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图聚类用于美国股票多对统计套利

文章 arXiv papers · 作者: Adam Korniejczuk et al.
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总结

本研究提出了一个面向美国股票的多对统计套利框架，利用图聚类识别股票之间的关系。该框架结合量化方法、机器学习分类器和 Kelly 准则，旨在改善信号识别、风险管理以及对交易成本的适应能力。研究还测试了适用于日频交易的止盈和止损规则设定方法。

作者报告称，在假设的现实交易成本下，所有测试方法均优于各自对应的基准；表现最好的技术和参数组合取得了显著更优的绩效指标。作者也指出，结果对某些关键参数的变化较为敏感。本文没有说明股票、评估期间、具体基准、成本假设或敏感性范围，因此限制了独立评估，也无法据此判断测试设定之外的表现。

核心观点

  • 研究使用图聚类组织股票，以构建多对统计套利策略。
  • 该框架结合机器学习分类器、量化方法和 Kelly 准则。
  • 信号识别和风险控制包括优化日频止盈与止损规则。
  • 作者报告称，在假设的现实交易成本下，策略表现优于基准。
  • 报告的表现对某些参数变化较为敏感，但未提供相关细节。

标签

股票配对交易套利机器学习风险管理美国市场
全文
# Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market


# Statistical arbitrage in multi-pair trading strategy based on graph clustering algorithms in US equities market









The study seeks to develop an effective strategy based on the novel framework of statistical arbitrage based on graph clustering algorithms. Amalgamation of quantitative and machine learning methods, including the Kelly criterion, and an ensemble of machine learning classifiers have been used to improve risk-adjusted returns and increase immunity to transaction costs over existing approaches. The study seeks to provide an integrated approach to optimal signal detection and risk management. As a part of this approach, innovative ways of optimizing take profit and stop loss functions for daily frequency trading strategies have been proposed and tested. All of the tested approaches outperformed appropriate benchmarks. The best combinations of the techniques and parameters demonstrated significantly better performance metrics than the relevant benchmarks. The results have been obtained under the assumption of realistic transaction costs, but are sensitive to changes in some key parameters.

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。