A Hidden Markov Model Approach to Information-Based Trading: Theory and Applications

Abstract

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 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 also characterize the features of information asymmetry and belief dispersion around earnings announcements and show that information-based trading prior to earnings announcements can significantly explain abnormal returns on announcement days and post-earnings-announcement drifts.

Keywords: Hidden Markov process, Information-based trading, Information asymmetry, Public information, Earnings announcement

JEL Classification: D82, G12, G14