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dc.contributor.authorBakke, Andreas Lillehagen
dc.contributor.authorNergaard, Erik Danielsen
dc.descriptionMaster's thesis Business Administration BE501 - University of Agder 2017nb_NO
dc.description.abstractDeMiguel, Garlappi, and Uppal (2009) conducted a study demonstrating that meanvariance optimized portfolios do not consistently outperform the naive diversi cation strategy in out-of-sample tests. This caused a heated debate and several studies claim to defend the value of mean-variance optimization. Kirby and Ostdiek (2012) developed two new methods of mean-variance portfolio optimization and demonstrated that these strategies show superior out-of-sample performance as compared to performance of the 1/N strategy. Several other papers demonstrated that the Global Minimum Variance portfolio outperforms the naive diversi cation. What all these papers have in common is that they measure the performance using the Sharpe ratio. Zakamulin (2017) argues that to display a convincing demonstration of the value of mean-variance optimization, one needs to show that the superior performance cannot be attributed to some known anomalies. In this thesis, we demonstrate that the strategies of Kirby and Ostdiek and the Global Minimum Variance strategy outperform the naive rule. We use several US datasets with an extended sample period and shorter estimation window. However, after accounting for three known anomalies, there is no longer any evidence of superior performance. Using similar data from the OSE, we also demonstrate that these strategies do not seem to work in Norway. inb_NO
dc.publisherUniversitetet i Agder ; University of Agdernb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleA Re-Examination of Performance of Optimized Portfoliosnb_NO
dc.typeMaster thesisnb_NO
dc.subject.nsiVDP::Samfunnsvitenskap: 200::Økonomi: 210::Samfunnsøkonomi: 212nb_NO
dc.source.pagenumberIV, 42nb_NO

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal