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Mandatory IFRS adoption and analyst forecast accuracy: the role of financial statement-based forecasts and analyst characteristics

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Abstract

This study examines whether the improvement in analyst forecast accuracy around mandatory IFRS adoption is associated with the improvement in the accuracy of financial statement-based forecasts. We find significant out-of-sample improvement in financial statement-based forecast accuracy around mandatory IFRS adoption and significant improvement in analyst forecast accuracy only in countries that made concurrent improvements to financial reporting enforcement. We show that the improvement in analyst forecast accuracy is associated with the improvement in financial statement-based forecast accuracy around IFRS adoption. We also show that analyst forecasts, particularly for firms whose analysts forecast under favorable conditions (i.e., analysts who are less busy with more experience and resources), have a greater association with financial statement-based forecasts, after mandatory IFRS adoption in countries with concurrent changes in enforcement.

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Notes

  1. We note that IFRS is associated with a greater fair-value orientation, which is likely to make earnings more volatile and less predictable (Mensah et al. 2004; Peek 2005). However, IFRS requires only a few fair value measures that impact the income statement (Ernstberger 2008).

  2. While the IASB eliminated the separate presentation of extraordinary items in the income statement in 2005, financial databases commonly used by analysts continue to present extraordinary and special items separately.

  3. To estimate our yearly regressions, we align all financial variables that are reported by the sample firms between January and December of the respective year. For example, if Company A’s financial year 2010 ends in March of 2010 while Company B’s financial year 2010 ends in December of 2010, the regression parameters are estimated by regressing EPS for both companies for the year ended 2010 on the independent variables of the SFSF (DFSF) model for the year ended 2009.

  4. We restrict the out-of-sample EPS forecasts to 1999 through 2004 and 2007 through 2012 to have the same number of pre- and post-IFRS forecast years, respectively. If we exclude the recession related to the financial crisis in 2008 and 2009, the average coefficient estimates for the SFSF model are 1.17 for α0, 0.74 for α1, −2.29 for α2, −0.71 for α3, and 0.08 for α4, while the average coefficient estimates for the DFSF model are 1.51 for α0, 0.74 for α1, 2.03 for α2, −0.09 for α3, 0.002 for α4, −1.14 for α5, −0.36 for α6, 0.26 for α7, and − 3.17 for α8. Here, the average R2 of the SFSF model is 64.88%, while the average R2 of the DFSF model is 59.37%. We note that the inclusion of the recessionary period after 2008 is likely to bias against finding an improvement in financial statement-based forecasts after IFRS adoption.

  5. The classification for IFRS adoption and concurrent enforcement changes follows Christensen et al. (2013) and Daske et al. (2013).

  6. Balance sheet items are translated using spot exchange rates, while income statement items are translated using the average exchange rates over the fiscal year. The introduction of the euro in the European Union in 2001 is unlikely to lead to distortions in our out-of-sample forecasts, as the underlying prediction model mainly relies on financial ratios, rather than on amounts stated in local currencies.

  7. Our results remain qualitatively similar after including a separate fixed effect for each country and each industry and when country characteristics are excluded.

  8. This procedure is repeated 1000 times to obtain the empirical distributions of the differences between the two subsamples that are approximately normal, with means not significantly different from zero. We compute t-statistics using the standard errors derived from these empirical distributions.

  9. This procedure is repeated 1000 times to obtain the empirical distributions of the differences between periods and the difference-in-differences between the IFRS sample(s) and the U.S. control sample that are approximately normal. Again, we compute t-statistics using the standard errors derived from the empirical distributions.

  10. We also estimate the regression models in equations (3) and (4) for the subsample of firms whose analysts forecast under neither favorable nor unfavorable conditions (i.e., the excluded subsample of observations). Again, the results suggest that the improvement in analyst forecast accuracy is more pronounced for firms in countries that adopted IFRS with concurrent changes in enforcement (the coefficient on IFRS_ENFi,t* POSTi,t amounts to 0.0669 and is significant at the 1% level) but not for firms in countries that adopted IFRS without enforcement changes.

