Portfolio Optimization via a Surrogate Risk Measure: Conditional Desirability Value at Risk (CDVaR)

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dc.authorid0000-0002-7085-0452
dc.authorscopusid15836690800
dc.contributor.authorBoduroğlu, İ. İlkay
dc.date.accessioned2022-05-11T14:15:56Z
dc.date.available2022-05-11T14:15:56Z
dc.date.issued2019
dc.departmentFakülteler, Çorlu Mühendislik Fakültesi, Bilgisayar Mühendisliği Bölümü
dc.description12th International Conference on Learning and Intelligent Optimization (LION) -- JUN 10-15, 2018 -- Kalamata, GREECE
dc.description.abstractA risk measure that specifies minimum capital requirements is the amount of cash that must be added to a portfolio to make its risk acceptable to regulators. The 2008 financial crisis highlighted the demise of the most widely used risk measure, Value-at-Risk. Unlike the Conditional VaR model of Rockafellar & Uryasev, VaR ignores the possibility of abnormal returns and is not even a coherent risk measure as defined by Pflug. Both VaR and CVaR portfolio optimizers use asset-price return histories. Our novelty here is introducing an annual Desirability Value (DV) for a company and using the annual differences of DVs in CVaR optimization, instead of simply utilizing annual stock-price returns. The DV of a company is the perpendicular distance from the fundamental position of that company to the best separating hyperplane H-0 that separates profitable companies from losers during training. Thus, we introduce both a novel coherent surrogate risk measure, Conditional-Desirability-Value-at-Risk (CDVaR) and a direction along which to reduce (downside) surrogate risk, the perpendicular to H-0. Since it is a surrogate measure, CDVaR optimization does not produce a cash amount as the risk measure. However, the associated CVaR (or VaR) is trivially computable. Our machine-learning-fundamental-analysis-based CDVaR portfolio optimization results are comparable to those of mainstream price-returns-based CVaR optimizers.
dc.description.sponsorshipUniv Florida, Ctr Appl Optimizat, Wilfrid Laurier Univ, Comp Algebra Res Grp Lab, Inst Advancement Phys & Math
dc.identifier.doi10.1007/978-3-030-05348-2_23
dc.identifier.endpage270
dc.identifier.isbn978-3-030-05348-2
dc.identifier.isbn978-3-030-05347-5
dc.identifier.issn0302-9743
dc.identifier.issn1611-3349
dc.identifier.scopus2-s2.0-85059948504
dc.identifier.scopusqualityQ3
dc.identifier.startpage257
dc.identifier.urihttps://doi.org/10.1007/978-3-030-05348-2_23
dc.identifier.urihttps://hdl.handle.net/20.500.11776/6123
dc.identifier.volume11353
dc.identifier.wosWOS:000611949200023
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.institutionauthorBoduroğlu, İ. İlkay
dc.language.isoen
dc.publisherSpringer International Publishing Ag
dc.relation.ispartofLearning and Intelligent Optimization, Lion 12
dc.relation.publicationcategoryKonferans Öğesi - Uluslararası - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.subjectPortfolio optimization
dc.subjectMachine learning
dc.subjectRisk management
dc.subjectDownside risk
dc.subjectConditional value at risk
dc.subjectLinear programming
dc.subjectFundamental analysis
dc.subjectInternational financial reporting standards
dc.subjectModel
dc.titlePortfolio Optimization via a Surrogate Risk Measure: Conditional Desirability Value at Risk (CDVaR)
dc.typeConference Object

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