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


  • Andrei A. Kirilenko
  • Andrew W. Lo


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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    References listed on IDEAS

    1. 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.
    2. Lo, Andrew W & MacKinlay, A Craig, 1990. "When Are Contrarian Profits Due to Stock Market Overreaction?," Review of Financial Studies, Society for Financial Studies, vol. 3(2), pages 175-205.
    3. 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.
    4. Gorton, Gary & Metrick, Andrew, 2012. "Securitized banking and the run on repo," Journal of Financial Economics, Elsevier, vol. 104(3), pages 425-451.
    5. Rosenberg, Barr, 1974. "Extra-Market Components of Covariance in Security Returns," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 9(02), pages 263-274, March.
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    Cited by:

    1. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part I: The Models," Papers 1401.1888,, revised Feb 2016.
    2. Rossi, S & Tinn, K, 2012. "Man or Machine? Rational trading without information about fundamentals," Working Papers 12194, Imperial College, London, Imperial College Business School.
    3. repec:kap:compec:v:50:y:2017:i:3:d:10.1007_s10614-016-9591-2 is not listed on IDEAS
    4. Sandrine Jacob Leal & Mauro Napoletano, 2016. "Market Stability vs. Market Resilience: Regulatory Policies Experiments in an Agent-Based Model with Low- and High- Frequency Trading," Sciences Po publications 2016-12, Sciences Po.
    5. 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.
    6. 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.
    7. Mike Farjam & Oliver Kirchkamp, 2015. "Bubbles in Hybrid Markets - How Expectations about Algorithmic Trading Affect Human Trading," CESifo Working Paper Series 5631, CESifo Group Munich.
    8. repec:eee:finlet:v:22:y:2017:i:c:p:136-139 is not listed on IDEAS
    9. 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.
    10. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part II: Analysis of the Models," Papers 1401.1891,, revised Feb 2016.
    11. repec:eee:riibaf:v:41:y:2017:i:c:p:158-171 is not listed on IDEAS
    12. Li-Xin Wang, 2014. "Dynamical Models of Stock Prices Based on Technical Trading Rules Part III: Application to Hong Kong Stocks," Papers 1401.1892,, revised Feb 2016.

    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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