Faculty & Research

Anastasia Zakolyukina

Anastasia A. Zakolyukina

Associate Professor of Accounting and IBM Corporation Faculty Scholar

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

 

2018 - 2019 Course Schedule

Number Name Quarter
30000 Financial Accounting 2018 (Fall)

Research Activities

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

REVISION: Information versus Investment
Date Posted: Nov  21, 2018
Firms' efficient long-term investment and accurate reporting of information about performance both serve crucial roles in the economy and capital markets. We argue quantitatively that the two goals are in direct conflict in the presence of realistic manager compensation contracts, which provide managers with incentives both to misreport financial statements and to distort their real investment choices. We build a dynamic structural model rich enough to capture a natural tradeoff between investment and information. The model matches a range of observable moments constructed from data on firm investment and periods of detected misreporting by firms. Counterfactuals show that regulations preventing misreporting do in fact incentivize managers to distort real investment, whose volatility rises. This excess volatility lowers firm value, suggesting a quantitatively meaningfully tradeoff.

REVISION: Accounting Fundamentals and Systematic Risk: Corporate Failure over the Business Cycle
Date Posted: Jul  20, 2018
In this paper, we use accounting fundamentals to measure systematic risk. We develop a statistical model that, based on accounting fundamentals, allows us to predict whether a firm’s failure will coincide with a recession. We demonstrate in a stylized model that the obtained probability of recessionary failure should reflect a firm’s systematic risk. The return-prediction tests suggest our approach successfully extracts systematic risk information from accounting data—the probability of recessionary-failure estimates are positively associated with future returns. We further show that requiring fundamental return predictors to also predict recessionary failure imposes a “structure” that is crucial for identifying risk-related return predictability. The “agnostic” return prediction that relies only on past correlations between the same fundamental variables and returns tends to detect mispricing.

New: How Common Are Intentional GAAP Violations? Estimates From a Dynamic Model
Date Posted: May  06, 2018
This paper uses data on detected misstatements — earnings restatements — and a dynamic model to estimate the extent of undetected misstatements that violate GAAP. The model features a CEO who can manipulate his firm's stock price by misstating earnings. I find the CEO's expected cost of misleading investors is low. The probability of detection over a five-year horizon is 13.91%, and the average misstatement, if detected, results in an 8.53% loss in the CEO's retirement wealth. The low expected cost implies a high fraction of CEOs who misstate earnings at least once at 60%, with 2%–22% of CEOs starting to misstate earnings in each year 2003–2010, inflation in stock prices across CEOs who misstate earnings at 2.02%, and inflation in stock prices across all CEOs at 0.77%. Wealthier CEOs manipulate less, and the average misstatement is larger in smaller firms.

REVISION: How Common Are Intentional GAAP Violations? Estimates from a Dynamic Model
Date Posted: Oct  16, 2017
This paper uses data on detected misstatements — earnings restatements — and a dynamic model to estimate the extent of undetected misstatements that violate GAAP. The model features a CEO who can manipulate his firm’s stock price by misstating earnings. I find the CEO’s expected cost of misleading investors is low. The probability of detection over a five-year horizon is 13.91%, and the average misstatement, if detected, results in an 8.53% loss in the CEO’s retirement wealth. The low expected cost implies a high fraction of CEOs who misstate earnings at least once at 60% with 2%–22% of CEOs starting to misstate earnings in each year 2003–2010, inflation in stock prices across CEOs who misstate earnings at 2.02%, and inflation in stock prices across all CEOs at 0.77%. Wealthier CEOs manipulate less, and the average misstatement is larger in smaller firms.

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: 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 ...