4.7 Article

How to Explain the Cross-Section of Equity Returns through Common Principal Components

期刊

MATHEMATICS
卷 9, 期 9, 页码 -

出版社

MDPI
DOI: 10.3390/math9091011

关键词

asset pricing; bootstrap; common principal component analysis; cross-sectional regression; factor models; time series

资金

  1. V Regional Plan for Scientific Research and Technological Innovation 2016-2020 of the Community of Madrid
  2. Universidad Carlos III de Madrid in the action of Excellence for University Professors

向作者/读者索取更多资源

In this paper, a procedure to obtain and test multifactor models based on statistical and financial factors is proposed, utilizing dimensionality reduction technique and block-bootstrap methodology to address factor selection and parameter significance issues. Results show that the multifactor model improves the Capital Asset Pricing Model in time-series regressions.
In this paper, we propose a procedure to obtain and test multifactor models based on statistical and financial factors. A major issue in the factor literature is to select the factors included in the model, as well as the construction of the portfolios. We deal with this matter using a dimensionality reduction technique designed to work with several groups of data called Common Principal Components. A block-bootstrap methodology is developed to assess the validity of the model and the significance of the parameters involved. Data come from Reuters, correspond to nearly 1250 EU companies, and span from October 2009 to October 2019. We also compare our bootstrap-based inferential results with those obtained via classical testing proposals. Methods under assessment are time-series regression and cross-sectional regression. The main findings indicate that the multifactor model proposed improves the Capital Asset Pricing Model with regard to the adjusted-R-2 in the time-series regressions. Cross-section regression results reveal that Market and a factor related to Momentum and mean of stocks' returns have positive risk premia for the analyzed period. Finally, we also observe that tests based on block-bootstrap statistics are more conservative with the null than classical procedures.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据