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

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Show simple item record Cueto, José Manuel Grané Chávez, Aurea Cascos Fernández, Ignacio
dc.contributor.editor Universidad Carlos III de Madrid. Departamento de Estadística 2021-04-05T10:52:03Z 2021-04-05T10:52:03Z 2021-04-05
dc.identifier.issn 2387-0303
dc.description.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.
dc.language.iso eng
dc.relation.ispartofseries Working paper Statistics and Econometrics
dc.relation.ispartofseries 21-04
dc.rights Atribución-NoComercial-SinDerivadas 3.0 España
dc.subject.other Asset Pricing
dc.subject.other Bootstrap
dc.subject.other Common Principal Component Analysis
dc.subject.other Cross-Sectional Regression
dc.subject.other Factor Models
dc.subject.other Time Series
dc.title How to explain the cross-section of equity returns through Common Principal Components
dc.type workingPaper
dc.identifier.uxxi DT/0000001891
dc.affiliation.dpto UC3M. Departamento de Estadística
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