  We introduce Spatio-Temporal Momentum strategies, a class of models that
unify both time-series and cross-sectional momentum strategies by trading
assets based on their cross-sectional momentum features over time. While both
time-series and cross-sectional momentum strategies are designed to
systematically capture momentum risk premia, these strategies are regarded as
distinct implementations and do not consider the concurrent relationship and
predictability between temporal and cross-sectional momentum features of
different assets. We model spatio-temporal momentum with neural networks of
varying complexities and demonstrate that a simple neural network with only a
single fully connected layer learns to simultaneously generate trading signals
for all assets in a portfolio by incorporating both their time-series and
cross-sectional momentum features. Backtesting on portfolios of 46
actively-traded US equities and 12 equity index futures contracts, we
demonstrate that the model is able to retain its performance over benchmarks in
the presence of high transaction costs of up to 5-10 basis points. In
particular, we find that the model when coupled with least absolute shrinkage
and turnover regularization results in the best performance over various
transaction cost scenarios.

Portfolio Management;Machine Learning;Trading and Market Microstructure;Machine Learning