Equilibrium Data Mining and Data Abundance

Jerome Dugast and Thierry Foucault
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Jerome Dugast: Université Paris-Dauphine, PSL Research University; Université Paris Dauphine - Department of Finance

No 1393, HEC Research Papers Series from HEC Paris

Abstract: We study theoretically how the proliferation of new data (``data abundance") affects the allocation of capital between quantitative and non-quantitative asset managers (``data miners" and ``experts"), their performance, and price informativeness. Data miners search for predictors of asset payoffs and select those with a sufficiently high precision. Data abundance raises the precision of the best predictors but it can induce data miners to search less intensively for high precision signals. In this case, their performance becomes more dispersed, and they receive less capital. Nevertheless, data abundance always raises price informativeness and can therefore reduce asset managers' average performance.

Keywords: Big Data; Active Asset Management; Data Mining; Price Informativeness. (search for similar items in EconPapers)
JEL-codes: C55 (search for similar items in EconPapers)
Pages: 62 pages
Date: 2021-01-27
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https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3710495 Full text (text/html)

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 (2023)
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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Persistent link: https://EconPapers.repec.org/RePEc:ebg:heccah:1393

DOI: 10.2139/ssrn.3710495

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