Faculty & Research

Anastasia A. Zakolyukina

Assistant Professor of Accounting

Phone :
1-773-834-4838
Address :
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

 

2016 - 2017 Course Schedule

Number Name Quarter
30000 Financial Accounting 2017 (Winter)

Research Activities

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

REVISION: CEO Personality and Firm Policies
Date Posted: Jul  13, 2016
Based on two samples of high quality personality data for chief executive officers (CEOs), we use linguistic features extracted from conferences calls and statistical learning techniques to develop a measure of CEO personality in terms of the Big Five traits: agreeableness, conscientiousness, extraversion, neuroticism, and openness to experience. These personality measures have strong out-of-sample predictive performance and are stable over time. Our measures of the Big Five personality traits are associated with financing choices, investment choices and firm operating performance.

REVISION: Corporate Failure and the Business Cycle: Measuring Systematic Risk
Date Posted: Jun  24, 2016
Using firm fundamentals, we develop a forward-looking measure of systematic risk associated with corporate failure. Our measure is consistent with standard asset pricing theory and is equal to the ratio of probabilities of failure in recession to failure in expansion. We validate the measure using two predictions from a stylized model. First, for a given probability of failure, expected returns should increase in our measure. Second, the resulting spread in returns should be larger for more distressed stocks. We find support for both predictions. For stocks in the top quintile of distress, a median hedge portfolio based on our measure generates 10%-12% per annum, suggesting that our approach successfully extracts risk information from accounting data. Our findings have important implications for the literature on distress risk that, despite compelling theoretical arguments, finds little evidence of a distress risk premium. Instead, it documents the distress anomaly — a negative ...

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 ...