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:我们引入了Spatio-Temporal Momomentum战略,这是一系列模式,根据资产在一段时间内具有的跨部门势头特点,通过交易资产来统一时间序列和跨部门势头战略;虽然时间序列和跨部门势头战略旨在系统地捕捉势头风险溢价,但这些战略被视为不同的执行,不考虑不同资产在时间和跨部门势头特点之间的并行关系和可预测性;我们以复杂程度不同的神经网络来模拟瞬时势头,并表明一个只有单一的完全连接层的简单神经网络通过纳入时间序列和跨部门的势头特点,学习同时为投资组合中的所有资产生成贸易信号;关于46个积极交易的美国股票组合和12个股票指数期合同的后期测试,我们证明该模型在交易成本高达5-10个基点的高基点的情况下能够保持其业绩超过基准的基准;特别是,我们发现模型如果加上最小的绝对收缩和变换,就能在各种交易成本设想中取得最佳业绩。