Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | Econometrics with Machine Learning |
| Herausgeber/-innen | Felix Chan, László Mátyás |
| Herausgeber (Verlag) | Springer International Publishing AG |
| Seiten | 337–366 |
| Seitenumfang | 29 |
| Band | 53 |
| ISBN (elektronisch) | 978-3-031-15149-1 |
| ISBN (Print) | 978-3-031-15148-4 |
| Publikationsstatus | Veröffentlicht - 7 Sept. 2022 |
| Extern publiziert | Ja |
Abstract
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Econometrics with Machine Learning. Hrsg. / Felix Chan; László Mátyás. Band 53 Springer International Publishing AG, 2022. S. 337–366.
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Beitrag in Buch/Sammelwerk › Forschung
}
TY - CHAP
T1 - Machine Learning for Asset Pricing
AU - Sönksen, Jantje
PY - 2022/9/7
Y1 - 2022/9/7
N2 - This chapter reviews the growing literature that describes machine learning applications in the field of asset pricing. In doing so, it focuses on the additional benefits that machine learning – in addition to, or in combination with, standard econometric approaches – can bring to the table. This issue is of particular importance because in recent years, improved data availability and increased computational facilities have had huge effects on finance literature. For example, machine learning techniques inform analyses of conditional factor models; they have been applied to identify the stochastic discount factor and purposefully to test and evaluate existing asset pricing models. Beyond those pertinent applications, machine learning techniques also lend themselves to prediction problems in the domain of empirical asset pricing.
AB - This chapter reviews the growing literature that describes machine learning applications in the field of asset pricing. In doing so, it focuses on the additional benefits that machine learning – in addition to, or in combination with, standard econometric approaches – can bring to the table. This issue is of particular importance because in recent years, improved data availability and increased computational facilities have had huge effects on finance literature. For example, machine learning techniques inform analyses of conditional factor models; they have been applied to identify the stochastic discount factor and purposefully to test and evaluate existing asset pricing models. Beyond those pertinent applications, machine learning techniques also lend themselves to prediction problems in the domain of empirical asset pricing.
U2 - 10.1007/978-3-031-15149-1
DO - 10.1007/978-3-031-15149-1
M3 - Contribution to book/anthology
SN - 978-3-031-15148-4
VL - 53
SP - 337
EP - 366
BT - Econometrics with Machine Learning
A2 - Chan, Felix
A2 - Mátyás, László
PB - Springer International Publishing AG
ER -