Characteristic-Sorted Portfolios: Estimation and Inference
Characteristic-Sorted Portfolios: Estimation and Inference
复制标题
DOI：
10.1162/rest_a_00883
复制
发表时间：
2018-09
影响因子：
8
通讯作者：
M. D. Cattaneo;Richard K. Crump;M. Farrell;E. Schaumburg
中科院分区：
文献类型：
--
作者：
M. D. Cattaneo;Richard K. Crump;M. Farrell;E. Schaumburg
摘要 投资组合排序在实证金融文献中无处不在，它被广泛用于识别定价异常。尽管它很受欢迎，但很少有人关注该过程的统计特性。我们通过将其转换为非参数估计器来开发投资组合排序的通用框架。我们提出了有效的渐近推理方法和估计器的有效均方误差扩展，从而得出投资组合数量的最佳选择。在实际设置中，最佳选择可能比标准选择五个或十个大得多。为了说明我们结果的相关性，我们重新审视大小和动量异常。
Abstract Portfolio sorting is ubiquitous in the empirical finance literature, where it has been widely used to identify pricing anomalies. Despite its popularity, little attention has been paid to the statistical properties of the procedure. We develop a general framework for portfolio sorting by casting it as a nonparametric estimator. We present valid asymptotic inference methods and a valid mean square error expansion of the estimator leading to an optimal choice for the number of portfolios. In practical settings, the optimal choice may be much larger than the standard choices of five or ten. To illustrate the relevance of our results, we revisit the size and momentum anomalies.