- Tytuł:
- Forecasting the equity premium: Do deep neural network models work?
- Autorzy:
-
Zhou, Xianzheng
Zhou, Hui
Long, Huaigang - Powiązania:
- https://bibliotekanauki.pl/articles/23943440.pdf
- Data publikacji:
- 2023
- Wydawca:
- Fundacja Naukowa Instytut Współczesnych Finansów
- Tematy:
-
equity premium
return predictability
deep neural network
asset allocation
forecasting performance - Opis:
- This paper constructs deep neural network (DNN) models for equity-premium forecasting. We compare the forecasting performance of DNN models with that of ordinary least squares (OLS) and historical average (HA) models. The DNN models robustly work best and significantly outperform both OLS and HA models in both in- and out-of-sample tests and asset allocation exercises. Specifically, DNN models generate monthly out-of-sample R2 of 3.42% and an annual utility gain of 2.99% for a mean-variance investor from 2011:1 to 2016:12. Moreover, the forecasting performance of DNN models is enhanced by adding additional 14 variables selected from finance literature.
- Źródło:
-
Modern Finance; 2023, 1, 1; 1-11
2956-7742 - Pojawia się w:
- Modern Finance
- Dostawca treści:
- Biblioteka Nauki
Artykuł