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迁移学习用于加密货币横截面动量排序

文章 arXiv papers · 作者: Daniel Poh et al.
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总结

本文针对使用历史数据有限的资产训练横截面策略时可能出现的过拟合问题，提出融合编码器网络。该网络将源数据集上训练的编码器注意力模块所形成的表示，与一个专注于较小目标数据集的独立模块相结合。共享并融合信息旨在提升泛化能力；自注意力则在训练和推理时对金融工具之间的相互作用进行建模。

示例针对市值最大的十种加密货币构建动量策略并进行排序。报告结果在若干绩效指标上优于大多数参照模型，包括夏普比率达到经典动量策略的三倍，以及在不计交易成本时较最强基准提升约50%。论文称，计入加密货币交易成本后，该方法仍保持优势，但所提供的摘要没有进一步说明测试设计、稳健性分析或该应用场景之外的证据。

核心观点

  • 历史数据有限可能使复杂的横截面排序模型容易过拟合。
  • 融合编码器网络结合源数据集和目标数据集的表示，以迁移有用信息。
  • 自注意力使模型能够在推理时表示金融工具之间的相互作用。
  • 加密货币动量示例报告称，其表现优于基准，且计入交易成本后仍有优势。

标签

加密资产动量机器学习投资组合构建回测
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# Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity


# Transfer Ranking in Finance: Applications to Cross-Sectional Momentum with Data Scarcity









Cross-sectional strategies are a classical and popular trading style, with recent high performing variants incorporating sophisticated neural architectures. While these strategies have been applied successfully to data-rich settings involving mature assets with long histories, deploying them on instruments with limited samples generally produce over-fitted models with degraded performance. In this paper, we introduce Fused Encoder Networks -- a novel and hybrid parameter-sharing transfer ranking model. The model fuses information extracted using an encoder-attention module operated on a source dataset with a similar but separate module focused on a smaller target dataset of interest. This mitigates the issue of models with poor generalisability that are a consequence of training on scarce target data. Additionally, the self-attention mechanism enables interactions among instruments to be accounted for, not just at the loss level during model training, but also at inference time. Focusing on momentum applied to the top ten cryptocurrencies by market capitalisation as a demonstrative use-case, the Fused Encoder Networks outperforms the reference benchmarks on most performance measures, delivering a three-fold boost in the Sharpe ratio over classical momentum as well as an improvement of approximately 50% against the best benchmark model without transaction costs. It continues outperforming baselines even after accounting for the high transaction costs associated with trading cryptocurrencies.

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。