Data and Code For: Long Run Growth of Financial Data Technology
Principal Investigator(s): View help for Principal Investigator(s) Maryam Farboodi, MIT Sloan; Laura Veldkamp, Columbia Business School
Version: View help for Version V3
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| FarboodiVeldkamp_DataCode | 04/20/2020 10:07:PM | ||
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Project Citation:
Farboodi, Maryam, and Veldkamp, Laura. Data and Code For: Long Run Growth of Financial Data Technology. Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-04-20. https://doi.org/10.3886/E114984V3
Project Description
Summary:
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"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 resolves investment risk, but future data creates 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.
Funding Sources:
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Goldman Sachs, Global Markets Institute (GMI) fellowship
Scope of Project
JEL Classification:
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E02 Institutions and the Macroeconomy
G14 Information and Market Efficiency; Event Studies; Insider Trading
E02 Institutions and the Macroeconomy
G14 Information and Market Efficiency; Event Studies; Insider Trading
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