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Long-Run Growth of Financial Data Technology

Author(s)
Farboodi, Maryam
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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Abstract
“Big data” financial technology raises concerns about market inefficiency. A common concern is that the technology might induce traders to extract others’information, rather than to produce information themselves. We allow agents to choose how much they learn about future asset values or about others’ demands, and we explore how improvements in data processing shape these information choices, trading strategies and market outcomes. Our main insight is that unbiased technological change can explain a market-wide shift in data collection and trading strategies. However, in the long run, as data processing technology becomes increasingly advanced, both types of data continue to be processed. Two competing forces keep the data economy in balance: data resolve investment risk, but future data create risk. The efficiency results that follow from these competing forces upend two pieces of common wisdom: our results offer a new take on what makes prices informative and whether trades typically deemed liquidity-providing actually make markets more resilient.
Date issued
2020-08
URI
https://hdl.handle.net/1721.1/129767
Department
Sloan School of Management
Journal
American Economic Review
Publisher
American Economic Association
Citation
Farboodi, Maryam and Laura Veldkamp. “Long-Run Growth of Financial Data Technology.” American Economic Review, 110, 8 (August 2020): 2485-2523 © 2020 The Author(s)
Version: Final published version
ISSN
0002-8282

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