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多任务学习构建多尺度时间序列动量投资组合

文章 arXiv papers · 作者: Joel Ong et al.
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总结

DeepUnifiedMom 是一种投资组合构建框架，将多个时间跨度的时间序列动量信号结合起来。它采用多任务学习和多门控专家混合架构构建统一投资组合，旨在捕捉较窄时间框架的策略可能遗漏的动量行为。

文档报告了涵盖股票指数、固定收益、外汇和大宗商品的回测，并称该框架扣除交易成本后优于基准模型。所提供的描述没有具体表现数据、基准定义、样本期或风险控制细节。因此难以评估其稳健性、对常见动量风险的敞口或实盘交易可行性；报告的结果应视为回测主张，而非未来收益的证据。

核心观点

  • DeepUnifiedMom 将多个时间跨度的时间序列动量信号整合到一个投资组合框架中。
  • 模型采用多任务学习和多门控专家混合架构。
  • 报告的回测涵盖股票指数、固定收益、外汇和大宗商品。
  • 描述称扣除交易成本后结果优于基准模型，但未提供表现细节。
  • 回测主张本身不能证明策略稳健或预期实盘表现。

标签

动量机器学习投资组合构建多资产回测
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# DeepUnifiedMom: Unified Time-series Momentum Portfolio Construction via Multi-Task Learning with Multi-Gate Mixture of Experts









This paper introduces DeepUnifiedMom, a deep learning framework that enhances portfolio management through a multi-task learning approach and a multi-gate mixture of experts. The essence of DeepUnifiedMom lies in its ability to create unified momentum portfolios that incorporate the dynamics of time series momentum across a spectrum of time frames, a feature often missing in traditional momentum strategies. Our comprehensive backtesting, encompassing diverse asset classes such as equity indexes, fixed income, foreign exchange, and commodities, demonstrates that DeepUnifiedMom consistently outperforms benchmark models, even after factoring in transaction costs. This superior performance underscores DeepUnifiedMom's capability to capture the full spectrum of momentum opportunities within financial markets. The findings highlight DeepUnifiedMom as an effective tool for practitioners looking to exploit the entire range of momentum opportunities. It offers a compelling solution for improving risk-adjusted returns and is a valuable strategy for navigating the complexities of portfolio management.

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。