neugebauer, 2019. "A Test of the Modigliani‐Miller Invariance Theorem and Arbitrage in Experimental Asset Markets," Journal of Finance, American Finance Association, vol. 74(1), pages 493-529, February.
    2. Gary Charness & Uri Gneezy, 2010. "Portfolio Choice And Risk Attitudes: An Experiment," Economic Inquiry, Western Economic Association International, vol. 48(1), pages 133-146, January.
    3. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    4. Torben G. Andersen & Oleg Bondarenko & Albert S. Kyle & Anna Obizhaeva, 2016. "Intraday Trading Invariance in the E-mini S&P 500 Futures Market," Working Papers w0229, New Economic School (NES).
    5. Thierry Foucault & Johan Hombert & Ioanid Roşu, 2016. "News Trading and Speed," Journal of Finance, American Finance Association, vol. 71(1), pages 335-382, February.
    6. Shleifer, Andrei, 2000. "Inefficient Markets: An Introduction to Behavioral Finance," OUP Catalogue, Oxford University Press, number 9780198292272.
    7. Glantz, Morton & Kissell, Robert, 2013. "Multi-Asset Risk Modeling," Elsevier Monographs, Elsevier, edition 1, number 9780124016903.
    8. Andrei Kirilenko & Albert S. Kyle & Mehrdad Samadi & Tugkan Tuzun, 2017. "The Flash Crash: High-Frequency Trading in an Electronic Market," Journal of Finance, American Finance Association, vol. 72(3), pages 967-998, June.
    9. 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.
    10. Albert J. Menkveld & Bart Zhou Yueshen, 2019. "The Flash Crash: A Cautionary Tale About Highly Fragmented Markets," Management Science, INFORMS, vol. 65(10), pages 4470-4488, October.
    11. Carrion, Allen, 2013. "Very fast money: High-frequency trading on the NASDAQ," Journal of Financial Markets, Elsevier, vol. 16(4), pages 680-711.
    12. Robert A. Jarrow & Philip Protter, 2012. "A Dysfunctional Role Of High Frequency Trading In Electronic Markets," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 15(03), pages 1-15.
    13. Brogaard, Jonathan & Garriott, Corey, 2019. "High-Frequency Trading Competition," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 54(4), pages 1469-1497, August.
    14. 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.
    15. Te Bao & Elizaveta Nekrasova & Tibor Neugebauer & Yohanes E. Riyanto, 2022. "Algorithmic trading in experimental markets with human traders: A literature survey," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 23, pages 302-322, Edward Elgar Publishing.
    16. John Duffy & Jean Paul Rabanal & Olga A. Rud, 2022. "Market experiments with multiple assets: A survey," Chapters, in: Sascha Füllbrunn & Ernan Haruvy (ed.), Handbook of Experimental Finance, chapter 18, pages 213-224, Edward Elgar Publishing.
    17. Peter Gomber & Martin Haferkorn, 2013. "High-Frequency-Trading," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 5(2), pages 97-99, April.
    18. Hendershott, Terrence & Riordan, Ryan, 2013. "Algorithmic Trading and the Market for Liquidity," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 48(4), pages 1001-1024, August.
    19. Stöckl, Thomas & Kirchler, Michael, 2014. "Trading behavior and profits in experimental asset markets with asymmetric information," Journal of Behavioral and Experimental Finance, Elsevier, vol. 2(C), pages 18-30.
    20. Aldrich, Eric M. & Friedman, Daniel, 2017. "Order protection through delayed messaging," Discussion Papers, Research Professorship Market Design: Theory and Pragmatics SP II 2017-502, WZB Berlin Social Science Center.
    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. 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, President and Fellows of Harvard College, vol. 130(4), pages 1547-1621.
    23. Gjerstad, Steven, 2007. "The competitive market paradox," Journal of Economic Dynamics and Control, Elsevier, vol. 31(5), pages 1753-1780, May.
    24. Ernan Haruvy & Charles N. Noussair, 2006. "The Effect of Short Selling on Bubbles and Crashes in Experimental Spot Asset Markets," Journal of Finance, American Finance Association, vol. 61(3), pages 1119-1157, 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. Smith, Vernon L & Suchanek, Gerry L & Williams, Arlington W, 1988. "Bubbles, Crashes, and Endogenous Expectations in Experimental Spot Asset Markets," Econometrica, Econometric Society, vol. 56(5), pages 1119-1151, September.
    27. Glenn W. Harrison, 1992. "Market Dynamics, Programmed Traders and Futures Markets: Beginning the Laboratory Search for a Smoking Gun," The Economic Record, The Economic Society of Australia, vol. 68(S1), pages 46-62, December.
    28. Yan Peng & Jason Shachat & Lijia Wei & S. Sarah Zhang, 2020. "Speed Traps: Algorithmic Trader Performance Under Alternative Market Structures," Working Papers 20-39, Chapman University, Economic Science Institute.
    29. Harrison, J. Michael & Kreps, David M., 1979. "Martingales and arbitrage in multiperiod securities markets," Journal of Economic Theory, Elsevier, vol. 20(3), pages 381-408, June.
    30. Thomas Stöckl & Jürgen Huber & Michael Kirchler, 2010. "Bubble measures in experimental asset markets," Experimental Economics, Springer;Economic Science Association, vol. 13(3), pages 284-298, September.
    31. Peter, Bossaerts & Jason, Shachat & Kuangli, Xie, 2018. "Arbitrage Opportunities: Anatomy and Remediation," MPRA Paper 87273, University Library of Munich, Germany.
    32. Shane Frederick, 2005. "Cognitive Reflection and Decision Making," Journal of Economic Perspectives, American Economic Association, vol. 19(4), pages 25-42, Fall.
    33. 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.
    34. David Easley & Marcos M. López de Prado & Maureen O'Hara, 2012. "Flow Toxicity and Liquidity in a High-frequency World," The Review of Financial Studies, Society for Financial Studies, vol. 25(5), pages 1457-1493.
    35. Füllbrunn, Sascha & Neugebauer, Tibor, 2022. "Testing market regulations in experimental asset markets – The case of margin purchases," Journal of Economic Behavior & Organization, Elsevier, vol. 200(C), pages 1160-1183.
    36. Terrance Odean, 1999. "Do Investors Trade Too Much?," American Economic Review, American Economic Association, vol. 89(5), pages 1279-1298, December.
    37. Katya Malinova & Andreas Park, 2015. "Subsidizing Liquidity: The Impact of Make/Take Fees on Market Quality," Journal of Finance, American Finance Association, vol. 70(2), pages 509-536, April.
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    Cited by:

