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利用动态贝叶斯网络估计基本面PE比率并用于交易

文章 arXiv papers · 作者: Haizhen Wang et al.
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总结

本研究使用动态贝叶斯网络对基本面市盈率进行估计并加以形式化，而这类估计通常基于专家判断。推断出的PE比率旨在支持人工投资决策，或作为自动化交易系统的输入。作者为所提网络推导了前向—后向推断和期望最大化程序，并将其结构与对波动率的行为金融学解释联系起来。

研究根据贝叶斯推断结果构建交易策略，并通过实验将其与标准投资基准进行比较。作者报告称，该策略在这些实验中持续表现优于基准。摘要未说明资产、基准定义、测试时期或交易成本假设，因此所报告结果无法证明其普遍表现或适合实盘交易。该方法既用于估计估值输入，也作为系统化决策的依据。

核心观点

  • 研究使用动态贝叶斯网络估计基本面PE比率。
  • 研究为所提模型推导了前向—后向推断和期望最大化方法。
  • 推断出的PE比率可为专家决策提供依据，或输入自动化交易系统。
  • 模型的解释与有关波动率的行为金融学证据相关联。
  • 实验报告称，使用推断出的PE比率构建的策略优于标准基准。

标签

股票机器学习统计学回测
全文
# Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment


# Stock Trading Using PE ratio: A Dynamic Bayesian Network Modeling on Behavioral Finance and Fundamental Investment









On a daily investment decision in a security market, the price earnings (PE) ratio is one of the most widely applied methods being used as a firm valuation tool by investment experts. Unfortunately, recent academic developments in financial econometrics and machine learning rarely look at this tool. In practice, fundamental PE ratios are often estimated only by subjective expert opinions. The purpose of this research is to formalize a process of fundamental PE estimation by employing advanced dynamic Bayesian network (DBN) methodology. The estimated PE ratio from our model can be used either as a information support for an expert to make investment decisions, or as an automatic trading system illustrated in experiments. Forward-backward inference and EM parameter estimation algorithms are derived with respect to the proposed DBN structure. Unlike existing works in literatures, the economic interpretation of our DBN model is well-justified by behavioral finance evidences of volatility. A simple but practical trading strategy is invented based on the result of Bayesian inference. Extensive experiments show that our trading strategy equipped with the inferenced PE ratios consistently outperforms standard investment benchmarks.

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。