Machine Forecast Disagreement / Turan G. Bali, Bryan T. Kelly, Mathis Mörke, Jamil Rahman.
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- Statistical Simulation Methods: General
- Statistical Simulation Methods: General
- Econometric and Statistical Methods: Special Topics
- Econometric and Statistical Methods: Special Topics
- Neural Networks and Related Topics
- Neural Networks and Related Topics
- Financial Econometrics
- Financial Econometrics
- General Financial Markets
- General Financial Markets
- General
- General
- Financial Forecasting and Simulation
- Financial Forecasting and Simulation
- Behavioral Finance
- Behavioral Finance
- General
- General
- C15
- C4
- C45
- C58
- G1
- G10
- G17
- G4
- G40
- Hardcopy version available to institutional subscribers
Item type | Home library | Collection | Call number | Status | Date due | Barcode | Item holds | |
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Working Paper | Biblioteca Digital | Colección NBER | nber w31583 (Browse shelf(Opens below)) | Not For Loan |
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August 2023.
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 average return of 14% per year. A range of analyses suggest the alpha is mispricing induced by short-sale costs and limits-to-arbitrage.
Hardcopy version available to institutional subscribers
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