Can sell-side analysts’ experience, expertise and qualifications help mitigate the adverse effects of accounting reporting complexity?

Abstract

We examine the relation between accounting reporting complexity and analysts’ performance and whether analysts’ qualifications, experience, and expertise in specific financial domains help them more effectively process complex information. We document an inverse relation between complexity and analysts’ performance. Further, we show that analysts’ firm-specific experience, industry focus, and CFA certification alleviate some of the adverse effects of complexity, whereas analysts’ general experience does not appear to do so. Using an XBRL-based approach, we also develop new measures of analysts’ expertise and find that expertise in the areas of fair value, derivatives and pension are more valuable than other analyst characteristics in attenuating the negative effects of complexity arising from transactions and events in these areas. Overall, this study underscores the importance of analyst characteristics and the need to simplify the complex disclosures in the notes to the financial statements.

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Data Availability

Data are publicly available from sources identified in the paper. The measure of accounting reporting complexity (ARC) is available at www.xbrlresearch.com.

Notes

  1. 1.

    The XBRL-based measure of accounting reporting complexity (ARC) is available for download at http://www.xbrlresearch.com/accounting-reporting-complexity/.

  2. 2.

    The XBRL taxonomy, maintained by FASB, contains a comprehensive list of nearly 16,000 tags that companies can use to report financial information. Each financial concept in XBRL is assigned a tag to give it a machine-readable meaning. For example, the net sale concept is assigned the following tag <us-gaap:SalesRevenueNet>.

  3. 3.

    In the subsequent section, we elaborate on why an attempt to disentangle disclosure choices from economic complexity is challenging.

  4. 4.

    To proxy for complexity, studies often use firm segments and foreign operations. These measures lack the necessary granularity to measure complexity (Hoitash and Hoitash 2018). Others developed and used innovative measures based on linguistic disclosures including, length of the financial statement and the Fog index (Miller 2010; Lehavy, Li, and Merkley 2011). An important difference of an XBRL-based measure of complexity is that it is based on monetary financial disclosures, allowing for the disaggregation into account-specific measures of complexity. We recognize these other important dimensions of complexity and throughout our analyses we control for several of these measures.

  5. 5.

    We manually collect the CFA credential information from Factset for each analyst in our sample. We were able to identify this information for roughly 80 percent of our sample.

  6. 6.

    This is similar to the approach in Ma et al. (2016). They suggest that analyst can gain nuanced expertise, in their case global expertise, from similar firms in their portfolio.

  7. 7.

    For example, Compustat includes about 10 fair value variables. In contrast, the FASB U.S. GAAP XBRL taxonomy includes more than 400 monetary fair value tags and Ahn et al. (2020) report that they observe over 200 fair value tags in their study.

  8. 8.

    Similarly, Miller (2010) measures complexity as readability and length and finds that complexity is associated with lower trading activity.

  9. 9.

    In a related paper, Bradshaw et al. (2009) find that atypical accounting methods impede analysts’ performance, suggesting that analysts specialize in covering specific methods within industries and deviation from common industry methods can be detrimental to their work.

  10. 10.

    Fair value, derivatives and pensions have also received significant attention from standard setters. The FASB included several of these accounts in the simplification initiative, suggesting that these are complex accounts (FASB Simplification Project 2016).

  11. 11.

    The audit literature demonstrates that auditors perform better when they develop account-specific expertise in: research and development (Godfrey and Hamilton 2005), information technology (Haislip et al. 2016), and fair value accounting (Ahn et al.  2020).

  12. 12.

    Chen et al. (2018) provide an excellent discussion of the legislative events that led to the enactment of the rule governing XBRL disclosures. Their analysis shows that the legislation had an overall positive market reaction, in particular for firms with weaker information environments. More information on the XBRL taxonomy, tags and extensions is available at the following link: https://xbrl.us/wp-content/uploads/2015/03/PreparersGuide.pdf.

  13. 13.

    Since we extract XBRL tags directly from SEC filings using a standard method, our measure is not based on subjective judgment and can be easily replicated.

  14. 14.

    We retain only the last annual estimates that analysts issue before the earnings announcement.

  15. 15.

    Specifically, smaller filers were not required to file XBRL reports that include the financial statement notes until 2012. As we describe later, removing 2011 from our sample does not alter our results.

  16. 16.

    We included a detailed description of the process to construct account-specific complexity and a sample of fair value tags in Appendices A and B, respectively.

  17. 17.

