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Equilibrium Data Mining and Data Abundance

Dugast, Jérôme; Foucault, Thierry (2020), Equilibrium Data Mining and Data Abundance. https://basepub.dauphine.fr/handle/123456789/21288

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Dugast,Foucault2020.pdf (979.0Kb)
Type
Document de travail / Working paper
Date
2020
Series title
HEC Paris Research Paper
Series number
FIN-2020-1393
Pages
49
Metadata
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Author(s)
Dugast, Jérôme
Dauphine Recherches en Management [DRM]
Foucault, Thierry
HEC Paris - Recherche - Hors Laboratoire
Abstract (EN)
We analyze how computing power and data abundance affect speculators' search for predictors. In our model, speculators search for predictors through trials and optimally stop searching when they find a predictor with a signal-to-noise ratio larger than an endogenous threshold. Greater computing power raises this threshold, and therefore price informativeness, by reducing search costs. In contrast, data abundance can reduce this threshold because it intensifies competition among speculators and it increases the average number of trials to find a predictor. In the former (latter) case, price informativeness increases (decreases) with data abundance. We derive implications of these effects for the distribution of asset managers' skills and trading profits.
Subjects / Keywords
Alternative Data; Price Informativeness; Search for Information
JEL
G14 - Information and Market Efficiency; Event Studies; Insider Trading
D84 - Expectations; Speculations

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