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端到端学习金融网络以进行期货动量交易

文章 arXiv papers · 作者: Xingyue Pu et al.
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总结

本文介绍 L2GMOM，一种用于网络动量的机器学习框架。网络动量利用资产之间的关系帮助预测未来收益。该框架同时学习金融网络和交易信号，而不是先构建网络，再单独优化投资组合。其神经网络架构源自算法展开，并被描述为具有可解释性。该框架可以采用不同的投资组合表现目标，包括负夏普比率。

对 64 个连续期货合约进行 20 年回测，报告夏普比率为 1.74，且盈利能力和风险控制有所改善。这些发现仅限于所报告的回测；本文没有进一步说明成本、实施方式、比较方法或样本外验证。文中还指出，传统的网络构建流程可能依赖昂贵的数据库和专业知识，但没有说明这些获取门槛如何影响报告结果。

核心观点

  • 网络动量利用资产之间的联系来辅助预测收益。
  • L2GMOM 将金融网络与动量策略的交易信号联合学习。
  • 其神经网络架构基于算法展开，并被描述为具有可解释性。
  • 该框架可使用负夏普比率等投资组合目标进行训练。
  • 报告的回测涵盖 64 个连续期货合约，历时 20 年，夏普比率为 1.74。

标签

期货动量机器学习投资组合构建回测风险管理
全文
# Learning to Learn Financial Networks for Optimising Momentum Strategies


# Learning to Learn Financial Networks for Optimising Momentum Strategies









Network momentum provides a novel type of risk premium, which exploits the interconnections among assets in a financial network to predict future returns. However, the current process of constructing financial networks relies heavily on expensive databases and financial expertise, limiting accessibility for small-sized and academic institutions. Furthermore, the traditional approach treats network construction and portfolio optimisation as separate tasks, potentially hindering optimal portfolio performance. To address these challenges, we propose L2GMOM, an end-to-end machine learning framework that simultaneously learns financial networks and optimises trading signals for network momentum strategies. The model of L2GMOM is a neural network with a highly interpretable forward propagation architecture, which is derived from algorithm unrolling. The L2GMOM is flexible and can be trained with diverse loss functions for portfolio performance, e.g. the negative Sharpe ratio. Backtesting on 64 continuous future contracts demonstrates a significant improvement in portfolio profitability and risk control, with a Sharpe ratio of 1.74 across a 20-year period.

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。