Equilibrium Data Mining and Data Abundance

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.