68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS

Selection of the securities for investment portfoliodesign is one of the most important optimizationproblems of the last century. For this, numerousstrategies and mathematical models have beenproposed. For instance, the passive investmentstrategy performs the tracking of market indices with theinten...

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Fecha de publicación:
2019
Institución:
Universidad Industrial de Santander
Repositorio:
Repositorio UIS
Idioma:
spa
OAI Identifier:
oai:noesis.uis.edu.co:20.500.14071/5497
Acceso en línea:
https://revistas.uis.edu.co/index.php/memoriasuis/article/view/10477
https://noesis.uis.edu.co/handle/20.500.14071/5497
Palabra clave:
Sparse Portfolio Optimization
Index Tracking
Low-Rank Approximation
Correlated Stocks
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openAccess
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Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
id UISANTADR2_9b7185bdab94b9db2eac6ee5a96ae3cf
oai_identifier_str oai:noesis.uis.edu.co:20.500.14071/5497
network_acronym_str UISANTADR2
network_name_str Repositorio UIS
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spelling Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)http://purl.org/coar/access_right/c_abf2info:eu-repo/semantics/openAccessAtribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)2019-01-012022-03-14T20:10:18Z2022-03-14T20:10:18Zhttps://revistas.uis.edu.co/index.php/memoriasuis/article/view/10477https://noesis.uis.edu.co/handle/20.500.14071/5497Selection of the securities for investment portfoliodesign is one of the most important optimizationproblems of the last century. For this, numerousstrategies and mathematical models have beenproposed. For instance, the passive investmentstrategy performs the tracking of market indices with theintention of reproducing its performance with anoptimized portfolio as described in [1]. This passive strategy is based on the advances shownby Palomar [2] who deals with the issue of designingsparse portfolios to efficiently reproduce the returns ofany index. Once the stocks have been selected, thefollowing step aims at dividing the investment capitalbetween these stocks in some efficient way. Thisstrategy has shown promising performance, however, itdoes not take into account the correlation between theselected stock returns, which is an important factor inthe efficient selection of the stocks, but a cointegrationbased approach. Therefore, the main objective of this work relies onformulating a mathematical model that allows to findhigh correlated stocks for the sparse portfolio design.Thus, it aims at modifying previous work to improve thequality results by taking into account the correlationbetween the stocks. In this manner, the proposed optimization problemincludes the nuclear norm over the market returnsmatrix multiplied by the desired variable weights, suchthat it is possible to apply some thresholding techniqueover the singular value decomposition of this resultingmatrix as presented in [3]. This allows to reduce its rankiteratively with the objective of obtaining its low-rankapproximation, which multiplied by the inverse returnsmatrix, results in the desired portfolio weightsapplication/pdfspaUniversidad Industrial de Santanderhttps://revistas.uis.edu.co/index.php/memoriasuis/article/view/10477/10354Memorias Institucionales UIS; Vol. 2 Núm. 1 (2020): Memorias Institucionales UISMemorias Institucionales UIS; Vol. 2 No. 1 (2020): Memorias Institucionales UISMemorias Institucionales UIS; v. 2 n. 1 (2020): Memorias Institucionales UIS2711-0567Sparse Portfolio OptimizationIndex TrackingLow-Rank ApproximationCorrelated Stocks68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKSinfo:eu-repo/semantics/articlehttp://purl.org/coar/version/c_970fb48d4fbd8a85http://purl.org/coar/resource_type/c_2df8fbb1Chacón Suárez, Javier Alexi20.500.14071/5497oai:noesis.uis.edu.co:20.500.14071/54972022-03-16 12:39:37.078metadata.onlyhttps://noesis.uis.edu.coDSpace at UISnoesis@uis.edu.co
dc.title.es-ES.fl_str_mv 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
title 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
spellingShingle 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
