英文标题：
《Combining Alphas via Bounded Regression》
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作者：
Zura Kakushadze
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最新提交年份：
2015
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英文摘要：
We give an explicit algorithm and source code for combining alpha streams via bounded regression. In practical applications typically there is insufficient history to compute a sample covariance matrix (SCM) for a large number of alphas. To compute alpha allocation weights, one then resorts to (weighted) regression over SCM principal components. Regression often produces alpha weights with insufficient diversification and/or skewed distribution against, e.g., turnover. This can be rectified by imposing bounds on alpha weights within the regression procedure. Bounded regression can also be applied to stock and other asset portfolio construction. We discuss illustrative examples.
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中文摘要：
我们给出了通过有界回归组合alpha流的显式算法和源代码。在实际应用中，通常没有足够的历史来计算大量字母的样本协方差矩阵（SCM）。为了计算alpha分配权重，可以对SCM主成分进行（加权）回归。回归通常会产生阿尔法权重，其多样化和/或分布不均，例如营业额。这可以通过在回归过程中对alpha权重施加边界来纠正。有界回归也可以应用于股票和其他资产组合的构建。我们讨论示例。
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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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