End-to-End Policy Learning of a Statistical Arbitrage Autoencoder
  Architecture
Fabian Krause Jan-Peter Calliess
In Statistical Arbitrage (StatArb), classical mean reversion trading
strategies typically hinge on asset-pricing or PCA based models to identify the
mean of a synthetic asset. Once such a (linear) model is identified, a separate
mean reversion strategy is then devised to generate a trading signal. With a
view of generalising such an approach and turning it truly data-driven, we
study the utility of Autoencoder architectures in StatArb. As a first approach,
we employ a standard Autoencoder trained on US stock returns to derive trading
strategies based on the Ornstein-Uhlenbeck (OU) process. To further enhance
this model, we take a policy-learning approach and embed the Autoencoder
network into a neural network representation of a space of portfolio trading
policies. This integration outputs portfolio allocations directly and is
end-to-end trainable by backpropagation of the risk-adjusted returns of the
neural policy. Our findings demonstrate that this innovative end-to-end policy
learning approach not only simplifies the strategy development process, but
also yields superior gross returns over its competitors illustrating the
potential of end-to-end training over classical two-stage approaches.