Machine Forecast Disagreement

• 成果类型: Article; Early Access
• 署名作者: Bali, Turan G.; Kelly, Bryan T.; Morke, Mathis; Rahman, Jamil
• 署名单位: Georgetown University; Yale University; National Bureau of Economic Research; Yale University
• 刊物名称: REVIEW OF FINANCIAL STUDIES
• ISSN/ISSBN: 0893-9454; 1465-7368
• DOI: 10.1093/rfs/hhag042
• 发表日期: 2026-06-22
• 关键词: G10; G11; G12; G14; cross-section; stock-market; Investor sentiment; SHORT-SALES; OF-INTEREST; opinion; dispersion; BEHAVIOR; RISK; arbitrage

摘要

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.