Measuring Intentional Manipulation: A Structural Approach
Anastasia A. Zakolyukina
University of Chicago - Booth School of Business
March 30, 2013
Chicago Booth Research Paper No. 13-45
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.
Number of Pages in PDF File: 71
Keywords: Earnings manipulation, Executive compensation, Earnings restatements
JEL Classification: M41, G34, G38, K22, K42working papers series
Date posted: March 31, 2013 ; Last revised: February 17, 2014
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