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Moore's Law versus Murphy's Law: Algorithmic Trading and Its Discontents

Author

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  • Andrei A. Kirilenko
  • Andrew W. Lo

Abstract

Financial markets have undergone a remarkable transformation over the past two decades due to advances in technology. These advances include faster and cheaper computers, greater connectivity among market participants, and perhaps most important of all, more sophisticated trading algorithms. The benefits of such financial technology are evident: lower transactions costs, faster executions, and greater volume of trades. However, like any technology, trading technology has unintended consequences. In this paper, we review key innovations in trading technology starting with portfolio optimization in the 1950s and ending with high-frequency trading in the late 2000s, as well as opportunities, challenges, and economic incentives that accompanied these developments. We also discuss potential threats to financial stability created or facilitated by algorithmic trading and propose "Financial Regulation 2.0," a set of design principles for bringing the current financial regulatory framework into the Digital Age.

Suggested Citation

  • 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.
  • Handle: RePEc:aea:jecper:v:27:y:2013:i:2:p:51-72
    Note: DOI: 10.1257/jep.27.2.51
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    File URL: http://www.aeaweb.org/articles.php?doi=10.1257/jep.27.2.51
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    References listed on IDEAS

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    1. Umlauf, Steven R., 1993. "Transaction taxes and the behavior of the Swedish stock market," Journal of Financial Economics, Elsevier, vol. 33(2), pages 227-240, April.
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    3. Gorton, Gary & Metrick, Andrew, 2012. "Securitized banking and the run on repo," Journal of Financial Economics, Elsevier, vol. 104(3), pages 425-451.
    4. 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.
    5. Rosenberg, Barr, 1974. "Extra-Market Components of Covariance in Security Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(2), pages 263-274, March.
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    Citations

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    Cited by:

    1. Marco B Caminati & Manfred Kerber & Christoph Lange & Colin Rowat, 2015. "Sound Auction Specification and Implementation," Discussion Papers 15-08, Department of Economics, University of Birmingham.
    2. Degiannakis, Stavros & Filis, George & Siourounis, Grigorios & Trapani, Lorenzo, 2019. "Superkurtosis," MPRA Paper 94473, University Library of Munich, Germany.
      • Degiannakis, Stavros & Filis, George & Siourounis, Grigorios & Trapani, Lorenzo, 2019. "Superkurtosis," MPRA Paper 96563, University Library of Munich, Germany.
    3. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part I: The Models," Papers 1401.1888, arXiv.org, revised Feb 2016.
    4. Jaqueson K. Galimberti & Nicolas Suhadolnik & Sergio Silva, 2017. "Cowboying Stock Market Herds with Robot Traders," Computational Economics, Springer;Society for Computational Economics, vol. 50(3), pages 393-423, October.
    5. Corsetti, G. & Lafarguette, R. & Mehl, A., 2019. "Fast Trading and the Virtue of Entropy: Evidence from the Foreign Exchange Market," Cambridge Working Papers in Economics 1970, Faculty of Economics, University of Cambridge.
    6. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    7. Rossi, S & Tinn, K, 2012. "Man or Machine? Rational trading without information about fundamentals," Working Papers 12194, Imperial College, London, Imperial College Business School.
    8. Angerer, Martin & Neugebauer, Tibor & Shachat, Jason, 2019. "Arbitrage bots in experimental asset markets," MPRA Paper 96224, University Library of Munich, Germany.
    9. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models," Papers 1401.1891, arXiv.org, revised Feb 2016.
    10. Leal, Sandrine Jacob & Napoletano, Mauro, 2019. "Market stability vs. market resilience: Regulatory policies experiments in an agent-based model with low- and high-frequency trading," Journal of Economic Behavior & Organization, Elsevier, vol. 157(C), pages 15-41.
    11. 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.
    12. Tamer Khraisha & Keren Arthur, 2018. "Can we have a general theory of financial innovation processes? A conceptual review," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 4(1), pages 1-27, December.
    13. Gianluca Piero Maria Virgilio, 2019. "High-frequency trading: a literature review," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(2), pages 183-208, June.
    14. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part III: Application to Hong Kong Stocks," Papers 1401.1892, arXiv.org, revised Feb 2016.
    15. Farjam, Mike & Kirchkamp, Oliver, 2018. "Bubbles in hybrid markets: How expectations about algorithmic trading affect human trading," Journal of Economic Behavior & Organization, Elsevier, vol. 146(C), pages 248-269.
    16. Wall, Larry D., 2018. "Some financial regulatory implications of artificial intelligence," Journal of Economics and Business, Elsevier, vol. 100(C), pages 55-63.
    17. Sandrine Jacob Leal & Mauro Napoletano & Andrea Roventini & Giorgio Fagiolo, 2016. "Rock around the clock: An agent-based model of low- and high-frequency trading," Journal of Evolutionary Economics, Springer, vol. 26(1), pages 49-76, March.
    18. Virgilio, Gianluca, 2017. "Is high-frequency trading tiering the financial markets?," Research in International Business and Finance, Elsevier, vol. 41(C), pages 158-171.
    19. Zhou, Hao & Kalev, Petko S., 2019. "Algorithmic and high frequency trading in Asia-Pacific, now and the future," Pacific-Basin Finance Journal, Elsevier, vol. 53(C), pages 186-207.
    20. Flood, M. D. & Jagadish, H. V. & Raschid, L., 2016. "Big data challenges and opportunities in financial stability monitoring," Financial Stability Review, Banque de France, issue 20, pages 129-142, April.
    21. Hudson, Robert & McGroarty, Frank & Urquhart, Andrew, 2017. "Sampling frequency and the performance of different types of technical trading rules," Finance Research Letters, Elsevier, vol. 22(C), pages 136-139.
    22. Kromidha, Endrit & Li, Matthew C., 2019. "Determinants of leadership in online social trading: A signaling theory perspective," Journal of Business Research, Elsevier, vol. 97(C), pages 184-197.
    23. Mike Farjam & Oliver Kirchkamp, 2015. "Bubbles in Hybrid Markets - How Expectations about Algorithmic Trading Affect Human Trading," CESifo Working Paper Series 5631, CESifo.

    More about this item

    JEL classification:

    • E44 - Macroeconomics and Monetary Economics - - Money and Interest Rates - - - Financial Markets and the Macroeconomy
    • G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
    • G20 - Financial Economics - - Financial Institutions and Services - - - General

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