Score contribution per author:
α: calibrated so average coauthorship-adjusted count equals average raw count
We propose a model selection method to systematically evaluate the contribution to asset pricing of any new factor, above and beyond what a high‐dimensional set of existing factors explains. Our methodology accounts for model selection mistakes that produce a bias due to omitted variables, unlike standard approaches that assume perfect variable selection. We apply our procedure to a set of factors recently discovered in the literature. While most of these new factors are shown to be redundant relative to the existing factors, a few have statistically significant explanatory power beyond the hundreds of factors proposed in the past.