Lest we forget: learn from out-of-sample errors when optimizing portfolios
Autor(a) principal: | |
---|---|
Data de Publicação: | 2020 |
Outros Autores: | |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
Texto Completo: | http://hdl.handle.net/10400.14/35011 |
Resumo: | Portfolio optimization often struggles in realistic out-of-sample contexts. We de-construct this stylized fact, comparing historical forecasts of portfolio optimization inputs with subsequent out of sample values. We confirm that historical forecasts are imprecise guides of subsequent values but also find the resulting forecast errors are not entirely random. They have predictable patterns and can be partially reduced using their own history. Learning from past forecast errors to calibrate inputs (akin to empirical Bayesian learning) results in portfolio performance that reinforces the case for optimization. Furthermore, the portfolios achieve performance that meets expectations, a desirable yet elusive feature of optimization methods. |
id |
RCAP_4b62d655344a7e36699c158578640d6a |
---|---|
oai_identifier_str |
oai:repositorio.ucp.pt:10400.14/35011 |
network_acronym_str |
RCAP |
network_name_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository_id_str |
7160 |
spelling |
Lest we forget: learn from out-of-sample errors when optimizing portfoliosPortfolio optimizationRisk managementEstimation errorCovariance matrixPortfolio optimization often struggles in realistic out-of-sample contexts. We de-construct this stylized fact, comparing historical forecasts of portfolio optimization inputs with subsequent out of sample values. We confirm that historical forecasts are imprecise guides of subsequent values but also find the resulting forecast errors are not entirely random. They have predictable patterns and can be partially reduced using their own history. Learning from past forecast errors to calibrate inputs (akin to empirical Bayesian learning) results in portfolio performance that reinforces the case for optimization. Furthermore, the portfolios achieve performance that meets expectations, a desirable yet elusive feature of optimization methods.Veritati - Repositório Institucional da Universidade Católica PortuguesaBarroso, PedroSaxena, Konark2021-09-20T14:56:50Z2020-09-282020-09-28T00:00:00Zinfo:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10400.14/35011enginfo:eu-repo/semantics/openAccessreponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos)instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãoinstacron:RCAAP2023-07-12T17:40:31Zoai:repositorio.ucp.pt:10400.14/35011Portal AgregadorONGhttps://www.rcaap.pt/oai/openaireopendoar:71602024-03-19T18:28:23.544151Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informaçãofalse |
dc.title.none.fl_str_mv |
Lest we forget: learn from out-of-sample errors when optimizing portfolios |
title |
Lest we forget: learn from out-of-sample errors when optimizing portfolios |
spellingShingle |
Lest we forget: learn from out-of-sample errors when optimizing portfolios Barroso, Pedro Portfolio optimization Risk management Estimation error Covariance matrix |
title_short |
Lest we forget: learn from out-of-sample errors when optimizing portfolios |
title_full |
Lest we forget: learn from out-of-sample errors when optimizing portfolios |
title_fullStr |
Lest we forget: learn from out-of-sample errors when optimizing portfolios |
title_full_unstemmed |
Lest we forget: learn from out-of-sample errors when optimizing portfolios |
title_sort |
Lest we forget: learn from out-of-sample errors when optimizing portfolios |
author |
Barroso, Pedro |
author_facet |
Barroso, Pedro Saxena, Konark |
author_role |
author |
author2 |
Saxena, Konark |
author2_role |
author |
dc.contributor.none.fl_str_mv |
Veritati - Repositório Institucional da Universidade Católica Portuguesa |
dc.contributor.author.fl_str_mv |
Barroso, Pedro Saxena, Konark |
dc.subject.por.fl_str_mv |
Portfolio optimization Risk management Estimation error Covariance matrix |
topic |
Portfolio optimization Risk management Estimation error Covariance matrix |
description |
Portfolio optimization often struggles in realistic out-of-sample contexts. We de-construct this stylized fact, comparing historical forecasts of portfolio optimization inputs with subsequent out of sample values. We confirm that historical forecasts are imprecise guides of subsequent values but also find the resulting forecast errors are not entirely random. They have predictable patterns and can be partially reduced using their own history. Learning from past forecast errors to calibrate inputs (akin to empirical Bayesian learning) results in portfolio performance that reinforces the case for optimization. Furthermore, the portfolios achieve performance that meets expectations, a desirable yet elusive feature of optimization methods. |
publishDate |
2020 |
dc.date.none.fl_str_mv |
2020-09-28 2020-09-28T00:00:00Z 2021-09-20T14:56:50Z |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://hdl.handle.net/10400.14/35011 |
url |
http://hdl.handle.net/10400.14/35011 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
application/pdf |
dc.source.none.fl_str_mv |
reponame:Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) instname:Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação instacron:RCAAP |
instname_str |
Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
instacron_str |
RCAAP |
institution |
RCAAP |
reponame_str |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
collection |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) |
repository.name.fl_str_mv |
Repositório Científico de Acesso Aberto de Portugal (Repositórios Cientìficos) - Agência para a Sociedade do Conhecimento (UMIC) - FCT - Sociedade da Informação |
repository.mail.fl_str_mv |
|
_version_ |
1799132002520137728 |