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Higher-moment portfolios with practical constraints based on Polynomial goal programming

Carlenius, Jens Gautefall; Chen, Vis
Master thesis
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URI
http://hdl.handle.net/11250/2453527
Date
2017
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  • Master Thesis [3377]
Abstract
This thesis contributes to the field of portfolio selection by constructing and analyzing the

impact of incorporating higher-moments by Polynomial goal programming. We construct

the mean-variance-skewness and the mean-variance-skewness-kurtosis portfolio over a 20-year

horizon using 29 stocks from the S&P Global 1200-index. We examine the performance of

higher-moment portfolios in terms of return, risk and allocation, compared to two benchmark

portfolios; the traditional Markowitz portfolio and the global minimum variance portfolio.

Our findings suggest that an investor obtains a higher return and risk-adjusted return by

incorporating skewness into the mean-variance allocation framework. The mean-varianceskewness

portfolio can further be improved by a diversification constraint as a result of the

portfolio’s occasional concentrated allocations, while limiting turnover turns out to be relatively

detrimental for its performance. The results are less clear when both skewness and kurtosis

are incorporated into the asset allocation framework, as the mean-variance-skewness-kurtosis

portfolio is outperformed by the benchmark portfolios unless a turnover or a strong diversification

constraint is imposed. In general we find that higher-moment portfolios obtain more optimal

out-of-sample higher-moments at the cost of higher out-of-sample variance. The differences

between the out-of-sample moments are augmented by rebalancing the portfolios or by imposing

the strong diversification constraint.

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