Accounting theory as a Bayesian discipline
Author(s)
Bibliographic Information
Accounting theory as a Bayesian discipline
（Foundations and trends in accounting / editor-in-chief, Stefan J. Reichelstein, v. 13,
now Publishers, c2018
- : [pbk]
Available at / 2 libraries
-
No Libraries matched.
- Remove all filters.
Note
References: p. 255-274
Description and Table of Contents
Description
Introduces Bayesian theory and its role in statistical accounting information theory. The Bayesian statistical logic of probability, evidence and decision lies at the historical and modern center of accounting thought and research. It is not only the presumed rule of reasoning in analytical models of accounting disclosure, it is the default position for empiricists when hypothesizing about how the users of financial statements think. Bayesian logic comes to light throughout accounting research and is the soul of most strategic disclosure models. In addition, Bayesianism is similarly a large part of the stated and unstated motivation of empirical studies of how market prices and their implied costs of capital react to better financial disclosure.
The approach taken in this monograph is a Demski-like treatment of ""accounting numbers"" as ""signals"" rather than as ""measurements"". It should be of course that ""good"" measurements like ""quality earnings"" reports make generally better signals. However, to be useful for decision making under uncertainty, accounting measurements need to have more than established accounting measurement virtues. This monograph explains what those Bayesian information attributes are, where they come from in Bayesian theory, and how they apply in statistical accounting information theory.
Table of Contents
1. Introduction
2. Bayesianism Early in Accounting Theory
3. Survey of Bayesian Fundamentals
4. Case Study: Using All the Evidence
5. Is Accounting Bayesian or Frequentist?
6. Decision Support Role of Accounting Information
7. Demski's (1973) Impossibility Result
8. Does Information Reduce Uncertainty
9. How Information Combines
10. Ex Ante Effect of Greater Risk/Uncertainty1
1. Ex Post Decision Outcomes1
2. Information Uncertainty1
3. Conditioning Beliefs and the Cost of Capital1
4. Reliance on the Normal-Normal Model1
5. Bayesian Subjective Beta1
6. Other Bayesian Points of Interest1
7. Conclusion
Acknowledgements
References
by "Nielsen BookData"
