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Measuring Firm Complexity

Published online by Cambridge University Press:  15 May 2023

Tim Loughran*
Affiliation:
University of Notre Dame Mendoza College of Business
Bill McDonald
Affiliation:
University of Notre Dame Mendoza College of Business [email protected]
*
[email protected] (corresponding author)
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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.

Type
Research Article
Creative Commons
Creative Common License - CCCreative Common License - BY
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.

References

Akey, P.; Grégoire, V.; and Martineau, C.. “Price Revelation from Insider Trading: Evidence from Hacked Earnings News.” Journal of Financial Economics, 143 (2022), 11621184.CrossRefGoogle Scholar
Bae, J. W.; Belo, F.; Li, J.; Lin, X.; and Zhao, X.The Opposing Effects of Complexity and Information Content on Uncertainty Dynamics: Evidence from 10-K Filings.” Management Science, forthcoming (2023).CrossRefGoogle Scholar
Balvers, R. J.; McDonald, B.; and Miller, R. E.. “Underpricing of New Issues and the Choice of Auditor as a Signal of Investment Banker Reputation.” Accounting Review, 63 (1988), 605622.Google Scholar
Bellstam, G.; Bhagat, S.; and Cookson, J. A.. “A Text-Based Analysis of Corporate Innovation.” Management Science, 67 (2021), 40044031.CrossRefGoogle Scholar
Bloom, N.Fluctuations in Uncertainty.” Journal of Economic Perspectives, 28 (2014), 153176.CrossRefGoogle Scholar
Bloom, N.; Bond, S.; and Reenan, J.. Uncertainty and Investment Dynamics.” Review of Economic Studies, 74 (2007), 391415.CrossRefGoogle Scholar
Bloomfield, R. Discussion of “Annual Report Readability, Current Earnings, and Earnings Persistence.” Journal of Accounting and Economics, 45 (2008), 248252.CrossRefGoogle Scholar
Bond, S.; Moessner, R.; Mumtaz, H.; and Syed, M.. “Microeconometric Evidence on Uncertainty and Investment.” Working Paper, The Institute for Fiscal Studies, available at https://ifs.org.uk/sites/default/files/output_url_files/wpinvunc.pdf (2005).CrossRefGoogle Scholar
Botosan, C.; Huffman, A.; and Stanford, M.. “The State of Segment Reporting by US Public Entities: 1976–2017.” Accounting Horizons, 35 (2021), 127.CrossRefGoogle Scholar
Bratten, B.; Gleason, C. A.; Larocque, S. A.; and Mills, L. F.. “Forecasting Taxes: New Evidence from Analysts.” Accounting Review, 92 (2017), 129.CrossRefGoogle Scholar
Chakrabarty, B., Seetharaman, A., Swanson, Z. and Wang, X., “Management Risk Incentives and the Readability of Corporate Disclosures.” Financial Management, 47 (2018), 583616.CrossRefGoogle Scholar
Chaney, P. K., and Philipich, K. L.. “Shredded Reputation: The Cost of Audit Failure.” Journal of Accounting Research, 40 (2002), 12211245.CrossRefGoogle Scholar
Chen, S.; DeFond, M.; and Park, C.. “Voluntary Disclosure of Balance Sheet Information in Quarterly Earnings Announcements.” Journal of Accounting and Economics, 33 (2002), 229251.CrossRefGoogle Scholar
Chinco, A.; Clark-Joseph, A.; and Ye, M.. “Sparse Signals in the Cross-Section of Returns.” Journal of Finance, 74 (2019), 449492.CrossRefGoogle Scholar
Cohen, L., and Lou, D.. “Complicated Firms.” Journal of Financial Economics, 104 (2012), 383400.CrossRefGoogle Scholar
