论文标题
与许多股票共同发现基于因子的预期回流结构
Uncovering a factor-based expected return conditioning structure with Regression Trees jointly for many stocks
论文作者
论文摘要
鉴于简单线性回归三因素模型的成功和几乎普遍接受，在允许分析考虑因素和股票收益之间的非线性依赖性时，分析三个因素的信息内容是有趣的。为了更好地了解有关回归树设置中预期股票收益的基于因子的调理信息，使用5个主要美国公司的每日股票收益数据来证明对股票收益的分析。第一个发现是，在所有情况下（独奏和联合），最有用的因素始终是市场多余的回报因素。此外，讨论了三个主要问题：a）深度= 1树的平衡，因为它与股票回报分布的特性有关，b）深度= 1树的背后的机制在关节回归树中的平衡和c）c）在关节回归树中的主要库存。结果表明，仅高偏斜值无法解释所产生的树的分裂的不平衡，因为具有明显偏斜的库存可能会产生平衡的树木分裂。
Given the success and almost universal acceptance of the simple linear regression three-factor model, it is interesting to analyze the informational content of the three factors in explaining stock returns when the analysis is allowed to consider non-linear dependencies between factors and stock returns. In order to better understand factor-based conditioning information with respect to expected stock returns within a regression tree setting, the analysis of stock returns is demonstrated using daily stock return data for 5 major US corporations. The first finding is that in all cases (solo and joint) the most informative factor is always the market excess return factor. Further, three major issues are discussed: a) the balance of a depth=1 tree as it relates to properties of the stock return distribution, b) the mechanism behind depth=1 tree balance in a joint regression tree and c) the dominant stock in a joint regression tree. It is shown that high skew values alone cannot explain the imbalance of the resulting tree split as stocks with pronounced skew may produce balanced tree splits.
