Equilibrium Data Mining and Data Abundance

Jérôme Dugast and Thierry Foucault
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Jérôme 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 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.

Keywords: Alternative Data; Price Informativeness; Search for Information (search for similar items in EconPapers)
Date: 2020-12-11
Note: View the original document on HAL open archive server: https://hal.science/hal-03053967v1
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Citations: View citations in EconPapers (1)

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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 (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)
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