IDEAS home Printed from https://ideas.repec.org/p/nbr/nberwo/26856.html
   My bibliography  Save this paper

Measuring the Cost of Regulation: A Text-Based Approach

Author

Listed:
  • Charles W. Calomiris
  • Harry Mamaysky
  • Ruoke Yang

Abstract

We derive a measure of firm-level regulatory exposure from the text of corporate earnings calls. We use this measure to study the effect of regulation on companies’ growth, leverage, profitability, and equity returns. Higher regulatory exposure results in slower sales and asset growth, lower leverage, reduced profitability, but higher post-call equity returns. These effects are mitigated for larger firms. Our findings suggest that both compliance risk and physical operational cost are consequences of increased regulation, but the magnitude of the effects of compliance risk are larger. The topical context of regulation is important for future firm-level outcomes.

Suggested Citation

  • Charles W. Calomiris & Harry Mamaysky & Ruoke Yang, 2020. "Measuring the Cost of Regulation: A Text-Based Approach," NBER Working Papers 26856, National Bureau of Economic Research, Inc.
  • Handle: RePEc:nbr:nberwo:26856
    Note: CF EFG IO LE POL
    as

    Download full text from publisher

    File URL: http://www.nber.org/papers/w26856.pdf
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Sanjiv R. Das & Mike Y. Chen, 2007. "Yahoo! for Amazon: Sentiment Extraction from Small Talk on the Web," Management Science, INFORMS, vol. 53(9), pages 1375-1388, September.
    2. Charles W. Calomiris & Stephen H. Haber, 2015. "Fragile by Design: The Political Origins of Banking Crises and Scarce Credit," Economics Books, Princeton University Press, edition 1, number 10177-2.
    3. George J. Stigler, 1971. "The Theory of Economic Regulation," Bell Journal of Economics, The RAND Corporation, vol. 2(1), pages 3-21, Spring.
    4. Calomiris, Charles W. & Mamaysky, Harry, 2019. "How news and its context drive risk and returns around the world," Journal of Financial Economics, Elsevier, vol. 133(2), pages 299-336.
    5. Price, S. McKay & Doran, James S. & Peterson, David R. & Bliss, Barbara A., 2012. "Earnings conference calls and stock returns: The incremental informativeness of textual tone," Journal of Banking & Finance, Elsevier, vol. 36(4), pages 992-1011.
    6. Bernard, Vl & Thomas, Jk, 1989. "Post-Earnings-Announcement Drift - Delayed Price Response Or Risk Premium," Journal of Accounting Research, Wiley Blackwell, vol. 27, pages 1-36.
    7. Òscar Jordà, 2005. "Estimation and Inference of Impulse Responses by Local Projections," American Economic Review, American Economic Association, vol. 95(1), pages 161-182, March.
    8. Tim Loughran & Bill Mcdonald, 2011. "When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10‐Ks," Journal of Finance, American Finance Association, vol. 66(1), pages 35-65, February.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Francesco Trebbi & Miao Ben Zhang, 2022. "The Cost of Regulatory Compliance in the United States," NBER Working Papers 30691, National Bureau of Economic Research, Inc.
    2. Singla, Shikhar, 2023. "Regulatory costs and market power," LawFin Working Paper Series 47, Goethe University, Center for Advanced Studies on the Foundations of Law and Finance (LawFin).
    3. Tara M. Sinclair & Zhoudan Xie, 2021. "Sentiment and Uncertainty about Regulation," Working Papers 2021-004, The George Washington University, Department of Economics, H. O. Stekler Research Program on Forecasting.
    4. Michael Ryan, 2020. "A Narrative Approach to Creating Instruments with Unstructured and Voluminous Text: An Application to Policy Uncertainty," Working Papers in Economics 20/10, University of Waikato.
    5. de Lucio, Juan & Mora-Sanguinetti, Juan S., 2022. "Drafting “better regulation”: The economic cost of regulatory complexity," Journal of Policy Modeling, Elsevier, vol. 44(1), pages 163-183.
    6. MORIKAWA Masayuki, 2022. "Compliance Costs of Regulations and Productivity," Policy Discussion Papers 22025, Research Institute of Economy, Trade and Industry (RIETI).
    7. Masayuki Morikawa, 2023. "Compliance costs and productivity: an approach from working hours," Journal of Regulatory Economics, Springer, vol. 63(3), pages 117-137, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thomas Renault, 2020. "Sentiment analysis and machine learning in finance: a comparison of methods and models on one million messages," Digital Finance, Springer, vol. 2(1), pages 1-13, September.
    2. Devrimi Kaya & Christian Maier & Tobias Böhmer, 2020. "Empirische Kapitalmarktforschung zu Conference Calls: Eine Literaturanalyse [Empirical Capital Market Research on Conference Calls: A Literature Review]," Schmalenbach Journal of Business Research, Springer, vol. 72(2), pages 183-212, June.
