Long Run Growth of Financial Technology
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.
We thank Marios Angeletos, Markus Brunnermeier, Martin Eichenbaum, Sergio Rebelo, Steven Strongin and Xavier Vives, seminar and conference participants at Cornell, Fordham, Maryland, NYU, Princeton, Stanford, Yale and the SED conference, the NASDAQ DRP research day and the LAEF conference on information in finance for comments. We thank Goldman Sachs for their financial support through the GMI Fellowship program. We thank John Barry, Chase Coleman, Matias Covarrubias, Roxana Mihet and Arnav Sood for their capable research assistance. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
I have visited or lectured at the following institutions, where I have received an honorarium and/or have been paid travel expenses:
EIEF, Rome, Italy, research visitor.
Federal Reserve Bank of New York, US. As consultant to the Research Department.
Federal Reserve Bank of Minneapolis, US. As consultant to the Research Department.
Goldman Sachs, as a GMI fellow.
Standard & Poors, one-time honorarium.
University of California at Los Angeles, as a guest Ph.D. lecturer
I also receive a salary from Elsevier as an editor of the Journal of Economic Theory.
Maryam Farboodi & Laura Veldkamp, 2020. "Long-Run Growth of Financial Data Technology," American Economic Review, vol 110(8), pages 2485-2523.