Machine Forecast Disagreement / Turan G. Bali, Bryan T. Kelly, Mathis Mörke, Jamil Rahman.

Format:

Book

Author/Creator:

Bali, Turan G.

Contributor:

National Bureau of Economic Research.

Kelly, Bryan T.

Mörke, Mathis.

Rahman, Jamil.

Series:

Working Paper Series (National Bureau of Economic Research) no. w31583.

NBER working paper series no. w31583

Language:

English

Physical Description:

1 online resource: illustrations (black and white);

Place of Publication:

Cambridge, Mass. National Bureau of Economic Research 2023.

Summary:

We propose a statistical model of differences in beliefs in which heterogeneous investors are represented as different machine learning model specifications. Each investor forms return forecasts from their own specific model using data inputs that are available to all investors. We measure disagreement as dispersion in forecasts across investor-models. Our measure aligns with extant measures of disagreement (e.g., analyst forecast dispersion), but is a significantly stronger predictor of future returns. We document a large, significant, and highly robust negative cross-sectional relation between belief disagreement and future returns. A decile spread portfolio that is short stocks with high forecast disagreement and long stocks with low disagreement earns a value-weighted alpha of 15% per year. A range of analyses suggest the alpha is mispricing induced by short-sale costs and limits-to-arbitrage.

Notes:

Print version record

August 2023.