A Hidden Markov Model Approach to Information-Based Trading: Theory and Applications
提出一种基于隐马尔可夫模型的方法来刻画市场隐藏状态，模拟和实证表明该方法能更好识别市场状态并生成动态信息交易度量，优于现有模型。
This paper develops a novel approach to information-based securities trading by characterizing the hidden state of the market, which varies following a Markov process. Extensive simulation demonstrates that the approach can successfully identify market states and generate dynamic measures of information-based trading that outperform prevailing models. A sample of 120 NYSE stocks further verifies that it can better depict trading dynamics. With this sample, we characterize the features of information asymmetry and belief dispersion around earnings announcements. The sample is also applied to the study of the co-movements of trading activities due to private information or disputable public information. Copyright © 2014 John Wiley & Sons, Ltd.