Anastasia Zakolyukina studies corporate governance and incentives, accounting manipulation, linguistic-analysis of disclosures, and accounting-based risk assessment. Her most recent work titled "Detecting Deceptive Discussions in Conference Calls' examines prediction of misstatements from the conference calls narratives of CEOs and CFOs. This study has been mentioned in The Economist, NPR, the Wall Street Journal, the New York Times, CBC, CNBC, and Bloomberg.
Zakolyukina earned her Ph.D. in Business Administration from Stanford Graduate School of Business. Additionally, she holds a M.A. in Economics from the New Economic School. Before pursuing graduate studies, Zakolyukina studied at the Udmurt State University where she earned dual degrees in Information Systems and Law.
Outside of academia, Zakolyukina has worked as an analyst at the Center for Economic and Financial Research in Moscow and was also a short-term consultant at the World Bank, International Bank for Reconstruction and Development
2014 - 2015 Course Schedule
Corporate governance and incentives, accounting manipulation, linguistic-analysis of disclosures, accounting-based risk assessment
REVISION: Measuring Intentional GAAP Violations: A Structural Approach
Based on a sample of about 1,400 CEOs, I estimate the extent of undetected GAAP violations 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 6%, and the average misstatement results in a 24% decrease in the manager's wealth if the manipulation is detected and the manger is terminated. Based on the estimated parameters, the implied fraction of CEOs who manipulate at least once during their tenure is 73%; the value-weighted bias in the stock price across manipulating CEOs is 6.97%, and the value-weighted bias in the stock price across all CEOs is 2.82%. Finally, I find that the model-implied measure performs at least six times better in terms of the root mean squared error out-of-sample than any of the five discretionary accruals measures that, in ...
REVISION: Detecting Deceptive Discussions in Conference Calls
We estimate classification models of deceptive discussions during quarterly earnings conference calls. Using data on subsequent financial restatements (and a set of criteria to identify especially serious accounting problems), we label each call as “truthful” or “deceptive”. Our models are developed with the word categories that have been shown by previous psychological and linguistic research to be related to deception. Using conservative statistical tests, we find that the out-of-sample perfor