Details
| Originalsprache | Englisch |
|---|---|
| Seiten (von - bis) | 91-118 |
| Seitenumfang | 28 |
| Fachzeitschrift | Journal of Financial Markets |
| Jahrgang | 44 |
| Frühes Online-Datum | 22 März 2019 |
| Publikationsstatus | Veröffentlicht - Juni 2019 |
Abstract
Researchers and practitioners face many choices when estimating an asset's sensitivities toward risk factors, i.e., betas. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as simple shrinkage adjustments yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors and fail to create market-neutral anomaly portfolios. Finally, we document a robust link between stock characteristics and beta predictability.
ASJC Scopus Sachgebiete
- Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.)
- Finanzwesen
- Volkswirtschaftslehre, Ökonometrie und Finanzen (insg.)
- Volkswirtschaftslehre und Ökonometrie
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in: Journal of Financial Markets, Jahrgang 44, 06.2019, S. 91-118.
Publikation: Beitrag in Fachzeitschrift › Artikel › Forschung › Peer-Review
}
TY - JOUR
T1 - Estimating beta
T2 - Forecast adjustments and the impact of stock characteristics for a broad cross-section
AU - Hollstein, Fabian
AU - Prokopczuk, Marcel
AU - Wese Simen, Chardin
PY - 2019/6
Y1 - 2019/6
N2 - Researchers and practitioners face many choices when estimating an asset's sensitivities toward risk factors, i.e., betas. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as simple shrinkage adjustments yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors and fail to create market-neutral anomaly portfolios. Finally, we document a robust link between stock characteristics and beta predictability.
AB - Researchers and practitioners face many choices when estimating an asset's sensitivities toward risk factors, i.e., betas. Using the entire U.S. stock universe and a sample period of more than 50 years, we find that a historical estimator based on daily return data with an exponential weighting scheme as well as simple shrinkage adjustments yield the best predictions for future beta. Adjustments for asynchronous trading, macroeconomic conditions, or regression-based combinations, on the other hand, typically yield very high prediction errors and fail to create market-neutral anomaly portfolios. Finally, we document a robust link between stock characteristics and beta predictability.
KW - Beta estimation
KW - Forecast adjustments
KW - Forecast combinations
UR - http://www.scopus.com/inward/record.url?scp=85064594847&partnerID=8YFLogxK
U2 - 10.1016/j.finmar.2019.03.001
DO - 10.1016/j.finmar.2019.03.001
M3 - Article
AN - SCOPUS:85064594847
VL - 44
SP - 91
EP - 118
JO - Journal of Financial Markets
JF - Journal of Financial Markets
SN - 1386-4181
ER -