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神经网络联合学习时间序列与横截面动量

文章 arXiv papers · 作者: Wee Ling Tan et al.
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总结

本文介绍时空动量策略，将资产自身的动量历史与其相对其他资产的动量结合起来。该方法利用神经网络学习投资组合中的交易信号，把时间序列动量和横截面动量视为相互关联的输入，而非彼此独立的策略。

回测涵盖交易活跃的 US 股票和股指期货。报告称，单个全连接层在交易成本达到所测试的最高水平时，仍优于基准。就文中所述的各项成本情景而言，最小绝对收缩和换手率正则化表现最佳。证据仅限于所描述的投资组合和回测；文章未进一步说明评估时期、基准定义或实盘交易结果。

核心观点

  • 该策略结合每项资产的历史动量及其相对于投资组合中其他资产的动量。
  • 神经网络可以联合学习多项资产的交易信号。
  • 在所测试的投资组合中，报告称单个全连接层表现良好。
  • 在文中所述的交易成本情景下，最小绝对收缩与选择算子（LASSO）及换手率正则化均能改善结果。

标签

股票期货动量机器学习回测
全文
# Spatio-Temporal Momentum: Jointly Learning Time-Series and Cross-Sectional Strategies


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









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.

在遵守原作品许可的前提下，附作者信息全文展示。 许可协议: abstract CC0

此摘要由 Stratmill 研究智能体根据原文撰写，并非原文副本。