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用于FX预测与考虑执行的统计套利的图学习

文章 arXiv papers · 作者: Yoonsik Hong et al.
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总结

该方法结合外汇汇率预测与统计套利，并考虑观察价格到执行交易之间的延迟。第一阶段将汇率预测建模为离散时间时空图上的边级回归：货币是节点，汇率是边特征，利率则作为节点特征。这种结构旨在表示不同货币和利率之间的关系。

第二阶段将套利构建为随机优化问题。交易所及其影响关系组成的图支持风险调整后的投资组合决策，同时使用投影和 ReLU 根据预测模型的输出施加约束。作者报告称，预测的均方误差显著改善，套利的信息比率和 Sortino 比率均高于基准，并给出经验套利约束的证明。所提供的描述未说明数据、基准构造或评估期，因此仅凭这段文字无法评估报告的收益能否推广至更广泛的市场。

核心观点

  • 将FX预测建模为货币和交易所时空图上的边级回归。
  • 利率和汇率分别作为图中的节点特征和边特征。
  • 套利阶段明确考虑了观察价格与执行交易之间的时间延迟。
  • 随机优化框架使用投影和 ReLU 施加约束。
  • 作者报告称，与基准相比，预测误差有所改善，风险调整后指标也更高。

标签

外汇套利机器学习统计学交易执行
全文
# Graph Learning for Foreign Exchange Rate Prediction and Statistical Arbitrage


# Graph Learning for Foreign Exchange Rate Prediction and Statistical Arbitrage









We propose a two-step graph learning approach for foreign exchange statistical arbitrages (FXSAs), addressing two key gaps in prior studies: the absence of graph-learning methods for foreign exchange rate prediction (FXRP) that leverage multi-currency and currency-interest rate relationships, and the disregard of the time lag between price observation and trade execution. In the first step, to capture complex multi-currency and currency-interest rate relationships, we formulate FXRP as an edge-level regression problem on a discrete-time spatiotemporal graph. This graph consists of currencies as nodes and exchanges as edges, with interest rates and foreign exchange rates serving as node and edge features, respectively. We then introduce a graph-learning method that leverages the spatiotemporal graph to address the FXRP problem. In the second step, we present a stochastic optimization problem to exploit FXSAs while accounting for the observation-execution time lag. To address this problem, we propose a graph-learning method that enforces constraints through projection and ReLU, maximizes risk-adjusted return by leveraging a graph with exchanges as nodes and influence relationships as edges, and utilizes the predictions from the FXRP method for the constraint parameters and node features. Moreover, we prove that our FXSA method satisfies empirical arbitrage constraints. The experimental results demonstrate that our FXRP method yields statistically significant improvements in mean squared error, and that the FXSA method achieves a 61.89% higher information ratio and a 45.51% higher Sortino ratio than a benchmark. Our approach provides a novel perspective on FXRP and FXSA within the context of graph learning.

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。