Equilibrium Data Mining and Data Abundance

• 成果类型: Article
• 署名作者: Dugast, Jerome; Foucault, Thierry
• 署名单位: Universite PSL; Universite Paris-Dauphine; Centre National de la Recherche Scientifique (CNRS); Hautes Etudes Commerciales (HEC) Paris
• 刊物名称: JOURNAL OF FINANCE
• ISSN/ISSBN: 0022-1082
• DOI: 10.1111/jofi.13397
• 发表日期: 2025
• 页码: 211-258
• 关键词: FUND SIZE; INFORMATION; INVESTMENT; skill

摘要

We study theoretically how the proliferation of new data (data abundance) affects the allocation of capital between quantitative and nonquantitative 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.