Partially egalitarian portfolio selection

IF 0.8 4区 管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE Operations Research Letters Pub Date : 2023-12-07 DOI:10.1016/j.orl.2023.11.008
Yiming Peng, Vadim Linetsky
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Abstract

We propose a new portfolio optimization framework, partially egalitarian portfolio selection (PEPS). Inspired by the celebrated LASSO regression and its recent variant partially egalitarian LASSO (PELASSO) developed in [1] in the context of the forecast combinations problem in econometrics in [1], we regularize the mean-variance portfolio optimization of Markowitz by adding two regularizing terms that essentially zero out portfolio weights of some of the assets in the portfolio and select and shrink portfolio weights of the remaining assets towards equal weights to hedge against parameter estimation risk. We solve our PEPS formulations by applying Gurobi 9.0 mixed integer optimization (MIO) solver that allow us to tackle large-scale portfolio problems. We test our PEPS portfolios against an array of classical portfolio optimization strategies on a number of datasets in the US equity markets. The PEPS portfolios exhibit the highest out-of-sample Sharpe ratios in all instances considered.

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部分平均主义的投资组合选择
我们提出了一种新的投资组合优化框架--部分平均投资组合选择(PEPS)。受著名的 LASSO 回归及其最新变体部分平均主义 LASSO(PELASSO)的启发,我们通过添加两个正则化项对马科维茨的均值方差投资组合优化进行了正则化,这两个正则化项实质上是将投资组合中部分资产的投资组合权重归零,并选择和缩减其余资产的投资组合权重,使其趋于等权重,以规避参数估计风险。我们采用 Gurobi 9.0 混合整数优化(MIO)求解器来求解 PEPS 公式,该求解器使我们能够解决大规模的投资组合问题。我们在美国股票市场的一些数据集上,将 PEPS 投资组合与一系列经典投资组合优化策略进行了对比测试。在所考虑的所有情况下,PEPS 投资组合都表现出最高的样本外夏普比率。
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来源期刊
Operations Research Letters
Operations Research Letters 管理科学-运筹学与管理科学
CiteScore
2.10
自引率
9.10%
发文量
111
审稿时长
83 days
期刊介绍: Operations Research Letters is committed to the rapid review and fast publication of short articles on all aspects of operations research and analytics. Apart from a limitation to eight journal pages, quality, originality, relevance and clarity are the only criteria for selecting the papers to be published. ORL covers the broad field of optimization, stochastic models and game theory. Specific areas of interest include networks, routing, location, queueing, scheduling, inventory, reliability, and financial engineering. We wish to explore interfaces with other fields such as life sciences and health care, artificial intelligence and machine learning, energy distribution, and computational social sciences and humanities. Our traditional strength is in methodology, including theory, modelling, algorithms and computational studies. We also welcome novel applications and concise literature reviews.
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