  Recent developments in deep learning techniques have motivated intensive
research in machine learning-aided stock trading strategies. However, since the
financial market has a highly non-stationary nature hindering the application
of typical data-hungry machine learning methods, leveraging financial inductive
biases is important to ensure better sample efficiency and robustness. In this
study, we propose a novel method of constructing a portfolio based on
predicting the distribution of a financial quantity called residual factors,
which is known to be generally useful for hedging the risk exposure to common
market factors. The key technical ingredients are twofold. First, we introduce
a computationally efficient extraction method for the residual information,
which can be easily combined with various prediction algorithms. Second, we
propose a novel neural network architecture that allows us to incorporate
widely acknowledged financial inductive biases such as amplitude invariance and
time-scale invariance. We demonstrate the efficacy of our method on U.S. and
Japanese stock market data. Through ablation experiments, we also verify that
each individual technique contributes to improving the performance of trading
strategies. We anticipate our techniques may have wide applications in various
financial problems.
