.png){kind=icon}
Return
Multifactor Timing with Deep Learning
.png){kind=icon}
PRE
AI
DOI：10.1093/jjfinec/nbag006.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
We develop deep neural networks with economically motivated restrictions that are designed to overcome the main challenges of factor timing. Our critical innovations include integrating multitask (MT) learning to capture the common structure across factors, with long short-term memory neural networks to extract financial and macroeconomic states. This dynamic MT neural network outperforms all benchmarks in terms of predictive accuracy and economic gains. We pinpoint unemployment, along with variations on leverage, profitability, and money as key predictors, and highlight the importance of capturing their nonlinear interactions. Improved factor timing through neural networks with economic restrictions facilitates more reliable investigation into the economic mechanisms driving factor risk premia, and underscores the value of deep learning for factor investing.
Keywords:
factor timing
deep learning
economic structure
machine learning
dynamic multitask neural networks
big data
G10
G11
G12
G17
C14
C22
C45
C58
Journal
J
IF:
2.2
Papers:
13
Citations:
1.9K
Organization
View more organizations
Cited Papers
ShareSave
ShareSaveValue Return Predictability across Asset Classes and Commonalities in Risk Premia.png)
REVIEW OF FINANCE
OAAI
REVIEW OF FINANCEIF8.4
OAAIShareSaveShareSaveShareSaveShareSave View more