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股票配对的稳健正期望反馈交易

文章 arXiv papers · 作者: Atul Deshpande et al.
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总结

本文将稳健正期望定理从单只股票扩展到两只股票。原有结果研究如何组合专门设计的线性反馈控制器，一个做多、一个做空，从而在广泛的随机股价过程中产生期望值稳健为正的盈亏函数。

对于两只股票的情形，该定理依赖于对股票预期收益的假设，包括价格相关性条件和有界且非零的动量。已知参数的不确定性界限后，论文推导出控制器正反馈参数满足稳健正期望的充要条件。论文称，该结果涵盖单只股票的情形，也可解释为配对交易。摘录没有提供具体条件或实证检验，因此无法证明其在实盘市场中的表现。

核心观点

  • 该定理将单只股票的稳健正期望反馈交易扩展到股票配对。
  • 该策略结合一个做多和一个做空的线性反馈控制器。
  • 股票配对结果依赖于价格相关性和有界非零动量等假设。
  • 参数不确定性界限决定实现稳健正期望的条件。
  • 论文将结果与配对交易联系起来，但此处没有提供实证绩效证据。

标签

股票配对交易动量风险管理
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# A Generalization of the Robust Positive Expectation Theorem for Stock Trading via Feedback Control


# A Generalization of the Robust Positive Expectation Theorem for Stock Trading via Feedback Control









The starting point of this paper is the so-called Robust Positive Expectation (RPE) Theorem, a result which appears in literature in the context of Simultaneous Long-Short stock trading. This theorem states that using a combination of two specially-constructed linear feedback trading controllers, one long and one short, the expected value of the resulting gain-loss function is guaranteed to be robustly positive with respect to a large class of stochastic processes for the stock price. The main result of this paper is a generalization of this theorem. Whereas previous work applies to a single stock, in this paper, we consider a pair of stocks. To this end, we make two assumptions on their expected returns. The first assumption involves price correlation between the two stocks and the second involves a bounded non-zero momentum condition. With known uncertainty bounds on the parameters associated with these assumptions, our new version of the RPE Theorem provides necessary and sufficient conditions on the positive feedback parameter K of the controller under which robust positive expectation is assured. We also demonstrate that our result generalizes the one existing for the single-stock case. Finally, it is noted that our results also can be interpreted in the context of pairs trading.

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。