In knowledge-based economies, many businesses enterprises defy traditional industry boundaries. In this study, we evaluate six "big data" approaches to peer firm identifications and show that some, but not all, "wisdom-of-crowd" techniques perform exceptionally well. We propose an analytical framework for understanding when crowds can be expected to provide wisdom and show, theoretically and empirically, that their efficacy is related to crowd sophistication and task complexity. Consistent with this framework, we find that a "crowd-of-crowds" approach, which combines EDGAR user co-searches and analyst co-coverage, dominates other state-of-the-art methods for identifying investment benchmarks.
