Useong Shin · 2026-06-22
A plain-English AI summary of what this paper means for investors — generated on demand from the abstract.
In an ideal stochastic discount factor, zero pricing errors and maximum Sharpe ratio coincide; in a low-dimensional approximation they need not. I test this separation by decomposing an investible CRSP market into capitalization-ranked body and tail legs that recombine to the market return. At the daily frequency, all models pass the aggregate benchmark, but q5 alone leaves systematic offsetting leg alphas-negative body, positive tail-at all nine split ratios, despite holding the strongest spanning position. Matched random splits remove the pattern. Monthly aggregation attenuates q5's joint rejection and shifts relative weakness toward FF3, showing that internal consistency is frequency-dependent.
Go deeper: a full research-committee breakdown of this paper, its assumptions and failure modes, and how its method would apply to a specific ticker or your watchlist. See StockTools AI →
AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.