摘要翻译：
在现代全球经济中，市场部门在资本的有效流动中发挥着关键作用。我们分析了现有的部门化启发式，并观察到最流行的--GICS（通知标准普尔500）和NAICS（由美国政府发布）--并不完全是由数量驱动的，而是看起来高度主观和植根于教条。在对资本结构无关性原理和莫迪里阿尼-米勒论域条件分析的基础上，我们假定公司的基本原理--尤其是莫迪里阿尼-米勒论域条件所特有的那些组成部分--将是公司经营的真实经济领域的最佳描述符。我们通过改变层次聚类算法的连接方法，生成一组潜在的候选学习扇区宇宙，以及从模型中导出的结果扇区的数量（范围从5到19)，从而得到总共60个候选学习扇区宇宙。然后我们引入reIndexer,一个回溯测试驱动的扇区宇宙评估研究工具，对我们学习到的扇区分类启发式产生的候选扇区宇宙进行排序。这一等级被用来确定风险调整收益最优学习部门宇宙是在CLINK（即完全联系）下产生的宇宙，有17个部门。用reIndexer对基准的GICS分类宇宙进行了测试，在回溯测试期间，在绝对投资组合价值和风险调整后的回报方面都优于最佳学习的部门宇宙。我们的结论是，我们的基本面驱动的学习行业分类启发式比现状分类启发式提供了一个更好的风险分散配置文件。
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英文标题：
《Learned Sectors: A fundamentals-driven sector reclassification project》
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作者：
Rukmal Weerawarana, Yiyi Zhu, Yuzhen He
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最新提交年份：
2019
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分类信息：

一级分类：Quantitative Finance        数量金融学
二级分类：General Finance        一般财务
分类描述：Development of general quantitative methodologies with applications in finance
通用定量方法的发展及其在金融中的应用
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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英文摘要：
  Market sectors play a key role in the efficient flow of capital through the modern Global economy. We analyze existing sectorization heuristics, and observe that the most popular - the GICS (which informs the S&P 500), and the NAICS (published by the U.S. Government) - are not entirely quantitatively driven, but rather appear to be highly subjective and rooted in dogma. Building on inferences from analysis of the capital structure irrelevance principle and the Modigliani-Miller theoretic universe conditions, we postulate that corporation fundamentals - particularly those components specific to the Modigliani-Miller universe conditions - would be optimal descriptors of the true economic domain of operation of a company. We generate a set of potential candidate learned sector universes by varying the linkage method of a hierarchical clustering algorithm, and the number of resulting sectors derived from the model (ranging from 5 to 19), resulting in a total of 60 candidate learned sector universes. We then introduce reIndexer, a backtest-driven sector universe evaluation research tool, to rank the candidate sector universes produced by our learned sector classification heuristic. This rank was utilized to identify the risk-adjusted return optimal learned sector universe as being the universe generated under CLINK (i.e. complete linkage), with 17 sectors. The optimal learned sector universe was tested against the benchmark GICS classification universe with reIndexer, outperforming on both absolute portfolio value, and risk-adjusted return over the backtest period. We conclude that our fundamentals-driven Learned Sector classification heuristic provides a superior risk-diversification profile than the status quo classification heuristic.
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PDF链接：
https://arxiv.org/pdf/1906.03935