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Long Run Growth of Financial Technology

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  • Maryam Farboodi
  • Laura Veldkamp

Abstract

In most sectors, technological progress boosts efficiency. But financial technology and the associated data-intensive trading strategies have been blamed for market inefficiency. A key cause for concern is that better technology might induce traders to extract other's information from order flow data mining, rather than produce information themselves. Defenders of these new trading strategies argue that they provide liquidity by identifying uninformed orders and taking the other side of their trades. We adopt the lens of long-run growth to understand how improvements in financial technology shape information choices, trading strategies and market efficiency, as measured by price informativeness and market liquidity. We find that unbiased technological change can explain a market-wide shift in data collection and trading strategies. But our findings also cast doubt on common wisdom. First, although extracting information from order flow does crowd out production of fundamental information, this does not compromise price informativeness. Second, although taking the opposite side of uninformed trades is typically called "providing liquidity," the rise of such trading strategies does not necessarily improve liquidity in the market as a whole.

Suggested Citation

  • Maryam Farboodi & Laura Veldkamp, 2017. "Long Run Growth of Financial Technology," NBER Working Papers 23457, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:23457
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    References listed on IDEAS

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    1. Thomas Philippon, 2015. "Has the US Finance Industry Become Less Efficient? On the Theory and Measurement of Financial Intermediation," American Economic Review, American Economic Association, vol. 105(4), pages 1408-1438, April.
    2. Grossman, Sanford J & Stiglitz, Joseph E, 1980. "On the Impossibility of Informationally Efficient Markets," American Economic Review, American Economic Association, vol. 70(3), pages 393-408, June.
    3. Songzi Du & Haoxiang Zhu, 2017. "What is the Optimal Trading Frequency in Financial Markets?," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 84(4), pages 1606-1651.
    4. Biais, Bruno & Foucault, Thierry & Moinas, Sophie, 2015. "Equilibrium fast trading," Journal of Financial Economics, Elsevier, vol. 116(2), pages 292-313.
    5. Martin Lettau & Sydney C. Ludvigson & Jessica A. Wachter, 2008. "The Declining Equity Premium: What Role Does Macroeconomic Risk Play?," The Review of Financial Studies, Society for Financial Studies, vol. 21(4), pages 1653-1687, July.
    6. Giovanni Cespa & Xavier Vives, 2012. "Dynamic Trading and Asset Prices: Keynes vs. Hayek," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 79(2), pages 539-580.
    7. Jiang Wang, 1993. "A Model of Intertemporal Asset Prices Under Asymmetric Information," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 60(2), pages 249-282.
    8. Banerjee, Snehal & Green, Brett, 2015. "Signal or noise? Uncertainty and learning about whether other traders are informed," Journal of Financial Economics, Elsevier, vol. 117(2), pages 398-423.
    9. Bai, Jennie & Philippon, Thomas & Savov, Alexi, 2016. "Have financial markets become more informative?," Journal of Financial Economics, Elsevier, vol. 122(3), pages 625-654.
    10. Kacperczyk, Marcin & Nosal, Jaromir & Stevens, Luminita, 2019. "Investor sophistication and capital income inequality," Journal of Monetary Economics, Elsevier, vol. 107(C), pages 18-31.
    11. Vanasco, Victoria & Asriyan, Vladimir, 2014. "Informed Intermediation over the Cycle," Research Papers 3235, Stanford University, Graduate School of Business.
    12. Vincent Glode & Richard C. Green & Richard Lowery, 2012. "Financial Expertise as an Arms Race," Journal of Finance, American Finance Association, vol. 67(5), pages 1723-1759, October.
    13. Eduardo Dávila & Cecilia Parlatore, 2021. "Trading Costs and Informational Efficiency," Journal of Finance, American Finance Association, vol. 76(3), pages 1471-1539, June.
    14. Bali, Turan G. & Brown, Stephen J. & Caglayan, Mustafa O., 2014. "Macroeconomic risk and hedge fund returns," Journal of Financial Economics, Elsevier, vol. 114(1), pages 1-19.
    15. Manzano, Carolina & Vives, Xavier, 2011. "Public and private learning from prices, strategic substitutability and complementarity, and equilibrium multiplicity," Journal of Mathematical Economics, Elsevier, vol. 47(3), pages 346-369.
    16. Edelman, Daniel & Fung, William & Hsieh, David A., 2013. "Exploring uncharted territories of the hedge fund Industry: Empirical characteristics of mega hedge fund firms," Journal of Financial Economics, Elsevier, vol. 109(3), pages 734-758.
    17. Jayant Vivek Ganguli & Liyan Yang, 2009. "Complementarities, Multiplicity, and Supply Information," Journal of the European Economic Association, MIT Press, vol. 7(1), pages 90-115, March.
    18. 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.
    19. Zhiguo He, 2009. "The Sale of Multiple Assets with Private Information," The Review of Financial Studies, Society for Financial Studies, vol. 22(11), pages 4787-4820, November.
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    Cited by:

    1. Andrea Barbon & Marco Di Maggio & Francesco Franzoni & Augustin Landier, 2019. "Brokers and Order Flow Leakage: Evidence from Fire Sales," Journal of Finance, American Finance Association, vol. 74(6), pages 2707-2749, December.
    2. Zhifeng Cai, 2020. "Dynamic information acquisition and time-varying uncertainty," Departmental Working Papers 202002, Rutgers University, Department of Economics.
    3. Jan Schneemeier, 2019. "Shock Propagation Through Cross-Learning in Opaque Networks," 2019 Meeting Papers 329, Society for Economic Dynamics.
    4. Marco Di Maggio & Francesco Franzoni & Amir Kermani & Carlo Sommavilla, 2017. "The Relevance of Broker Networks for Information Diffusion in the Stock Market," NBER Working Papers 23522, National Bureau of Economic Research, Inc.
    5. Peress, Joel & Schmidt, Daniel, 2021. "Noise traders incarnate: Describing a realistic noise trading process," Journal of Financial Markets, Elsevier, vol. 54(C).
    6. Cai, Zhifeng, 2019. "Dynamic information acquisition and time-varying uncertainty," Journal of Economic Theory, Elsevier, vol. 184(C).
    7. Marmora, Paul & Rytchkov, Oleg, 2018. "Learning about noise," Journal of Banking & Finance, Elsevier, vol. 89(C), pages 209-224.
    8. Walther, Ansgar & Uettwiller, Antoine, 2019. "The Market for Data Privacy," CEPR Discussion Papers 13588, C.E.P.R. Discussion Papers.

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    More about this item

    JEL classification:

    • E2 - Macroeconomics and Monetary Economics - - Consumption, Saving, Production, Employment, and Investment
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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