Equilibrium Data Mining and Data Abundance

Jérome Dugast and Thierry Foucault
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Jérome Dugast: DRM - Dauphine Recherches en Management - Université Paris Dauphine-PSL - PSL - Université Paris Sciences et Lettres - CNRS - Centre National de la Recherche Scientifique

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Abstract: We study, using a noisy rational expectations framework, how the availability of new data to forecast asset payoffs ("data abundance") affect the capital allocated to quantitative asset managers ("data miners") relative to other active asset managers, the mean and the cross-sectional dispersion of their performance, and price informativeness. Data miners search for predictors of asset payoffs and trade when they find one with a sufficiently high precision. Data abundance raises the precision of the best predictors. Yet, it eventually induces data miners to lower the bar for their signal precision. Then, their performance becomes more dispersed, and they receive less capital. Overall, data abundance is both a catalyst and an impediment to the rise of quant funds.

Keywords: Big Data; Active Asset Management; Data Mining; Price Informativeness (search for similar items in EconPapers)
Date: 2023-05-04
New Economics Papers: this item is included in nep-fdg and nep-mac
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Published in Macro Research Seminar 2023, CERGE-EI (Center for Economic Research and Graduate Education – Economics Institute), May 2023, Prague, Czech Republic

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Related works:
Working Paper: Equilibrium Data Mining and Data Abundance (2023)
Working Paper: Equilibrium Data Mining and Data Abundance (2023)
Working Paper: Equilibrium Data Mining and Data Abundance (2021)
Working Paper: Equilibrium Data Mining and Data Abundance (2020)
Working Paper: Equilibrium Data Mining and Data Abundance (2020)
Working Paper: Equilibrium Data Mining and Data Abundance (2020)
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