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Portfolio Inversion: Finding Market State Probabilities From Optimal Portfolios
KTH, School of Engineering Sciences (SCI).
KTH, School of Engineering Sciences (SCI).
2018 (English)Independent thesis Basic level (degree of Bachelor), 10 credits / 15 HE creditsStudent thesisAlternative title
Portföljinvertering : Sannolikheter för olika marknadstillstånd givet optimala portföljer (Swedish)
Abstract [en]

In this project, we aim to find a method for obtaining the factors in a bull/bear market factor model for asset return and variance, given an optimal portfolio. The proposed method was derived using the Karush-Kuhn-Tucker (KKT) conditions for optimal solutions to the convex Markowitz portfolio selection problem. For synthetic data where all necessary parameters were known exactly, the method could give bounds on the factors. The exact values of the factors were obtained when short selling was allowed, and in some instances when short selling was forbidden. The method was evaluated on real-world data with varying results, possibly due to estimation errors and invalid assumptions about the model of the investor.

Abstract [sv]

Målet med detta projekt var att utveckla en metod som givet en optimal portfölj returnerar sannolikheter för tjur-/björnmarknad. Dessa sannolikheter är faktorer i en faktormodell, vilken modellerar tillgångars förväntade avkastning samt variansen i deras avkastning. Den föreslagna metoden härleddes från Karush-Kuhn-Tucker-villkoren som uppfylls av optimala lösningar till det konvexa Markowitz-problemet. För syntetiska data där alla nödvändiga parametrar var kända exakt kunde metoden ge undre och övre gränser för faktorernas värden. Exakta värden för faktorerna erhölls i de fall då blankning var tillåten, samt i enskilda fall då blankning var förbjuden. Metoden tillämpades även på riktiga data utan entydiga resultat, möjligtvis till följd av skattningsfel samt ogiltiga antaganden om investerarens modell.

Place, publisher, year, edition, pages
2018. , p. 15
Series
TRITA-SCI-GRU ; 2018-135
National Category
Engineering and Technology
Identifiers
URN: urn:nbn:se:kth:diva-231743OAI: oai:DiVA.org:kth-231743DiVA, id: diva2:1229807
Supervisors
Examiners
Available from: 2018-07-02 Created: 2018-07-02 Last updated: 2018-07-02Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
  • harvard1
  • ieee
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  • en-US
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  • Other locale
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Output format
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