How to explain the cross-section of equity returns through Common Principal Components

Abstract

In this paper we propose a procedure to obtain and test multifactor models based on statistical and financial factors. In order to select the factors included in the model,as well as the construction of the portfolios, we use a multivariate technique 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-R2 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 analysed period. Finally, we also observe that tests based onblock-bootstrap statistics are more conservative with the none than classical procedures.