A disconnect exists between theories of and empirical research on accounting quality. Whereas the analytical literature has focused on static models of financial reporting (see Christensen and Feltham, 2003), the empirical literature has proposed measures of quality based on accounting numbers from multiple periods. The hidden Markov model allows us to extend the static reporting systems to one that is dynamic. By estimating properties of the reporting system in a multi-period setting, we bring theories closer to empirical research on earnings quality.
An HMM is a model of a stochastic process that can only be observed through a noisy signal released each period. HMMs have been applied to many fields of scientific inquiry, including speech processing (Rabiner and Juang, 1986) and biological sequence analysis (Durbin et al., 1998). In the social sciences, HMMs have been used to study non-stationary time series in finance and economics (e.g., Hamilton, 1989; Gray, 1996) and consumer behavior in marketing research (e.g., Netzer et al., 2008). For an overview of the applications of HMM, see MacDonald and Zucchini (1997).
The structure of our hidden Markov model with binary states and signals is illustrated below.
For a non-technical introduction to HMM and its estimation, see the excepts from the recent book “The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution”
by Gregory Zuckerman | 2019 | Penguin Publishing Group: HMM Simons Baum.
References:
Christensen, P., Feltham, G., 2003. Economics of Accounting — Volume I, Information in Markets. Springer Series in Accounting Scholarship. Heidelberg and New York: Springer.
Durbin, R., Eddy, S., Krogh, A., Mitchison, G., 1998. Biological Sequence Analysis: Probabilistic Models of Proteins and Nucleic Acids. Cambridge University Press.
Gray, S., 1996. Modeling the conditional distribution of interest rates as a regime-switching process. Journal of Financial Economics 42, 27–62.
Hamilton, J., 1989. A new approach to the economic analysis of nonstationary time series and the business cycle. Econometrica 57, 357–384.
Netzer, O., Lattin, J., Srinivasan, V., 2008. A hidden Markov model of customer relationship dynamics. Marketing Science 27, 185–204.
Rabiner, L., Juang, B., 1986. An introduction to hidden Markov models. IEEE ASSP Magazine 3, 4–16.