Integrating multiple sources of ordinal information in portfolio optimization

IF 4.4 3区 管理学 Q1 OPERATIONS RESEARCH & MANAGEMENT SCIENCE
Eranda Çela, Stephan Hafner, Roland Mestel, Ulrich Pferschy
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引用次数: 0

Abstract

In this contribution we consider multiple qualitative views specified as total orders of the expected asset returns and discuss two different approaches for incorporating this input in a mean–variance portfolio optimization model. In the robust optimization approach we first compute a posterior expectation of asset returns for every given total order by an extension of the Black–Litterman (BL) framework. Then these expected asset returns are considered as possible input scenarios for robust optimization variants of the mean–variance portfolio model (max–min robustness, min-max regret robustness and soft robustness). In the order aggregation approach rules from social choice theory (Borda, Footrule, Copeland, Best-of-k and MC4) are used to aggregate the individual total orders into a single “consensus total order”. Then expected asset returns are computed for this “consensus total order” by the extended BL framework mentioned above. Finally, these expectations are used as an input of the classical mean–variance optimization. Using data from EUROSTOXX 50 and S&P 100 we empirically compare the success of the two approaches in the context of portfolio performance analysis and observe that aggregating orders by social choice methods mostly outperforms robust optimization based methods for both data sets and for different combinations of confidence and quality levels of the views.

组合优化中多个有序信息源的集成
在这篇文章中,我们考虑了多个定性观点,指定为预期资产回报的总顺序,并讨论了两种不同的方法,将这一输入纳入均值-方差投资组合优化模型。在鲁棒优化方法中,我们首先通过扩展Black-Litterman (BL)框架计算每个给定总订单的资产回报的后检期望。然后将这些期望资产收益作为均值-方差组合模型(最大-最小鲁棒性、最小-最大遗憾鲁棒性和软鲁棒性)的鲁棒优化变量的可能输入场景。在订单聚合方法中,使用来自社会选择理论的规则(Borda, Footrule, Copeland, Best-of-k和MC4)将单个总订单聚合为单个“共识总订单”。然后,通过上面提到的扩展BL框架计算这个“一致总订单”的预期资产回报。最后,这些期望被用作经典均值-方差优化的输入。使用EUROSTOXX 50和标准普尔100的数据,我们在投资组合绩效分析的背景下经验地比较了两种方法的成功,并观察到社会选择方法的聚合订单在数据集和视图的信心和质量水平的不同组合中大多优于基于稳健优化的方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Annals of Operations Research
Annals of Operations Research 管理科学-运筹学与管理科学
CiteScore
7.90
自引率
16.70%
发文量
596
审稿时长
8.4 months
期刊介绍: The Annals of Operations Research publishes peer-reviewed original articles dealing with key aspects of operations research, including theory, practice, and computation. The journal publishes full-length research articles, short notes, expositions and surveys, reports on computational studies, and case studies that present new and innovative practical applications. In addition to regular issues, the journal publishes periodic special volumes that focus on defined fields of operations research, ranging from the highly theoretical to the algorithmic and the applied. These volumes have one or more Guest Editors who are responsible for collecting the papers and overseeing the refereeing process.
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