    To identify account categories we rely on “Disclosure” headings in the taxonomy files. Some tags appear in multiple financial statements and/or notes. We remove tags that repeat in more than three disclosures because we cannot uniquely associate them with a specific account category. The FASB taxonomy files are available at: http://www.fasb.org/cs/ContentServer?c=Page&pagename=FASB%2FPage%2FSectionPage&cid=1176164649716.

  18. 18.

    This is required for extended tags, which are not part of the XBRL taxonomy and adds to classification accuracy of tags that are part of the taxonomy. This process is described in more detail in appendices A and B.

  19. 19.

    For example, if an analyst covers four firms, we sum all fair-value tags across all four firms.

  20. 20.

    We measure share price as of the end of the fiscal period and adjust it for stock splits and stock dividends.

  21. 21.

    We eliminate revisions after earnings announcements to ensure that confounding events do not affect our measure.

  22. 22.

    We eliminate revisions issued on days with conflicting recommendation revisions because it is unclear which revision share prices are reacting to (or ignoring).

  23. 23.

    The expected market reaction to an upgrade is positive whereas it is negative for a downgrade. Hence, while a more positive reaction to an upgrade indicates greater informativeness, it indicates less informativeness for a downgrade. Multiplying the returns associated with downgrades allows us to interpret the results for both upgrades and downgrades in the same way.

  24. 24.

    We assume that markets are at a minimum semi-strong efficient.

  25. 25.

    We exclude forecast dispersion (FORDISP) because it can only be calculated at the firm-year level. We also exclude the value of recommendations (RECVAL) since estimates of the informativeness of recommendations are unreliable at the analyst level because analysts issue only a few recommendations for each firm per year.

  26. 26.

    Note that the sign of the ACCURACY variable does not indicate the direction of the forecast error. We first compute the absolute value of forecast errors and then multiply by −100 so that higher values indicate higher forecast accuracy.

  27. 27.

    Although we use the natural logarithm for the complexity variables, for ease of interpretation, we report statistics based on untransformed values.

  28. 28.

    INDFOCUS equals one divided by the number of industries covered. Since the mean value for INDFOCUS is 0.57 we infer that the average analyst covers 1/0.57 = 1.75 industries.

  29. 29.

    This transformation reverses the variable’s order so that higher (lower) values indicate greater (lower) industry focus.

  30. 30.

    The standard deviation of ARC for the sample used in Table 4 equals 0.372. This measure combined with the estimated coefficient on ARC indicates that a one standard deviation change in ARC is associated with a 0.372 × -0.323 = 0.12 change in forecast accuracy.

  31. 31.

    We mean-center the experience, industry focus, and expertise measures to alleviate the effects of any potential multicollinearity.

  32. 32.

    Analyst with CFA certification do not perform better when pension reporting is complex but they do perform better when fair value and derivatives are. This is consistent with the topics covered in the CFA certification that includes fair value and derivatives.

  33. 33.

    We thank Chen, Miao, and Shevlin for graciously sharing with us their data.

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Acknowledgements

We thank Jean Bedard, Khrystyna Bochkay, Jenna Burke, Roman Chychyla, Matt Ege, Roger Debreceny, Ronen Gal-Or, Jennifer Glenn, Mahendra Gujarathi, Brad Hepfer, Gopal Krishnan, Ariel Markelevich, Landi Morris, Miguel Minutti-Meza, Joseph Pacelli, Patrick Martin, Brian Miller, Sundaresh Ramnath, Alex Rapp, Lynn Rees, Melissa Reville, Sarah Rice, Joe Schroeder, Casey Schwab, Lori Shefchik, Ed Swanson, Senyo Tse, Brady Twedt, Dan Way, Chris Wolfe, and participants of research workshops at Bentley University, Indiana University, University of Miami, Texas A&M University, 2018 Hawai'i Accounting Research Conference, 2018 Global Finance Conference, and the 2018 New Zealand Finance Meeting.

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Correspondence to Ari Yezegel.

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Hoitash, R., Hoitash, U. & Yezegel, A. Can sell-side analysts’ experience, expertise and qualifications help mitigate the adverse effects of accounting reporting complexity?. Rev Quant Finan Acc (2021). https://doi.org/10.1007/s11156-021-00963-8

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Keywords

  • XBRL
  • Complexity
  • Financial analysts’ performance
  • Financial analysts’ expertise
  • Recognition versus disclosure

JEL Classification

  • G24
  • G29
  • M41