    3. Christina Bannier & Thomas Pauls & Andreas Walter, 2019. "Content analysis of business communication: introducing a German dictionary," Journal of Business Economics, Springer, vol. 89(1), pages 79-123, February.
    4. Xiao Wu, Dong & Yao, Xiao & Luan Guo, Jian, 2021. "Is Textual Tone Informative or Inflated for Firm’s Future Value? Evidence from Chinese Listed Firms," Economic Modelling, Elsevier, vol. 94(C), pages 513-525.
    5. Miwa, Kotaro, 2022. "The informational role of analysts’ textual statements," Research in International Business and Finance, Elsevier, vol. 59(C).
    6. Sharpe, Steven A. & Sinha, Nitish R. & Hollrah, Christopher A., 2023. "The power of narrative sentiment in economic forecasts," International Journal of Forecasting, Elsevier, vol. 39(3), pages 1097-1121.
    7. Tim Loughran & Bill Mcdonald, 2016. "Textual Analysis in Accounting and Finance: A Survey," Journal of Accounting Research, Wiley Blackwell, vol. 54(4), pages 1187-1230, September.
    8. Schnaubelt, Matthias & Seifert, Oleg, 2020. "Valuation ratios, surprises, uncertainty or sentiment: How does financial machine learning predict returns from earnings announcements?," FAU Discussion Papers in Economics 04/2020, Friedrich-Alexander University Erlangen-Nuremberg, Institute for Economics.
    9. Eryka Probierz & Adam Galuszka & Katarzyna Klimczak & Karol Jedrasiak & Tomasz Wisniewski & Tomasz Dzida, 2021. "Financial Sentiment on Twitter's Community and it's Connection to Polish Stock Market Movements in Context of Behavior Modelling," European Research Studies Journal, European Research Studies Journal, vol. 0(4B), pages 56-65.
    10. Miwa, Kotaro, 2021. "Language barriers in analyst reports," International Review of Economics & Finance, Elsevier, vol. 75(C), pages 223-236.
    11. Ingrid E. Fisher & Margaret R. Garnsey & Mark E. Hughes, 2016. "Natural Language Processing in Accounting, Auditing and Finance: A Synthesis of the Literature with a Roadmap for Future Research," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 23(3), pages 157-214, July.
    12. Enwei Zhu & Jing Wu & Hongyu Liu & Keyang Li, 2023. "A Sentiment Index of the Housing Market in China: Text Mining of Narratives on Social Media," The Journal of Real Estate Finance and Economics, Springer, vol. 66(1), pages 77-118, January.
    13. Paul E. Soto, 2021. "Breaking the Word Bank: Measurement and Effects of Bank Level Uncertainty," Journal of Financial Services Research, Springer;Western Finance Association, vol. 59(1), pages 1-45, April.
    14. Andres Algaba & David Ardia & Keven Bluteau & Samuel Borms & Kris Boudt, 2020. "Econometrics Meets Sentiment: An Overview Of Methodology And Applications," Journal of Economic Surveys, Wiley Blackwell, vol. 34(3), pages 512-547, July.
    15. Chouliaras, Andreas, 2015. "The Pessimism Factor: SEC EDGAR Form 10-K Textual Analysis and Stock Returns," MPRA Paper 65585, University Library of Munich, Germany.
    16. Yan Luo & Linying Zhou, 2020. "Textual tone in corporate financial disclosures: a survey of the literature," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 17(2), pages 101-110, September.
    17. Jiao Ji & Oleksandr Talavera & Shuxing Yin, 2018. "The Hidden Information Content: Evidence from the Tone of Independent Director Reports," Working Papers 2018-28, Swansea University, School of Management.
    18. Chen, Cathy Yi-Hsuan & Fengler, Matthias R. & Härdle, Wolfgang Karl & Liu, Yanchu, 2022. "Media-expressed tone, option characteristics, and stock return predictability," Journal of Economic Dynamics and Control, Elsevier, vol. 134(C).
    19. Kim, Jongkyum & Lim, Jee-Hae & Yoon, Kyunghee, 2022. "How do the content, format, and tone of Twitter-based corporate disclosure vary depending on earnings performance?," International Journal of Accounting Information Systems, Elsevier, vol. 47(C).
    20. Li, Frank Weikai & Sun, Chengzhu, 2022. "Information acquisition and expected returns: Evidence from EDGAR search traffic," Journal of Economic Dynamics and Control, Elsevier, vol. 141(C).

    More about this item

    JEL classification:

    • G18 - Financial Economics - - General Financial Markets - - - Government Policy and Regulation
    • G38 - Financial Economics - - Corporate Finance and Governance - - - Government Policy and Regulation
    • K2 - Law and Economics - - Regulation and Business Law
    • L51 - Industrial Organization - - Regulation and Industrial Policy - - - Economics of Regulation

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nbr:nberwo:26856. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: the person in charge (email available below). General contact details of provider: https://edirc.repec.org/data/nberrus.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.