摘要翻译：
讨论了投资组合优化中的估计误差问题，当投资组合规模N和样本规模T达到无穷大时，它们的比值是固定的。估计误差强烈地依赖于比值n/t，并在该参数的一个临界值处发散。这种分歧是一种算法相变的表现，它伴随着一些临界现象，表现出普遍性。由于大量多维回归和建模问题的结构与投资组合优化非常相似，上述观察的范围远远超出了金融学，涵盖了运筹学、机器学习、生物信息学、医学、经济学和技术等领域的大量问题。
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英文标题：
《Divergent estimation error in portfolio optimization and in linear
  regression》
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作者：
Imre Kondor, Istvan Varga-Haszonits
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最新提交年份：
2007
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：Portfolio Management        项目组合管理
分类描述：Security selection and optimization, capital allocation, investment strategies and performance measurement
证券选择与优化、资本配置、投资策略与绩效评价
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一级分类：Physics        物理学
二级分类：Physics and Society        物理学与社会
分类描述：Structure, dynamics and collective behavior of societies and groups (human or otherwise). Quantitative analysis of social networks and other complex networks. Physics and engineering of infrastructure and systems of broad societal impact (e.g., energy grids, transportation networks).
社会和团体（人类或其他）的结构、动态和集体行为。社会网络和其他复杂网络的定量分析。具有广泛社会影响的基础设施和系统（如能源网、运输网络）的物理和工程。
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英文摘要：
  The problem of estimation error in portfolio optimization is discussed, in the limit where the portfolio size N and the sample size T go to infinity such that their ratio is fixed. The estimation error strongly depends on the ratio N/T and diverges for a critical value of this parameter. This divergence is the manifestation of an algorithmic phase transition, it is accompanied by a number of critical phenomena, and displays universality. As the structure of a large number of multidimensional regression and modelling problems is very similar to portfolio optimization, the scope of the above observations extends far beyond finance, and covers a large number of problems in operations research, machine learning, bioinformatics, medical science, economics, and technology.
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PDF链接：
https://arxiv.org/pdf/0710.1855