基于Dirichlet权重的通用投资组合因子预测

IF 0.9 4区管理学 Q4 OPERATIONS RESEARCH & MANAGEMENT SCIENCE

Operations Research Letters Pub Date : 2025-09-03 DOI:10.1016/j.orl.2025.107360

Purushottam Parthasarathy , Avinash Bhardwaj , Manjesh K. Hanawal

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引用次数: 0

摘要

我们重新审视在线投资组合分配问题，寻求最大化长期投资组合增长率。我们提出了一种新的技术，修改狄利克雷分布的集中参数，将截面收益预测纳入到通用投资组合中。分析证明，在一定条件下，因子狄利克雷投资组合产生的财富优于均匀狄利克雷投资组合产生的财富。我们用股票市场的实证研究证实了我们的分析结果。

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Factor based forecasts in universal portfolios via Dirichlet weights

We revisit the online portfolio allocation problem that seeks to maximize the long term portfolio growth rate. We propose a new technique that modifies the concentration parameter of the Dirichlet distribution to incorporate cross-sectional return forecasts into the universal portfolio. We analytically establish that under certain conditions, the wealth generated by the factor Dirichlet portfolio dominates that generated by its uniform Dirichlet counterpart. We corroborate our analytical results with empirical studies on equity markets.

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来源期刊

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.