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股票动量风险承担的数学框架

文章 arXiv papers · 作者: Ivan Cherednik
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总结

本文将动量风险承担视为对短期股票波动率的实时管理。所提出的系统把新闻影响股价的理论转化为不同投资期限的离散预期收益表，再据此指导自动化交易决策。文章还将这一方法与机器学习、随机过程、分数布朗运动以及市场之外的决策联系起来。

作者报告了成功的历史实验和实时实验，但该描述未提供样本细节、表现数据或比较方法。文中称，底层价格影响理论使用专门的数学函数，而机器学习流程仅作了简要说明。因此，这份摘要介绍的是一个拟议框架，信息不足以评估其假设、实施方式或稳健性。

核心观点

  • 该方法将动量风险承担视为对短期股票波动率的实时控制。
  • 新闻影响理论被离散化为涵盖多个投资期限的预期收益表。
  • 这些表格作为自动化股票交易系统的输入。
  • 作者报告了历史实验和实时实验，但此处未提供量化结果。

标签

股票动量波动率机器学习风险管理
全文
# Artificial intelligence approach to momentum risk-taking


# Artificial intelligence approach to momentum risk-taking









We propose a mathematical model of momentum risk-taking, which is essentially real-time risk management focused on short-term volatility of stock markets. Its implementation, our fully automated momentum equity trading system presented systematically, proved to be successful in extensive historical and real-time experiments. Momentum risk-taking is one of the key components of general decision-making, a challenge for artificial intelligence and machine learning with deep roots in cognitive science; its variants beyond stock markets are discussed. We begin with a new algebraic-type theory of news impact on share-prices, which describes well their power growth, periodicity, and the market phenomena like price targets and profit-taking. This theory generally requires Bessel and hypergeometric functions. Its discretization results in some tables of bids, which are basically expected returns for main investment horizons, the key in our trading system. The ML procedures we use are similar to those in neural networking. A preimage of our approach is the new contract card game provided at the end, a combination of bridge and poker. Relations to random processes and the fractional Brownian motion are outlined.

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。