Selected Publications

We focus on the indirect effect of SEC comment letters by analyzing how companies change their risk factor disclosures after other leaders and peers in the industry receive a comment letter, even though the original company did not receive a letter itself. Not only does this spillover effect influence how much companies change their disclosures, but it also increases the amount of company-specific information they disclose. We confirm that these actions by the company (that did not receive a letter) improve its disclosures such that they are less likely to receive a future comment letter.
Contemporary Accounting Research, 2018.

We examine the likelihood of a client switching auditors when that client does not fit well with other clients of the same auditor. The most dissimilar clients are around 10% more likely to switch auditors, and will tend to change to an auditor with which they are more compatible. Following an auditor change, there is a quick alignment in the footnotes of that client with existing clients of the new auditor, followed by slower changes in unaudited (but reviewed) sections of the 10-K. There is mixed evidence as to whether a better client-firm alignment improves audit quality.
Journal of Accounting Research, 2016.

We measure year-over-year changes in companies’ 10-K MD&A disclosures, confirming these changes are more likely to appear following larger operational changes, but finding a decline in the magnitude of these changes and the stock market’s reaction to them. This latter result may indicate a decline in the usefulness of the MD&A for investors, coincident with the MD&A disclosures becoming longer and more boilerplate over time. We find no evidence that sell-side analysts use the MD&A in adjusting their forecasts, possibly due to having superior information sources.
Journal of Accounting Research, 2011.

Working Papers

Augmenting earlier measures of between-client similarity based on narrative text, I introduce a new measure for quantifying the similarity using quantitative data on the face of the financial statements. Clients having greater overlap with other clients of the auditor have audit fees that are lower than otherwise expected, which I interpret as evidence of increased efficiencies and lower risks within the audit process as the auditor is able to apply common knowledge, technology, and skills across related clients. The lower-fee effect is strongest in industries where the auditor has greater economic incentives (i.e., can better profit from the overlap). When the narrative disclosures and financial statements portray inconsistent views of the client, I find higher audit fees in situations that likely increase risk for the auditor and lower fees when risks decrease.
Working Paper, 2017.


In Spring 2019, I will be teaching the following courses at the University of Connecticut:

  • ACCT 2101: Principles of Managerial Accounting

I have previously taught the following courses:

  • ACG 5815: Accounting Regulation (University of Florida)
  • ACG 4111: Financial Accounting and Reporting 2 (University of Florida)
  • ACC 586: Shareholder Value Creation–Valuation and Financial Statement Analysis (Arizona State University)
  • ACG 2021: Intro to Financial Accounting (University of Florida)


I have around 20 years’ experience as a software developer, designing and building a wide variety of object-oriented libraries, databases, end-user applications, and back-end systems. Most of my work now is in direct support of my accounting research and data science activities.

Programming Languages

Python Especially Pandas, SciPy, and NumPy; around 75% of my machine learning/AI work is in Python, the rest is in R
R I use the tidyverse approach where possible; most of my R development is for statistical analysis and data visualization
SQL Extensive database development experience; in recent years pretty equally divided between traditional SQL backends and noSQL data stores
JavaScript Primarily to support web frontends
C For high-performance operations, less often for systems development
Perl Extensive use in earlier years, but less often now
XML, JSON Data representation
HTML, CSS Web development

General Data Science

Analytical Approach Machine learning and artificial intelligence; traditional statistics and econometrics
Data Transformation Processing, transforming, and merging large, semi-structured data; full extract-transform-load (ETL) process
Data Types Accounting, financial, operational, and natural language (NLP)
Statistical Tools Primarily R for traditional statistics; Python for machine learning; Stata occasionally
Data Visualization ggplot2 when possible; matplotlib when in Python

Development Environment

Operating System I feel most comfortable in a Linux environment, although I also use Windows regularly
IDE I use RStudio when coding R, vim for everything else


  • University of Connecticut, School of Business, Accounting Department, 2100 Hillside Road, Unit 1041, Storrs, CT 06269
  • BUSN 430; Email for appointment