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We introduce Spatio-Temporal Momentum strategies, a class of models thatunify both time-series and cross-sectional momentum strategies by tradingassets based on their cross-sectional momentum features over time. While bothtime-series and cross-sectional momentum strategies are designed tosystematically capture momentum risk premia, these strategies are regarded asdistinct implementations and do not consider the concurrent relationship andpredictability between temporal and cross-sectional momentum features ofdifferent assets. We model spatio-temporal momentum with neural networks ofvarying complexities and demonstrate that a simple neural network with only asingle fully connected layer learns to simultaneously generate trading signalsfor all assets in a portfolio by incorporating both their time-series andcross-sectional momentum features. Backtesting on portfolios of 46actively-traded US equities and 12 equity index futures contracts, wedemonstrate that the model is able to retain its performance over benchmarks inthe presence of high transaction costs of up to 5-10 basis points. Inparticular, we find that the model when coupled with least absolute shrinkageand turnover regularization results in the best performance over varioustransaction cost scenarios.

Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies