Anastasia A. Zakolyukina

How Common Are Intentional GAAP Violations? Estimates from a Dynamic Model

Using a sample of about 1,500 CEOs in the post-Sarbanes-Oxley Act of 2002 period, I estimate the extent of undetected intentional manipulation in earnings and managers’ manipulation costs using a dynamic finite-horizon structural model. The model features a risk-averse manager, who receives cash and equity compensation and maximizes his terminal wealth. I find that the expected cost of manipulation is low. The probability of detection is estimated to be 9%, and the average misstatement results in an 11% loss in the manager’s wealth if the manipulation is discovered. According to the estimated parameters, the implied fraction of manipulating CEOs is 66%, and the value-weighted bias in the stock price across manipulating CEOs is 15.5%. At the same time, the value-weighted bias in the stock price across all CEOs is 6%. Finally, I find that out-of-sample, the model-implied measure of intentional manipulation performs at least eight times better in terms of the root mean squared error than any of the five proxies for earnings management that have been used in the extant literature. ∗I thank my dissertation committee at the Stanford Graduate School of Business Anne Beyer, David Larcker (co-advisor), Maureen McNichols, Joseph Piotroski, and Peter Reiss (co-advisor) for their invaluable guidance and support. I acknowledge the University of Chicago Research Computing Center (RCC) for support of this study. I am grateful to John Johnson, Hakizumwami Birali Runesha (RCC), Andy Wettstein (RCC), Darren Young and, especially, Ravi Pillai and Robin Weiss (RCC) for their help with computing resources. I learned about computational issues from discussions with Che-Lin Su, Kenneth Judd and Stefan Wild. I extensively discussed the institutional details of restatements with Dennis Tanona and Olga Usvyatsky from Audit Analytics, Inc. I am grateful to Gaizka Ormazabal, Alan Jagolinzer, Christopher Armstrong, and Allan McCall for their insights into executive compensation data and to Mary Barth, Bill Beaver, Jean-Pierre Dube, Arthur Korteweg, Sergey Lobanov, John Lazarev, Pedro Gardete, Jesse Shapiro, Stephan Seiler, Ilya Strebulaev, Chad Syverson, Maria Ogneva, and Anita Rao for many helpful comments and suggestions. I would like to thank Carol Shabrami and Sarah Kervin for editorial help. I also benefited from the comments of the seminar participants at the Stanford Graduate School of Business, the Wharton School of Business, the Columbia Business School, the University of Chicago Booth School of Business, the Yale School of Management, the NYU Stern School of Business, the London Business School, and the 2013 FARS Midyear Meeting. I thank the Neubauer Family Foundation for financial support. Correspondence: aaz@chicagobooth.edu.

When Is Distress Risk Priced? Corporate Failure and the Business Cycle

This paper introduces a new measure of a firms exposure to systematic distress risk--the probability of a recession at the time of a firms failure. For stocks in the top quintile of the probability of failure, a median hedge portfolio based on our measure generates a positive risk premium of 5%-8% per annum. Our results differ from the previously documented distress-risk anomaly--a negative correlation between the probability of failure and stock returns. We argue that the probability of failure does not capture systematic distress risk well because it does not differentiate between failures occurring in recessions and expansions.In this paper, we use accounting fundamentals to measure systematic risk of distress. Our main testable prediction—that this risk increases with the probability of recessionary failure, P(R|F)—is based on a stylized model that guides our empirical analyses. We first apply the lasso method to select accounting fundamentals that can be combined into P(R|F) estimates. We then use the obtained estimates in asset-pricing tests. This approach successfully extracts systematic risk information from accounting data—we document a significant positive premium associated with P(R|F) estimates. The premium covaries with the news about the business cycle and aggregate failure rates. Additional tests underscore the importance of the “structure�? imposed through recessionary-failure-probability estimation. The “agnostic�? return predictor that relies only on past correlations between the same fundamental variables and returns exhibits markedly different properties.

Accounting Fundamentals and Systematic Risk: Corporate Failure over the Business Cycle

This paper introduces a new measure of a firms exposure to systematic distress risk--the probability of a recession at the time of a firms failure. For stocks in the top quintile of the probability of failure, a median hedge portfolio based on our measure generates a positive risk premium of 5%-8% per annum. Our results differ from the previously documented distress-risk anomaly--a negative correlation between the probability of failure and stock returns. We argue that the probability of failure does not capture systematic distress risk well because it does not differentiate between failures occurring in recessions and expansions.In this paper, we use accounting fundamentals to measure systematic risk of distress. Our main testable prediction—that this risk increases with the probability of recessionary failure, P(R|F)—is based on a stylized model that guides our empirical analyses. We first apply the lasso method to select accounting fundamentals that can be combined into P(R|F) estimates. We then use the obtained estimates in asset-pricing tests. This approach successfully extracts systematic risk information from accounting data—we document a significant positive premium associated with P(R|F) estimates. The premium covaries with the news about the business cycle and aggregate failure rates. Additional tests underscore the importance of the “structure�? imposed through recessionary-failure-probability estimation. The “agnostic�? return predictor that relies only on past correlations between the same fundamental variables and returns exhibits markedly different properties.

Corporate Failure and the Business Cycle: Measuring Systematic Risk

This paper introduces a new measure of a firms exposure to systematic distress risk--the probability of a recession at the time of a firms failure. For stocks in the top quintile of the probability of failure, a median hedge portfolio based on our measure generates a positive risk premium of 5%-8% per annum. Our results differ from the previously documented distress-risk anomaly--a negative correlation between the probability of failure and stock returns. We argue that the probability of failure does not capture systematic distress risk well because it does not differentiate between failures occurring in recessions and expansions.In this paper, we use accounting fundamentals to measure systematic risk of distress. Our main testable prediction—that this risk increases with the probability of recessionary failure, P(R|F)—is based on a stylized model that guides our empirical analyses. We first apply the lasso method to select accounting fundamentals that can be combined into P(R|F) estimates. We then use the obtained estimates in asset-pricing tests. This approach successfully extracts systematic risk information from accounting data—we document a significant positive premium associated with P(R|F) estimates. The premium covaries with the news about the business cycle and aggregate failure rates. Additional tests underscore the importance of the “structure�? imposed through recessionary-failure-probability estimation. The “agnostic�? return predictor that relies only on past correlations between the same fundamental variables and returns exhibits markedly different properties.