Faculty & Research

Anastasia A. Zakolyukina

Assistant Professor of Accounting and IBM Corporation Faculty Scholar

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5807 South Woodlawn Avenue
Chicago, IL 60637

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


2015 - 2016 Course Schedule

Number Name Quarter
30000 Financial Accounting 2016 (Spring)
30600 Workshop in Accounting Research 2015 (Fall)
30600 Workshop in Accounting Research 2016 (Winter)
30600 Workshop in Accounting Research 2016 (Spring)

Research Activities

Corporate governance and incentives, accounting manipulation, linguistic-analysis of disclosures, accounting-based risk assessment

REVISION: When Is Distress Risk Priced? Corporate Failure and the Business Cycle
Date Posted: Dec  30, 2015
Holding probability of failure constant, a firm’s expected return increases in the likelihood of failure in recession relative to expansion. Based on this observation, we develop a measure of firm’s systematic distress risk — the ratio of probabilities of failure in recession to failure in expansion. For stocks in the top quintile of distress, a median hedge portfolio based on our measure generates a return of 10%-12% per annum. This premium is confirmed in Fama-MacBeth regressions that control for the probability of failure. Our results differ from the distress anomaly — a negative correlation between the probability of failure and stock returns.

REVISION: Measuring Intentional GAAP Violations: A Structural Approach
Date Posted: Apr  26, 2014
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
Date Posted: Jan  28, 2012
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