Artificial Intelligence & Text Analytics
Applying large language models, natural language processing, and machine learning to corporate disclosures, regulatory documents, and other large collections of unstructured text.
BYU Marriott School of Business
Zackery Fox, PhD
Assistant Professor of Accounting · Brigham Young University
Andersen Foundation Fellowship
Accounting Research Artificial Intelligence Data Analytics
My work connects accounting research, business decisions, and advanced data analytics. I study how tax policy and corporate disclosures influence firm behavior, using artificial intelligence, machine learning, and large-scale empirical analysis to draw insights from complex business information.
About
I am an assistant professor in the School of Accountancy at Brigham Young University's Marriott School of Business, where I hold the Andersen Foundation Fellowship. I specialize in corporate taxation and study how tax policy, tax planning, and tax enforcement shape business decisions.
My tax research spans international tax incentives, enforcement uncertainty, and the connections between financial reporting and IRS scrutiny. I examine how these issues influence where firms invest, locate jobs, and allocate resources. I also study how tax rules affect executive incentives and how firms use disclosure to learn from stock prices. My work has appeared in The Accounting Review, Review of Accounting Studies, and Contemporary Accounting Research.
I apply artificial intelligence, natural language processing, and advanced statistical methods to complex financial, business, and regulatory data. I welcome opportunities to bring this research perspective to collaborations with businesses and other organizations.
I earned my bachelor's degree at Utah Tech University, my master's degree at BYU, and my doctorate at the University of Oregon, all in accounting. At Oregon, I received the Doctoral Fellowship for Outstanding Doctoral Student. Before joining BYU in 2022, I was on the faculty at Texas A&M University's Mays School of Business.
My dissertation received the American Accounting Association's 2025 Competitive Manuscript Award.
AI & Data Analytics
My research increasingly applies artificial intelligence and advanced analytics to complex business and regulatory data. These methods make it possible to extract information and address questions that traditional datasets and approaches cannot easily capture.
Large-Scale Data Development
Building, integrating, and validating complex datasets to create new measures, identify patterns, and extract information from sources that are difficult to analyze.
Applied Statistical & Economic Analysis
Using econometric modeling, causal inference, and advanced quantitative methods, such as machine learning, to evaluate business decisions, regulatory changes, and economic outcomes.
Research applications
Examples from ongoing research and analytical method development.
Ongoing research
AI-driven analysis of corporate filings
I use SEC 10-K filings to study corporations' multinational operations. Large-scale text processing and language models help identify geographic references and classify business activities, turning extensive disclosures into structured data and new measures of corporate multinationality.
Ongoing research
Classification of regulatory documents
I use machine learning and language models to classify IRS private letter rulings and related documents. This work identifies tax topics and subtopics, assesses the relevance of regulatory developments, and explores their connections to publicly traded businesses.
Analytical methods developed
Data-driven decision support
I have developed analytical approaches that combine geographic, economic, and administrative data to examine humanitarian aid allocation across municipalities after natural disasters. This research uses predictive information and statistical modeling to study real-world resource allocation.
Selected research
All researchBeyond Borders: Uncertainty in Supragovernmental Tax Enforcement and Corporate Investment
Abstract
Amid growing globalization, many countries have offered tax incentives to attract corporate investment. Prior research studies the role such incentives play in firms’ location and investment choices. However, we have limited evidence on the role that uncertainty about the intensity of future tax enforcement plays in those decisions.
In 2013, the European Commission (“E.C.”) abruptly began investigating tax ruling practices of several countries in response to allegations that certain firms received preferential tax treatment (“state aid cases”). We use this setting to study the economic consequences of increased uncertainty about future tax enforcement. We find evidence consistent with significant reductions in U.S. multinational firms’ subsidiary investments within, firm input purchases from, and aggregate investment of U.S. firms flowing to targeted state aid countries. Our results highlight significant real effects of increased uncertainty and complexity surrounding shifts in supragovernmental tax enforcement.
Redesigning Executive Incentives: The Rising Role of Subjective Performance Measures
Abstract
Despite the growing use of subjective performance incentives used in executive bonuses, empirical evidence on their effectiveness remains inconclusive. This study explores three aspects of subjective metrics in bonus plan design: their prevalence, the goals they target, and their impact on managerial behavior and firm outcomes.
First, I document 53.8 percent of CEO bonus plans include at least one subjective performance measure, and among these plans, an average of 38.9 percent of total bonus weight is allocated to these measures. Using machine learning, I show subjective metrics target incentives related to employees, firm culture, and executive performance.
Second, using the Tax Cuts and Jobs Act as a quasi-exogenous shock to contract design, I find firms increase the number and weight of subjective metrics by 22.9 percent and 10.4 percent, respectively. Finally, I find the increasing prevalence of subjective performance measures positively influences CEO effort, corporate culture, and innovation.
Foreign Tax Holiday Participation and U.S. Job and Investment Loss
Abstract
We investigate whether foreign tax holiday participation among US multinational companies is associated with offshoring US jobs and other domestic investment activities. We find that foreign tax holiday participation is associated with (1) an increase in offshoring US jobs and (2) a decrease in domestic investment, as proxied by changes in the number of employees, capital expenditures, and R&D activity. Furthermore, we find evidence suggesting that the association between targeted, temporary tax incentives provided by foreign tax holidays and firms' domestic activities is stronger among firms with a smaller foreign presence and is distinct from the impact of foreign statutory tax rate changes. Overall, the results of this study increase our understanding of the firm-level consequences of foreign tax holiday participation, the influence of various tax incentive structures on the allocation of firm resources, and the potential consequences of international tax competition.
Professional Inquiries & Collaboration
For professional inquiries, research collaborations, or speaking opportunities, please contact me. Potential areas include AI-driven document analysis, custom data development, research-based advisory projects, and the evaluation of business or regulatory questions.