    1. Neugebauer, Tibor & Shachat, Jason & Szymczak, Wiebke, 2023. "A test of the Modigliani-Miller theorem, dividend policy and algorithmic arbitrage in experimental asset markets," Journal of Banking & Finance, Elsevier, vol. 154(C).
    2. Brice Corgnet & Mark DeSantis & Christoph Siemroth, 2023. "Algorithmic Trading, Price Efficiency and Welfare: An Experimental Approach," Working Papers 2313, Groupe d'Analyse et de Théorie Economique Lyon St-Étienne (GATE Lyon St-Étienne), Université de Lyon.
    3. Lambrecht, Marco & Sofianos, Andis & Xu, Yilong, 2021. "Does mining fuel bubbles? An experimental study on cryptocurrency markets," Working Papers 0703, University of Heidelberg, Department of Economics.
    4. Arturo Macias, 2022. "Capital structure irrelevance in the laboratory: an experiment with complete and asymmetric information," Experimental Economics, Springer;Economic Science Association, vol. 25(5), pages 1418-1440, November.

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    More about this item

    Keywords

    asset market experiment; arbitrage; algorithmic trading;
    All these keywords.

    JEL classification:

    • C92 - Mathematical and Quantitative Methods - - Design of Experiments - - - Laboratory, Group Behavior
    • G12 - Financial Economics - - General Financial Markets - - - Asset Pricing; Trading Volume; Bond Interest Rates

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