This paper proposes a new method for financial portfolio optimisation based on reducing simultaneous asset shocks across a portfolio of assets. We adopt the new semi-metrics of \mbox{%DIFAUXCMD James2019 to determine the distance between two time series' structural breaks. We build on the optimal portfolio theory of \mbox{%DIFAUXCMD Markowitz1952 distance between asset structural breaks  , rather than portfolio variance , as our penalty function . Our experiments are promising: on synthetic data, they indicate that our proposed method does indeed diversify among time series with highly similar structural breaks  . On real data, experiments illustrate that our proposed optimisation method produces higher risk-adjusted returns than mean variance portfolio optimisation. The predictive distribution is superior in every measure  , producing a higher mean, lower standard deviation and less kurtosis. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns , during periods of highly similar structural breaks, such as a market crisis.
This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. We apply recently introduced semi-metrics between finite sets to determine the distance between time series' structural breaks. Then, we build on the classical portfolio optimization theory of Markowitz and use this distance between asset structural breaks for our penalty function , rather than portfolio variance . Our experiments are promising: on synthetic data, we show that our proposed method does indeed diversify among time series with highly similar structural breaks , and enjoys advantages over existing metrics between sets . On real data, experiments illustrate that our proposed optimization method produces higher risk-adjusted returns than mean-variance portfolio optimization. Moreover, the predictive distribution is superior in every measure analyzed , producing a higher mean, lower standard deviation and lower kurtosis. The main implication for this method in portfolio management is reducing simultaneous asset shocks and potentially sharp associated drawdowns during periods of highly similar structural breaks, such as a market crisis.
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