Sparse Portfolio Optimization
Index Tracking
Low-Rank Approximation
Correlated Stocks
title_short 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
title_full 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
title_fullStr 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
title_full_unstemmed 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
title_sort 68- #1168 SPARSE PORTFOLIOS FOR HIGHDIMENSIONAL FINANCIAL INDEX TRACKING WITH LOW-RANK MATRIX CONSTRAINT FOR STOCKS
dc.subject.es-ES.fl_str_mv Sparse Portfolio Optimization
Index Tracking
Low-Rank Approximation
Correlated Stocks
topic Sparse Portfolio Optimization
Index Tracking
Low-Rank Approximation
Correlated Stocks
description Selection of the securities for investment portfoliodesign is one of the most important optimizationproblems of the last century. For this, numerousstrategies and mathematical models have beenproposed. For instance, the passive investmentstrategy performs the tracking of market indices with theintention of reproducing its performance with anoptimized portfolio as described in [1]. This passive strategy is based on the advances shownby Palomar [2] who deals with the issue of designingsparse portfolios to efficiently reproduce the returns ofany index. Once the stocks have been selected, thefollowing step aims at dividing the investment capitalbetween these stocks in some efficient way. Thisstrategy has shown promising performance, however, itdoes not take into account the correlation between theselected stock returns, which is an important factor inthe efficient selection of the stocks, but a cointegrationbased approach. Therefore, the main objective of this work relies onformulating a mathematical model that allows to findhigh correlated stocks for the sparse portfolio design.Thus, it aims at modifying previous work to improve thequality results by taking into account the correlationbetween the stocks. In this manner, the proposed optimization problemincludes the nuclear norm over the market returnsmatrix multiplied by the desired variable weights, suchthat it is possible to apply some thresholding techniqueover the singular value decomposition of this resultingmatrix as presented in [3]. This allows to reduce its rankiteratively with the objective of obtaining its low-rankapproximation, which multiplied by the inverse returnsmatrix, results in the desired portfolio weights
publishDate 2019
dc.date.accessioned.none.fl_str_mv 2022-03-14T20:10:18Z
dc.date.available.none.fl_str_mv 2022-03-14T20:10:18Z
dc.date.none.fl_str_mv 2019-01-01
dc.type.none.fl_str_mv info:eu-repo/semantics/article
dc.type.coarversion.fl_str_mv http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.coar.fl_str_mv http://purl.org/coar/resource_type/c_2df8fbb1
dc.identifier.none.fl_str_mv https://revistas.uis.edu.co/index.php/memoriasuis/article/view/10477
dc.identifier.uri.none.fl_str_mv https://noesis.uis.edu.co/handle/20.500.14071/5497
url https://revistas.uis.edu.co/index.php/memoriasuis/article/view/10477
https://noesis.uis.edu.co/handle/20.500.14071/5497
dc.language.none.fl_str_mv spa
language spa
dc.relation.none.fl_str_mv https://revistas.uis.edu.co/index.php/memoriasuis/article/view/10477/10354
dc.rights.license.none.fl_str_mv Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
dc.rights.coar.none.fl_str_mv http://purl.org/coar/access_right/c_abf2
dc.rights.accessrights.none.fl_str_mv info:eu-repo/semantics/openAccess
dc.rights.creativecommons.none.fl_str_mv Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
rights_invalid_str_mv Attribution-NonCommercial 4.0 International (CC BY-NC 4.0)
http://purl.org/coar/access_right/c_abf2
Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.es-ES.fl_str_mv Universidad Industrial de Santander
dc.source.es-ES.fl_str_mv Memorias Institucionales UIS; Vol. 2 Núm. 1 (2020): Memorias Institucionales UIS
dc.source.en-US.fl_str_mv Memorias Institucionales UIS; Vol. 2 No. 1 (2020): Memorias Institucionales UIS
dc.source.pt-BR.fl_str_mv Memorias Institucionales UIS; v. 2 n. 1 (2020): Memorias Institucionales UIS
dc.source.none.fl_str_mv 2711-0567
institution Universidad Industrial de Santander
repository.name.fl_str_mv DSpace at UIS
repository.mail.fl_str_mv noesis@uis.edu.co
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