  11. In supplemental analyses, we test for the difference in coefficients and find that the improvement in forecast accuracy is significantly greater for firms from IFRS-adopting countries that did not incorporate concurrent changes in enforcement, in comparison to those from countries that made concurrent enforcement changes. However, we note that the absolute value of the financial statement-based forecast errors after IFRS adoption is significantly greater for firms from IFRS-adopting countries that did not incorporate concurrent enforcement changes than for firms from countries that did incorporate concurrent enforcement changes (see Table 4).

  12. We estimate the regression model in equations (5) for the subsample of firms whose analysts forecast under neither favorable nor unfavorable conditions. We find a positive and significant coefficient of 0.051 on SFSFEi,t * POSTi,t * IFRS_ENFi,t (0.0212 on DFSFEi,t * POSTi,t * IFRS_ENFi,t,) and an insignificant coefficient of 0.0046 on SFSFEi,t * POSTi,t* IFRS_nonENFi,t (0.0041 on DFSFEi,t * POSTi,t* IFRS_nonENFi,t).

  13. For this part of our analysis, EPS is taken from the I/B/E/S database as opposed to the Compustat Global database. This should bias against finding incremental improvement to analyst forecasts by incorporating the information provided by the financial statement-based forecasts, due to the fact that I/B/E/S does not provide detailed explanations with respect to which items are eliminated from the actual as well as the median consensus earnings per share number. Our results remain qualitatively similar using the mean consensus forecasted EPS.

  14. We also estimate the regression model in equations (6) for the subsample of firms whose analysts forecast under neither favorable nor unfavorable conditions. The corresponding results for the SFSF model (DFSF model) show a positive and significant coefficient of 0.0579 (0.2479) on (FSFi,t − AFi,t) and of 0.0248 (0.02857) on (FSFi,t − AFi,t) ∗ POSTi,t ∗ IFRS _ nonENFi,t.

  15. For firms that are delisted during the future period, we use a − 55% delisting return (Shumway and Warther 1999).

  16. In untabulated results, we find no difference in the incremental effect for the subsample of firms followed by analysts who forecast under favorable conditions and the subsample of firms followed by analysts who forecast under unfavorable conditions.

  17. We repeat this robustness test, using alternative proxies for the pre-existing level of quality of a country’s institutions, such as the common versus code law distinction, the difference between IFRS and domestic GAAP according to Bae et al. (2008), and the anti-self-dealing index of Djankov et al. (2008). For all of these additional specifications, our results remain qualitatively similar.

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Acknowledgements

We thank Lakshmanan Shivakumar (editor), Jochen Bigus, Joachim Gassen, Max Hewitt, Marlene Plumlee, Eddie Riedl, Harm Schütt, and workshop participants at the Freie Universität Berlin, Drexel University, University of Kansas, Humboldt Universität Berlin, Frankfurt School of Finance & Management, the 38th EAA Annual Congress at Glasgow, and the 1st Accounting, Auditing and Analysis Workshop at Ludwig-Maximilians-Universität München. Part of this research was conducted while Paul Pronobis was an assistant professor at the Freie Universität Berlin and a visiting assistant professor at Indiana University.

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  1. School of Business and Economics, Freie Universität Berlin, Boltzmannstr. 20, 14195, Berlin, Germany

    Matthias Demmer

  2. ESCP Europe, 79, Avenue de la République, 75011, Paris, France

    Paul Pronobis

  3. Kelley School of Business, Indiana University, Bloomington, IN, 47405, USA

    Teri Lombardi Yohn

  4. Kellogg School of Management, Northwestern University, 2211 Campus Drive, Evanston, IL, 60208, USA

    Teri Lombardi Yohn

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  1. Matthias Demmer
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Demmer, M., Pronobis, P. & Yohn, T.L. Mandatory IFRS adoption and analyst forecast accuracy: the role of financial statement-based forecasts and analyst characteristics. Rev Account Stud 24, 1022–1065 (2019). https://doi.org/10.1007/s11142-019-9481-7

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