  This paper studies deep learning methodologies for portfolio optimization in
the US equities market. We present a novel residual switching network that can
automatically sense changes in market regimes and switch between momentum and
reversal predictors accordingly. The residual switching network architecture
combines two separate residual networks (ResNets), namely a switching module
that learns stock market conditions, and the main module that learns momentum
and reversal predictors. We demonstrate that over-fitting noisy financial data
can be controlled with stacked residual blocks and further incorporating the
attention mechanism can enhance powerful predictive properties. Over the period
2008 to H12017, the residual switching network (Switching-ResNet) strategy
verified superior out-of-sample performance with an average annual Sharpe ratio
of 2.22, compared with an average annual Sharpe ratio of 0.81 for the ANN-based
strategy and 0.69 for the linear model.
