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Measuring Firm Complexity
Published online by Cambridge University Press: 15 May 2023
Abstract
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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.
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- Type
- Research Article
- Information
- Journal of Financial and Quantitative Analysis , Volume 59 , Issue 6 , September 2024 , pp. 2487 - 2514
- Creative Commons
This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (https://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited.- Copyright
- © The Author(s), 2023. Published by Cambridge University Press on behalf of the Michael G. Foster School of Business, University of Washington
Footnotes
We thank Brad Badertscher, Jeffrey Burks, Tony Cookson, Nan Da, Hermann Elendner, Mine Ertugrul (the referee), Margaret Forster, Andrew Imdieke, Jerry Langley, Paul Malatesta (the editor), Mikaela McDonald, Jamie O’Brien, Marcelo Ortiz, Jay Ritter, Bill Schmuhl, and seminar participants at the 2018 Digital Innovation in Finance Conference, 2019 Humboldt University Summer Camp, 2019 Future of Financial Information Conference, University of Notre Dame, University of Connecticut, Chinese University, Georgia State University, University of Colorado, 2023 Eastern Finance Association, 2020 Swiss Accounting Research Alpine Camp, 2019 International Research Symposium for Accounting Academics, Université Paris-Dauphine, and Baylor University for helpful comments.
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