Multifactor Timing with Deep Learning.
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 network...
| Publicado en: | Journal of Financial Econometrics Vol. 24; no. 3; pp. 1 - 31 |
|---|---|
| Autores principales: | , , |
| Formato: | Artículo |
| Publicado: |
Oxford University Press / USA
2026
|
| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=194637000&site=ehost-live header: @attributes: shortDbName: ssf uiTerm: 194637000 longDbName: Social Sciences Full Text (H.W. Wilson) uiTag: AN controlInfo: bkinfo: jinfo: jid: 14798409 T2Y jtl: Journal of Financial Econometrics issn: 14798409 maglogo: N pubinfo: dt: 2026 vid: 24 iid: 3 pid: 622 pub: Oxford University Press / USA artinfo: ui: 194637000 10.1093/jjfinec/nbag006 ppf: 1 ppct: 30 formats: tig: atl: Multifactor Timing with Deep Learning. aug: au: Cotturo, Paul Liu, Fred Proner, Robert affil: Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON N2L3G1, Canada Department of Economics and Finance, University of Guelph, Guelph, ON N1G2W1, CanadaDepartment of Economics, University of Western Ontario, London, ON N6A3K7, Canada Department of Economics, University of Western Ontario, London, ON N6A3K7, CanadaDepartment of Economics, University of Toronto, Toronto, ON M5S1A1, Canada su: Economic indicators Long short-term memory Deep learning Investment management Machine learning Business forecasting Artificial neural networks sug: subj: Economic indicators Investment Advice Miscellaneous Financial Investment Activities Long short-term memory Deep learning Investment management Machine learning Business forecasting Artificial neural networks keyword: big data C14 C22 C45 C58 copyrightHolder:Oxford University Press copyrightYear:2026 deep learning dynamic multitask neural networks economic structure factor timing G10 G11 G12 G17 inLanguage:en machine learning publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag006 big data C14 C22 C45 C58 copyrightHolder:Oxford University Press copyrightYear:2026 deep learning dynamic multitask neural networks economic structure factor timing G10 G11 G12 G17 inLanguage:en machine learning publisher:Oxford University Press sameAs:https://dx.doi.org/10.1093/jjfinec/nbag006 ab: 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. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: N holdings: @attributes: islocal: N |
|---|