Gregory Connor, Matthias Hagmann, Oliver Linton* (University of London) Abstract: This paper develops a new estimation procedure for characteristicbased factor models of stock returns. We treat the factor model as a weighted additive nonparametric regression model, with the factor returns serving as timevarying weights, and a set of univariate nonparametric functions relating security characteristic to the associated factor betas. We use a timeseries and crosssectional pooled weighted additive nonparametric regression methodology to simultaneously estimate the factor returns and characteristicbeta functions. Due to its greater efficiency our methodology allows for a larger number of factors than existing semiparametric methods. We apply the technique to the threefactor FamaFrench model, Carhart's fourfactor extension of it adding a momentum factor, and a fivefactor extension adding an ownvolatility factor.
