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The authors 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. Although both time-series and cross-sectional momentum strategies are designed to systematically capture momentum risk premiums, 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. They 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. Back testing on portfolios of 46 actively traded US equities and 12 equity index futures contracts, they 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, they find that the model when coupled with least absolute shrinkage and turnover regularization results in the best performance over various transaction cost scenarios.

Original publication




Journal article


Journal of Financial Data Science

Publication Date





107 - 129