  In this paper, we measure systematic risk with a new nonparametric factor
model, the neural network factor model. The suitable factors for systematic
risk can be naturally found by inserting daily returns on a wide range of
assets into the bottleneck network. The network-based model does not stick to a
probabilistic structure unlike parametric factor models, and it does not need
feature engineering because it selects notable features by itself. In addition,
we compare performance between our model and the existing models using 20-year
data of S&P 100 components. Although the new model can not outperform the best
ones among the parametric factor models due to limitations of the variational
inference, the estimation method used for this study, it is still noteworthy in
that it achieves the performance as best the comparable models could without
any prior knowledge.
