Deep Fundamental Factor Models.
Abstract
Deep fundamental factor models are developed to automatically capture non-linearity and interaction effects in factor modeling. Uncertainty quantification provides interpretability with interval estimation, ranking of factor importances and estimation of interaction effects. With no hidden layers we recover a linear factor model and for one or more hidden layers, uncertainty bands for the sensitivity to each input naturally arise from the...
Paper Details
Title
Deep Fundamental Factor Models.
Published Date
Mar 18, 2019
Journal
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