Du, Q.; Yu, F.; and Yu, X.. “Cultural Proximity and the Processing of Financial Information.” Journal of Financial and Quantitative Analysis, 52 (2017), 27032726.CrossRefGoogle Scholar
Dyer, T.; Lang, M.; and Stice-Lawrence, L.. “The Evolution of 10-K Textual Disclosure: Evidence from Latent Dirichlet Allocation.” Journal of Accounting and Economics, 64 (2017), 221245.CrossRefGoogle Scholar
Ertugrul, M.; Lei, J.; Qiu, J.; and Wan, C.. “Annual Report Readability, Tone Ambiguity, and the Cost of Borrowing.” Journal of Financial and Quantitative Analysis, 52 (2017), 811836.CrossRefGoogle Scholar
Fama, E. F., and French, K. R.. “Industry Costs of Equity.” Journal of Financial Economics, 43 (1997), 153193.CrossRefGoogle Scholar
Gao, Q.; Lin, M.; and Sias, R.. “Words Matter: The Role of Readability, Tone, and Deception Cues in Online Credit Markets.” Journal of Financial and Quantitative Analysis, 58 (2021), 128.CrossRefGoogle Scholar
Ge, W., and McVay, S.. “The Disclosure of Material Weaknesses in Internal Control After the Sarbanes-Oxley Act.” Accounting Horizons, 19 (2005), 137158.CrossRefGoogle Scholar
Gentzkow, M.; Kelly, B.; and Taddy, M.. “Text as Data.” Journal of Economic Literature, 57 (2019), 535574.CrossRefGoogle Scholar
Glendening, M.; Mauldin, E.; and Shaw, K.. “Determinants and Consequences of Quantitative Critical Accounting Estimate Disclosures.” Accounting Review, 94 (2019), 189218.CrossRefGoogle Scholar
Goldreich, O. P, Np, and Np-Completeness: The Basics of Computational Complexity. Cambridge: Cambridge University Press (2010).CrossRefGoogle Scholar
Gomes, A.; Gorton, G.; and Madureira, L.. “SEC Regulation Fair Disclosure, Information, and the Cost of Capital.” Journal of Corporate Finance, 13 (2007), 300334.CrossRefGoogle Scholar
Griffin, P.Got Information? Investor Response to Form 10-K and Form 10-Q EDGAR Filings.” Review of Accounting Studies, 8 (2003), 433460.CrossRefGoogle Scholar
Hay, D. C.; Knechel, W. R.; and Wong, N.. “Audit Fees: A Meta‐Analysis of the Effect of Supply and Demand Attributes.” Contemporary Accounting Research, 23 (2006), 141191.CrossRefGoogle Scholar
Hoberg, G., and Phillips, G.. “Text-Based Network Industries and Endogenous Product Differentiation.” Journal of Political Economy, 124 (2016), 14231465.CrossRefGoogle Scholar
Hogan, C. E., and Wilkins, M. S.. “Evidence on the Audit Risk Model: Do Auditors Increase Audit Fees in the Presence of Internal Control Deficiencies?Contemporary Accounting Research, 25 (2008), 219242.CrossRefGoogle Scholar
Hoitash, R., and Hoitash, U.. “Measuring Accounting Reporting Complexity with XBRL.” Accounting Review, 93 (2018), 259287.CrossRefGoogle Scholar
Hwang, B., and Kim, H.. “It Pays to Write Well.” Journal of Financial Economics, 124 (2017), 373394.CrossRefGoogle Scholar
Jiang, G.; Lee, C.; and Zhang, Y.. “Information Uncertainty and Expected Returns.” Review of Accounting Studies, 10 (2005), 185221.CrossRefGoogle Scholar
Jones, M. J. and Shoemaker, P. A.. “Accounting Narratives: A Review of Empirical Studies of Content and Readability.” Journal of Accounting Literature, 13 (1994), 142184.Google Scholar
Ke, Z.; Kelly, B.; and Xiu, D.. “Predicting Returns with Text Data.” NBER Working Paper No. w26186 (2019).CrossRefGoogle Scholar
Kim, C.; Wang, K.; and Zhang, L.. “Readability of 10-K Reports and Stock Price Crash Risk.” Contemporary Accounting Research, 36 (2019), 11841216.CrossRefGoogle Scholar
