Details
| Original language | English |
|---|---|
| Number of pages | 90 |
| Publication status | E-pub ahead of print - 24 Feb 2020 |
| Externally published | Yes |
Publication series
| Name | University of Tübingen Working Papers in Business and Economics |
|---|---|
| No. | 130 |
Abstract
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2020. (University of Tübingen Working Papers in Business and Economics; No. 130).
Research output: Working paper/Preprint › Working paper/Discussion paper
}
TY - UNPB
T1 - Diverging Roads
T2 - Theory-Based vs. Machine Learning-Implied Stock Risk Premia
AU - Grammig, Joachim
AU - Hanenberg, Constantin
AU - Schlag, Christian
AU - Sönksen, Jantje
PY - 2020/2/24
Y1 - 2020/2/24
N2 - We assess financial theory-based and machine learning methods to quantify stock risk premia and investigate the potential of hybrid strategies. In the low signal-to-noise environment of a one-month investment horizon, we recommend to rely on a theory-based strategy that exploits the information in current option prices, especially if the risk premium estimate is to be updated at a high frequency. At the one-year horizon, the theory/option-based strategy and an ensemble of neural networks, two notably different methodologies, perform comparably well. A random forest can improve on the theory-based method, provided that a sufficiently long training period is used. In an effort to connect the opposing philosophies, we identify the use of a random forest to account for the approximation errors of the theory-based approach towards measuring stock risk premia as a promising hybrid strategy. It combines the advantages of two diverging roads in the finance world.
AB - We assess financial theory-based and machine learning methods to quantify stock risk premia and investigate the potential of hybrid strategies. In the low signal-to-noise environment of a one-month investment horizon, we recommend to rely on a theory-based strategy that exploits the information in current option prices, especially if the risk premium estimate is to be updated at a high frequency. At the one-year horizon, the theory/option-based strategy and an ensemble of neural networks, two notably different methodologies, perform comparably well. A random forest can improve on the theory-based method, provided that a sufficiently long training period is used. In an effort to connect the opposing philosophies, we identify the use of a random forest to account for the approximation errors of the theory-based approach towards measuring stock risk premia as a promising hybrid strategy. It combines the advantages of two diverging roads in the finance world.
U2 - 10.2139/ssrn.3536835
DO - 10.2139/ssrn.3536835
M3 - Working paper/Discussion paper
T3 - University of Tübingen Working Papers in Business and Economics
BT - Diverging Roads
ER -