Deep Fundamental Factor Models.

Published: Mar 18, 2019
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...
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Title
Deep Fundamental Factor Models.
Published Date
Mar 18, 2019
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