A modified CTGAN-plus-features-based method for optimal asset allocation

Simple item page
dc.contributor.authorNa, Jose-Manuel Pe
dc.contributor.authorSuarez, Fernando
dc.contributor.authorLarre, Omar
dc.contributor.authorRamirez, Domingo
dc.contributor.authorCifuentes, Arturo
dc.date.accessioned2025-01-20T17:06:28Z
dc.date.available2025-01-20T17:06:28Z
dc.date.issued2024
dc.description.abstractWe propose a new approach to portfolio optimization that utilizes a unique combination of synthetic data generation and a CVaR-constraint. We formulate the portfolio optimization problem as an asset allocation problem in which each asset class is accessed through a passive (index) fund. The asset-class weights are determined by solving an optimization problem which includes a CVaR-constraint. The optimization is carried out by means of a Modified CTGAN algorithm which incorporates features (contextual information) and is used to generate synthetic return scenarios, which, in turn, are fed into the optimization engine. For contextual information, we rely on several points along the U.S. Treasury yield curve. The merits of this approach are demonstrated with an example based on 10 asset classes (covering stocks, bonds, and commodities) over a fourteen-and-half-year period (January 2008-June 2022). We also show that the synthetic generation process is able to capture well the key characteristics of the original data, and the optimization scheme results in portfolios that exhibit satisfactory out-of-sample performance. We also show that this approach outperforms the conventional equal-weights (1/N) asset allocation strategy and other optimization formulations based on historical data only.
dc.fuente.origenWOS
dc.identifier.doi10.1080/14697688.2024.2329194
dc.identifier.eissn1469-7696
dc.identifier.issn1469-7688
dc.identifier.urihttps://doi.org/10.1080/14697688.2024.2329194
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/90788
dc.identifier.wosidWOS:001198444800001
dc.issue.numero3-4
dc.language.isoen
dc.pagina.final479
dc.pagina.inicio465
dc.revistaQuantitative finance
dc.rightsacceso restringido
dc.subjectAsset allocation
dc.subjectPortfolio optimization
dc.subjectPortfolio selection
dc.subjectSynthetic data
dc.subjectSynthetic returns
dc.subjectMachine learning
dc.subjectFeatures
dc.subjectContextual information
dc.subjectGAN
dc.subjectCTGAN
dc.subjectNeural networks
dc.subject.ods08 Decent Work and Economic Growth
dc.subject.odspa08 Trabajo decente y crecimiento económico
dc.titleA modified CTGAN-plus-features-based method for optimal asset allocation
dc.typeartículo
dc.volumen24
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
Files
Collections
Artículos de revistas