Události

Authors: Jérôme Dugast, Thierry Foucault

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

Full Text: Equilibrium Data Mining and Data Abundance