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Diverging Roads: Theory-Based vs. Machine Learning-Implied Stock Risk Premia
| Content Provider | Scilit |
|---|---|
| Author | Grammig, Joachim Hanenberg, Constantin Schlag, Christian Sönksen, Jantje |
| Copyright Year | 2020 |
| Description | Journal: SSRN Electronic Journal We assess financial theory-based and machine learning-implied measurements of stock risk premia by comparing the quality of their return forecasts. In the low signal-to-noise environment of a one month horizon, we find that it is preferable to rely on a theory-based approach instead of engaging in the computer-intensive hyper-parameter tuning of statistical models. The theory-based approach also delivers a solid performance at the one year horizon, at which only one machine learning methodology (random forest) performs substantially better. We also consider ways to combine the opposing modeling philosophies, and identify the use of random forests to account for the approximation residuals of the theory-based approach as a promising hybrid strategy. It combines the advantages of the two diverging paths in the finance world. |
| Related Links | https://www.econstor.eu/bitstream/10419/213939/1/1689891602.pdf https://papers.ssrn.com/sol3/Delivery.cfm?abstractid=3536835 |
| ISSN | 10914358 |
| e-ISSN | 15565068 |
| DOI | 10.2139/ssrn.3536835 |
| Journal | SSRN Electronic Journal |
| Language | English |
| Publisher | Elsevier BV |
| Publisher Date | 2020-02-12 |
| Access Restriction | Open |
| Subject Keyword | Journal: SSRN Electronic Journal Stock Risk Premia Return Forecasts Machine Learning Theory-based Return Prediction |
| Content Type | Text |
| Resource Type | Article |
| Subject | Public Health, Environmental and Occupational Health Psychiatry and Mental Health |
