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.

翻译：我们提出时空动量策略，这是一类通过基于资产随时间变化的横截面动量特征进行交易，从而统一时间序列和横截面动量策略的模型。尽管时间序列和横截面动量策略旨在系统性地捕捉动量风险溢价，但这些策略被视为不同的实现方式，且未考虑不同资产的时间和横截面动量特征之间的同期关系与可预测性。我们采用不同复杂度的神经网络对时空动量进行建模，并证明仅含单一全连接层的简单神经网络能够通过学习整合资产的时间序列和横截面动量特征，同时为投资组合中的所有资产生成交易信号。基于46只活跃交易的美国股票和12个股指期货合约投资组合的回测表明，该模型在高达5-10个基点的高交易成本环境下仍能保持相对于基准的表现。特别地，我们发现该模型在结合最小绝对收缩与周转率正则化时，能够在不同交易成本场景下取得最佳表现。