Machine Forecast Disagreement

Turan G. Bali, Bryan T. Kelly, Mathis Mörke and Jamil Rahman
Authors registered in the RePEc Author Service: Mathis Moerke

No 31583, NBER Working Papers from National Bureau of Economic Research, Inc

Abstract: We propose a statistical model of heterogeneous beliefs where 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 14% per year. Further analyses suggest MFD-alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

JEL-codes: C15 C4 C45 C58 G1 G10 G17 G4 G40 (search for similar items in EconPapers)
Date: 2023-08
New Economics Papers: this item is included in nep-big and nep-cmp
Note: AP
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