Kravet, T., and Muslu, V.. “Textual Risk Disclosures and Investors’ Risk Perceptions.” Review of Accounting Studies, 18 (2013), 1088–1022.CrossRefGoogle Scholar
Lee, C.M.; Sun, S. T.; Wang, R.; and Zhang, R.. “Technological Links and Predictable Returns.” Journal of Financial Economics, 132 (2019), 7696.CrossRefGoogle Scholar
Lehavy, R.; Li, F.; and Merkley, K.. “The Effect of Annual Report Readability on Analyst Following and the Properties of their Earnings Forecasts.” Accounting Review, 86 (2011), 10871115.CrossRefGoogle Scholar
Leuz, C., and Wysocki, P.. “The Economics of Disclosure and Financial Reporting Regulation: Evidence and Suggestions for Future Research.” Journal of Accounting Research, 54 (2016), 525622.CrossRefGoogle Scholar
Li, F.Annual Report Readability, Current Earnings, and Earnings Persistence.” Journal of Accounting and Economics, 45 (2008), 221247.CrossRefGoogle Scholar
Liu, X., and Natarajan, R.. “The Effect of Financial Analysts’ Strategic Behavior on Analysts’ Forecast Dispersion.” Accounting Review, 87 (2012), 21232149.CrossRefGoogle Scholar
Lo, K.; Ramos, F.; and Rogo, R.. “Earnings Management and Annual Report Readability.” Journal of Accounting and Economics, 63 (2017), 125.CrossRefGoogle Scholar
Loughran, T., and McDonald, B.. “When is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks.” Journal of Finance, 66 (2011), 3565.CrossRefGoogle Scholar
Loughran, T., and McDonald, B.. “Measuring Readability in Financial Disclosures.” Journal of Finance, 69 (2014), 16431671.CrossRefGoogle Scholar
Loughran, T., and McDonald, B.. “Textual Analysis in Accounting and Finance: A Survey.” Journal of Accounting Research, 54 (2016), 11871230.CrossRefGoogle Scholar
Lowry, M.; Michaely, R.; and Volkova, E.. “Information Revealed Through the Regulatory Process: Interactions Between SEC and Companies Ahead of Their IPO.” Review of Financial Studies, 33 (2020), 55105554.CrossRefGoogle Scholar
Mai, D., and Pukthuanthong, K.. “Economic Narratives and Market Outcomes: A Semi-Supervised Topic Modeling Approach.” Working Paper, available at https://ssrn.com/abstract_id=3990324 (2021).CrossRefGoogle Scholar
Rudin, C.Stop Explaining Black Box Machine Learning Models for High States Decisions and Use Interpretable Models Instead.” Nature Machine Intelligence, 1 (2019), 206215.CrossRefGoogle ScholarPubMed
Simunic, D. A.The Pricing of Audit Services: Theory and Evidence.” Journal of Accounting Research, 18 (1980), 161190.CrossRefGoogle Scholar
Snowden, D., and Boone, M.. “A Leader’s Framework for Decision Making.” Harvard Business Review, 85 (2007), 6877.Google ScholarPubMed
Stice-Lawrence, L.Practical Issues to Consider when Working with Big Data.” Review of Accounting Studies, 27 (2022), 18.CrossRefGoogle Scholar
Tetlock, P.C.Giving Content to Investor Sentiment: The Role of Media in the Stock Market.” Journal of Finance, 62 (2007), 11391168.CrossRefGoogle Scholar
Wang, K.; Yu, X.; and Zhang, B.. “Panda Games: Corporate Disclosure in the Eclipse of Search.” Management Science, forthcoming (2023).CrossRefGoogle Scholar
You, H., and Zhang, X. J.. “Financial Reporting Complexity and Investor Underreaction to 10-K Information.” Review of Accounting Studies, 14 (2009), 559586.CrossRefGoogle Scholar