Essays on Optimal Portfolio and Resource Allocation

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dc.contributor.advisorTapon, Francis
dc.contributor.advisorAlexeev, Vitali
dc.contributor.advisorAnnen, Kurt
dc.contributor.authorPysarenko, Sergiy
dc.date.accessioned2018-08-31T19:10:09Z
dc.date.available2019-08-01T05:00:53Z
dc.date.copyright2018-08-31
dc.date.created2018-08-31
dc.date.issued2018-08-31
dc.degree.departmentDepartment of Economics and Financeen_US
dc.degree.grantorUniversity of Guelphen_US
dc.degree.nameDoctor of Philosophyen_US
dc.degree.programmeEconomicsen_US
dc.description.abstractThis paper proposes a micro-foundation for Contest-Success Functions (CSF) in a principal/agents setting. We characterize the principal's utility function so that the Ratio Form CSF results as the unique optimal allocation rule. The literature assumes a particular CSF without considering that the principal has her own preferences and that the optimal allocation, after having observed efforts, may differ from the allocation stipulated by the CSF. In this case, the CSF is not the principal's best response strategy; thus, the contest is not strongly credible. To ensure strong credibility, we consider sufficient conditions for a non-monotonic utility function, as well as for a larger family of monotonic utility functions compared to the literature. This paper proposes a novel approach of portfolio allocation. The fundamental indexing (FI) and Markowitz mean-variance optimization (MVO) approaches are complementary but have been considered separately in the portfolio choice literature. Using data on S&P 500 constituents, we evaluate a portfolio construction technique that utilizes the benefits of both approaches. The out-of-sample results of the blended portfolios attest to their superior performance compared to common benchmarks, and to portfolios constructed solely based on the FI or MVO methods. In pursuit of the optimal blend between the MVO and FI, we find that the ratio of market capitalization to GDP, being a leading indicator for an overpriced market, demonstrates remarkably advantageous properties. This paper proposes a Price-Adjusted Fundamental Index (PAFI) portfolio to improve on the Arnott Fundamental Index (FI) portfolio construction methodology. We adjust the Arnott fundamentals with a measure of under- or overpricing. We use data on S&P 500 constituents, and separate industries to compute Sharpe ratios that test the performance of the Arnott FI, the Global Minimum Variance (GMV), and PAFI portfolios against appropriate benchmarks. We test an alternative way to blend FI and GMV portfolios, based on Markowitz mean variance optimization. We find that PAFI and some of PAFI-based portfolios outperform FI and FI-based portfolios for Oil & Gas, Health Care, Technology and Telecommunications industries, and for defensive and sensitive super-industries.en_US
dc.identifier.urihttp://hdl.handle.net/10214/14155
dc.language.isoenen_US
dc.publisherUniversity of Guelphen_US
dc.rightsAttribution-NonCommercial-NoDerivs 2.5 Canada*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/2.5/ca/*
dc.subjectPortfolio Allocationen_US
dc.subjectFundamental Indexingen_US
dc.subjectPredictive Blendingen_US
dc.titleEssays on Optimal Portfolio and Resource Allocationen_US
dc.typeThesisen_US
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