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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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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中文摘要：
市场部门在资本通过现代全球经济的有效流动中发挥着关键作用。我们分析了现有的部门划分启发式方法，并观察到最流行的——GIC（通知标准普尔500指数）和NAIC（由美国政府发布）——并不是完全由数量驱动的，而是高度主观的，植根于教条。基于对资本结构无关性原则和莫迪利安尼-米勒理论宇宙条件的分析得出的推论，我们假设，公司基本面，尤其是莫迪利安尼-米勒宇宙条件特定的组成部分，将是公司真实经济运营领域的最佳描述符。我们通过改变层次聚类算法的链接方法，以及从模型得出的结果扇区数（从5到19），生成一组潜在的候选学习扇区通用，从而得到总共60个候选学习扇区通用。然后，我们引入reIndexer（一种回溯测试驱动的行业领域评估研究工具），对我们学习的行业分类启发式产生的候选行业领域进行排名。该排名用于确定风险调整后收益最优学习部门范围，即在CLINK（即完全关联）下产生的范围，共有17个部门。使用reIndexer对最佳学习行业范围进行了测试，与基准GICS分类范围进行了对比，在回溯测试期间，绝对投资组合价值和风险调整回报均表现优异。我们得出的结论是，我们的基本面驱动的学习型行业分类启发式方法比现状分类启发式方法提供了更好的风险分散情况。
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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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