13F Scraping: Optimal Institutional Following via Dynamic Performance Sieve

1. Strategy Overview

This strategy exploits manager skill heterogeneity within the institutional investor universe by dynamically selecting subsets of 13F filers whose recent performance justifies following, rather than treating all disclosed positions equally. The core edge rests on three mechanisms:

1. Skill differentiation: Not all institutional investors generate alpha. A rolling performance filter identifies managers whose disclosed holdings have outperformed, creating a signal of genuine stock-picking ability [Bender et al. 2013].