英文标题：
《Factor Models for Alpha Streams》
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作者：
Zura Kakushadze
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最新提交年份：
2014
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英文摘要：
  We propose a framework for constructing factor models for alpha streams. Our motivation is threefold. 1) When the number of alphas is large, the sample covariance matrix is singular. 2) Its out-of-sample stability is challenging. 3) Optimization of investment allocation into alpha streams can be tractable for a factor model alpha covariance matrix. We discuss various risk factors for alphas such as: style risk factors; cluster risk factors based on alpha taxonomy; principal components; and also using the underlying tradables (stocks) as alpha risk factors, for which computing the factor loadings and factor covariance matrices does not involve any correlations with alphas, and their number is much larger than that of the relevant principal components. We draw insight from stock factor models, but also point out substantial differences.
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中文摘要：
我们提出了一个构建阿尔法流因子模型的框架。我们的动机有三方面。1） 当字母数较大时，样本协方差矩阵是奇异的。2） 其样品外稳定性具有挑战性。3） 对于因子模型阿尔法协方差矩阵，可以对阿尔法流中的投资分配进行优化。我们讨论了阿尔法的各种风险因素，如：风格风险因素；基于阿尔法分类法的集群风险因素；主成分；并且还将基础可交易资产（股票）用作阿尔法风险因素，对于阿尔法风险因素，因子负荷和因子协方差矩阵的计算不涉及与阿尔法的任何相关性，且其数量远大于相关主成分的数量。我们从股票因素模型中得出了见解，但也指出了实质性的差异。
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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：Quantitative Finance        数量金融学
二级分类：Risk Management        风险管理
分类描述：Measurement and management of financial risks in trading, banking, insurance, corporate and other applications
衡量和管理贸易、银行、保险、企业和其他应用中的金融风险
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