英文标题：
《Statistical Industry Classification》
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作者：
Zura Kakushadze and Willie Yu
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最新提交年份：
2018
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英文摘要：
  We give complete algorithms and source code for constructing (multilevel) statistical industry classifications, including methods for fixing the number of clusters at each level (and the number of levels). Under the hood there are clustering algorithms (e.g., k-means). However, what should we cluster? Correlations? Returns? The answer turns out to be neither and our backtests suggest that these details make a sizable difference. We also give an algorithm and source code for building \"hybrid\" industry classifications by improving off-the-shelf \"fundamental\" industry classifications by applying our statistical industry classification methods to them. The presentation is intended to be pedagogical and geared toward practical applications in quantitative trading.
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中文摘要：
我们给出了构建（多级）统计行业分类的完整算法和源代码，包括确定每个级别的集群数量（以及级别数量）的方法。在引擎盖下有聚类算法（例如，k-means）。然而，我们应该将什么进行集群？相关性？退货？答案是两者都不是，我们的回溯测试表明，这些细节产生了很大的不同。通过将我们的统计行业分类方法应用于现成的“基本”行业分类，我们还提供了构建“混合”行业分类的算法和源代码。本演示文稿旨在进行教学，并面向定量交易的实际应用。
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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：Statistical Finance        统计金融
分类描述：Statistical, econometric and econophysics analyses with applications to financial markets and economic data
统计、计量经济学和经济物理学分析及其在金融市场和经济数据中的应用
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