Portfolio management with higher moments: the cardinality impact
This paper extends the study of the cardinality impact on portfolio performance, from the traditional mean‐variance framework to more general frameworks that include higher moments. For each framework, we propose a biobjective model that allows the investor to explicitly analyze the efficient trade‐...
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Veröffentlicht in: | International transactions in operational research 2019-11, Vol.26 (6), p.2531-2560 |
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creator | Brito, Rui Pedro Sebastião, Hélder Godinho, Pedro |
description | This paper extends the study of the cardinality impact on portfolio performance, from the traditional mean‐variance framework to more general frameworks that include higher moments. For each framework, we propose a biobjective model that allows the investor to explicitly analyze the efficient trade‐off between expected utility and cardinality. We applied the proposed methodology to data from the Portuguese Stock Index (PSI20 index). The empirical results show that, in‐sample, the certainty equivalent and the Sharpe ratio increase with the cardinality level in all frameworks. The results also suggest that there are no performance gains, in‐sample, in terms of certainty equivalent, when higher moments are considered. Out of sample, the turnover increases up to a certain cardinality level, then decreases. For certain cardinality levels, there are gains in terms of out‐of‐sample certainty equivalent and Sharpe ratio, when skewness and kurtosis are considered. Finally, we check the robustness of these results in a large dataset from the EUROSTOXX50 index. |
doi_str_mv | 10.1111/itor.12404 |
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For each framework, we propose a biobjective model that allows the investor to explicitly analyze the efficient trade‐off between expected utility and cardinality. We applied the proposed methodology to data from the Portuguese Stock Index (PSI20 index). The empirical results show that, in‐sample, the certainty equivalent and the Sharpe ratio increase with the cardinality level in all frameworks. The results also suggest that there are no performance gains, in‐sample, in terms of certainty equivalent, when higher moments are considered. Out of sample, the turnover increases up to a certain cardinality level, then decreases. For certain cardinality levels, there are gains in terms of out‐of‐sample certainty equivalent and Sharpe ratio, when skewness and kurtosis are considered. Finally, we check the robustness of these results in a large dataset from the EUROSTOXX50 index.</description><identifier>ISSN: 0969-6016</identifier><identifier>EISSN: 1475-3995</identifier><identifier>DOI: 10.1111/itor.12404</identifier><language>eng</language><publisher>Oxford: Blackwell Publishing Ltd</publisher><subject>cardinality ; CRRA preferences ; derivative‐free optimization ; Empirical analysis ; Equivalence ; Expected utility ; expected utility maximization ; Kurtosis ; Operations research ; Portfolio management ; Portfolio performance ; PSI20 index</subject><ispartof>International transactions in operational research, 2019-11, Vol.26 (6), p.2531-2560</ispartof><rights>2017 The Authors. 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Finally, we check the robustness of these results in a large dataset from the EUROSTOXX50 index.</description><subject>cardinality</subject><subject>CRRA preferences</subject><subject>derivative‐free optimization</subject><subject>Empirical analysis</subject><subject>Equivalence</subject><subject>Expected utility</subject><subject>expected utility maximization</subject><subject>Kurtosis</subject><subject>Operations research</subject><subject>Portfolio management</subject><subject>Portfolio performance</subject><subject>PSI20 index</subject><issn>0969-6016</issn><issn>1475-3995</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2019</creationdate><recordtype>article</recordtype><recordid>eNp9kE1LAzEURYMoWKsbf0HAnTA13zNxpRQ_CoWK1HVI06STMjOpSUrpv3fquPZtHjzOu1wOALcYTXA_Dz6HOMGEIXYGRpiVvKBS8nMwQlLIQiAsLsFVSluEEOa4HIGnjxCzC40PsNWd3tjWdhkefK5h7Te1jbANp1N6hLm20Oi49p1ufD5C3-60ydfgwukm2Zu_PQZfry_L6XsxX7zNps_zwlBassIIaR2hrHJOCLPG0umyspwaqxmSmHHpTLVirnSOmBVhEktsV9wRKTRx3NExuBtydzF8723Kahv2sa-SFCFUclZhLnvqfqBMDClF69Qu-lbHo8JInQypkyH1a6iH8QAffGOP_5Bqtlx8Dj8_TcJpSw</recordid><startdate>201911</startdate><enddate>201911</enddate><creator>Brito, Rui Pedro</creator><creator>Sebastião, Hélder</creator><creator>Godinho, Pedro</creator><general>Blackwell Publishing Ltd</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>7TB</scope><scope>8FD</scope><scope>FR3</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>201911</creationdate><title>Portfolio management with higher moments: the cardinality impact</title><author>Brito, Rui Pedro ; Sebastião, Hélder ; Godinho, Pedro</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3374-c69ef2348ff66cd19fa78e53cea4091459fc8b4f7ff2cb249191eb5f296a2f5f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2019</creationdate><topic>cardinality</topic><topic>CRRA preferences</topic><topic>derivative‐free optimization</topic><topic>Empirical analysis</topic><topic>Equivalence</topic><topic>Expected utility</topic><topic>expected utility maximization</topic><topic>Kurtosis</topic><topic>Operations research</topic><topic>Portfolio management</topic><topic>Portfolio performance</topic><topic>PSI20 index</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Brito, Rui Pedro</creatorcontrib><creatorcontrib>Sebastião, Hélder</creatorcontrib><creatorcontrib>Godinho, Pedro</creatorcontrib><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Mechanical & Transportation Engineering Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>International transactions in operational research</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Brito, Rui Pedro</au><au>Sebastião, Hélder</au><au>Godinho, Pedro</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Portfolio management with higher moments: the cardinality impact</atitle><jtitle>International transactions in operational research</jtitle><date>2019-11</date><risdate>2019</risdate><volume>26</volume><issue>6</issue><spage>2531</spage><epage>2560</epage><pages>2531-2560</pages><issn>0969-6016</issn><eissn>1475-3995</eissn><abstract>This paper extends the study of the cardinality impact on portfolio performance, from the traditional mean‐variance framework to more general frameworks that include higher moments. For each framework, we propose a biobjective model that allows the investor to explicitly analyze the efficient trade‐off between expected utility and cardinality. We applied the proposed methodology to data from the Portuguese Stock Index (PSI20 index). The empirical results show that, in‐sample, the certainty equivalent and the Sharpe ratio increase with the cardinality level in all frameworks. The results also suggest that there are no performance gains, in‐sample, in terms of certainty equivalent, when higher moments are considered. Out of sample, the turnover increases up to a certain cardinality level, then decreases. For certain cardinality levels, there are gains in terms of out‐of‐sample certainty equivalent and Sharpe ratio, when skewness and kurtosis are considered. 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subjects | cardinality CRRA preferences derivative‐free optimization Empirical analysis Equivalence Expected utility expected utility maximization Kurtosis Operations research Portfolio management Portfolio performance PSI20 index |
title | Portfolio management with higher moments: the cardinality impact |
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