This paper proposes a new method for financial portfolio optimization based on reducing simultaneous asset shocks across a collection of assets. This may be understood as an alternative approach to risk reduction in a portfolio based on a new mathematical quantity. First, 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 performs well relative to nine other commonly used options, producing the second-highest returns, the lowest volatility, and second-lowest drawdown. 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. Our method adds to a considerable literature of portfolio optimization techniques in econometrics and could complement these via portfolio averaging.

翻译：本文提出了一种基于减少资产集合内同步冲击的金融投资组合优化新方法。这可以理解为一种基于新数学量的投资组合风险降低替代方案。首先，我们应用近期引入的有限集间半度量，确定时间序列结构断点之间的距离。随后，我们基于马科维茨经典投资组合优化理论，将资产结构断点间的这一距离用于惩罚函数，而非使用投资组合方差。我们的实验结果令人满意：在合成数据上，所提方法确实能够对具有高度相似结构断点的时间序列进行多样化配置，并且相较于现有集合间度量具有优势。在真实数据上，实验表明，与九种常用方法相比，我们提出的优化方法表现良好：收益位列第二，波动率最低，回撤幅度排名第二。该方法在投资组合管理中的主要意义在于降低资产同步冲击，并可能在市场危机等结构断点高度相似的时期减少与之相关的剧烈回撤。我们的方法为计量经济学中大量投资组合优化技术文献提供了补充，并可通过投资组合平均化手段与其他方法形成互补。