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用于股票统计套利的条件因子模型

文章 arXiv papers · 作者: Trent Spears et al.
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总结

本研究围绕由US股票组成的投资组合，基于条件因子模型构建统计套利方法。状态空间模型将收益视为因子值与潜在且随时间变化的因子风险溢价的含噪组合。在线滤波与预测用于估计这些风险溢价，进而得到预期收益，并与观测收益进行比较；较大的偏差被视为可能均值回归的候选信号。

作者使用状态空间模型的线性和非线性版本，评估了纳入交易成本的交易策略。他们报告了跨越 29 年历史的数据，并将表现与简单基准及其他已发表方法进行比较，认为该方法具有竞争力。他们还报告称，策略表现随时间有所减弱，尤其是近年如此，不过在其分析中仍具有经济吸引力。文档未在此提供资产选择、交易阈值、成本假设或风险控制的详情，因此仅凭这些结果不足以判断该策略能否迁移到其他市场或时期。

核心观点

  • 状态空间因子模型可以将股票收益率表示为对潜在因子风险溢价的含噪敞口。
  • 对随时间变化的溢价进行在线估算，使模型能够适应收益率行为的变化。
  • 观测收益率与滤波收益率之间的较大偏差被视为潜在的均值回归交易机会。
  • 所评估的策略纳入交易成本，并使用线性和非线性模型变体。
  • 报告称，该策略在长样本期内具有竞争力，但近年表现有所下降。

标签

股票均值回归因子投资统计学回测美国市场
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# On statistical arbitrage under a conditional factor model of equity returns


# On statistical arbitrage under a conditional factor model of equity returns









We consider a conditional factor model for a multivariate portfolio of United States equities in the context of analysing a statistical arbitrage trading strategy. A state space framework underlies the factor model whereby asset returns are assumed to be a noisy observation of a linear combination of factor values and latent factor risk premia. Filter and state prediction estimates for the risk premia are retrieved in an online way. Such estimates induce filtered asset returns that can be compared to measurement observations, with large deviations representing candidate mean reversion trades. Further, in that the risk premia are modelled as time-varying quantities, non-stationarity in returns is de facto captured. We study an empirical trading strategy respectful of transaction costs, and demonstrate performance over a long history of 29 years, for both a linear and a non-linear state space model. Our results show that the model is competitive relative to the results of other methods, including simple benchmarks and other cutting-edge approaches as published in the literature. Also of note, while strategy performance degradation is noticed through time -- especially for the most recent years -- the strategy continues to offer compelling economics, and has scope for further advancement.

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。