Measuring Firm Complexity

Journal of Financial and Quantitative Analysis, Forthcoming

55 Pages Posted: 15 Jul 2020 Last revised: 25 Oct 2023

See all articles by Tim Loughran

Tim Loughran

University of Notre Dame

Bill McDonald

University of Notre Dame - Mendoza College of Business - Department of Finance

Date Written: October 24, 2023

Abstract

In business research, firm size is both ubiquitous and readily measured. Complexity, another firm-related construct, is also relevant, but difficult to measure and not well defined. As a result, complexity is less frequently incorporated in empirical designs. We argue that most extant measures of complexity are one-dimensional, have limited availability, and/or are frequently misspecified. Using both machine learning and an application specific lexicon, we develop a text solution that uses widely available data and provides an omnibus measure of complexity. Our proposed measure, used in tandem with 10-K file size, provides a useful proxy that dominates traditional measures.

Keywords: Firm complexity; textual analysis; Form 10-K; machine learning; lasso regression.

JEL Classification: D82, D83, G14, G18, G30, M40, M41

Suggested Citation

Loughran, Tim and McDonald, Bill, Measuring Firm Complexity (October 24, 2023). Journal of Financial and Quantitative Analysis, Forthcoming , Available at SSRN: https://ssrn.com/abstract=3645372 or http://dx.doi.org/10.2139/ssrn.3645372

Tim Loughran (Contact Author)

University of Notre Dame ( email )

Department of Finance
245 Mendoza College of Business
Notre Dame, IN 46556-5646
United States
574-631-8432 (Phone)
574-631-5255 (Fax)

Bill McDonald

University of Notre Dame - Mendoza College of Business - Department of Finance ( email )

University of Notre Dame
Notre Dame, IN 46556-0399
United States
574-274-2333 (Phone)

HOME PAGE: http://sites.nd.edu/bill-mcdonald

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