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Data and Code For: Long Run Growth of Financial Data Technology

Version
V0
Resource Type
Dataset
Creator
  • Farboodi, Maryam (MIT Sloan)
  • Veldkamp, Laura (Columbia Business School)
Publication Date
2019-01-03
Funding Reference
  • Goldman Sachs, Global Markets Institute (GMI) fellowship
Description
  • 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 theylearn 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 thelong 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.
Availability
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Update Metadata: 2019-10-17 | Issue Number: 1 | Registration Date: 2019-10-17

Farboodi, Maryam; Veldkamp, Laura (2019): Data and Code For: Long Run Growth of Financial Data Technology. Version: V0. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E114984