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Long Run Growth of Financial Technology

Author

Listed:
  • 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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    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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