Machine Forecast Disagreement

Turan G. Bali; Bryan T. Kelly; Mathis Mörke; Jamil Rahman

doi:10.1093/rfs/hhag042

Machine Forecast Disagreement

Turan G. Bali¹; Bryan T. Kelly²; Mathis Mörke³; Jamil Rahman⁴

¹ Georgetown University , the · ² Yale University, AQR Capital Management, and NBER , the · ³ ESCP Business School Paris , · ⁴ Yale University , the

Review of Financial Studies 2026 open access

Abstract We propose a statistical model of heterogeneous beliefs wherein investors are represented as different machine learning model specifications. Investors form return forecasts from their individual models using common data inputs. We measure disagreement as forecast dispersion across investor-models (MFD). Our measure aligns with analyst forecast disagreement but more powerfully predicts returns. We document a large and robust association between belief disagreement and future returns. A decile spread portfolio that sells stocks with high disagreement and buys stocks with low disagreement earns a value-weighted return of 13% per year. Further analyses suggest MFD-alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

DOI: 10.1093/rfs/hhag042
Language: en
Export: BibTeX
Sources: openalex crossref