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Measuring Risk Premiums Using Financial Reports and Actuarial Disclosures

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Abstract

Insurance companies increasingly augment their financial reports by releasing actuarial measures—the so-called embedded value—to supply information about the value of their life insurance activities. Both accounting and actuarial measures differ with respect to the timeliness of profit realisation and its reliability, and their performance in yielding information may differ. This paper asks if and how embedded values help in assessing risk premiums. We estimate multifactor market models in the spirit of Fama and French, and find that actuarial disclosures are superior to financial accounting in estimating these risk premiums. They further add information to financial reports as an estimator for growth opportunities.

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Notes

  1. O’Keeffe et al. (2005).

  2. Horton (2007).

  3. Serafeim (2008).

  4. Meyer (2005).

  5. Most prominent contributions are Klumpes (2002); Horton (2007) and Serafeim (2011).

  6. Fama and French (1993).

  7. Horton (2007) and Serafeim (2011), respectively.

  8. Francis et al. (2005) and Ernstberger and Vogler (2008).

  9. Horton et al. (2007) and De Mey (2009).

  10. Klumpes (1999).

  11. IASB FW.OB2-4 as well as MCEV Principle 1 (CFO Forum, 2009b) bear a similar reading.

  12. IASB FW.OB13, 17.

  13. MCEV.BC6.

  14. Originally set out by Anderson (1959).

  15. Horton and Macve (1997).

  16. Towers Perrin (2005).

  17. CFO Forum (2009a, 2009b).

  18. Serafeim (2011).

  19. Klumpes (2005).

  20. Horton et al. (2007).

  21. Dickinson and Liedtke (2004).

  22. DRSC (2010); ANC (2011) and IASB (2011).

  23. ED/2013/7 IFRS 4.18.

  24. IASB (2014).

  25. CFO Forum (2013); DRSC (2013); FRC (2013).

  26. CFO Forum (2006).

  27. Höring and Gründl (2011).

  28. Black et al. (1972).

  29. Merton (1973) and Ross (1976).

  30. For example, Chen et al. (1986); Jorion (1990) and El-Sharif et al. (2005).

  31. Fama and French (1992, 1993, 1995, 1996), 1997.

  32. Already Banz (1981); Reinganum (1981); more recently O’Brien et al. (2010).

  33. Perez-Quiros and Timmermann (2000); Dichev (1998); cf. Vassalou and Xing (2004)

  34. Smith and Watts (1992).

  35. Lakonishok et al. (1994) and La Porta et al. (1997).

  36. Vassalou (2003); Simpson and Ramchander (2008) and Petkova (2006).

  37. Klumpes (2002).

  38. Horton (2007) and Wu and Hsu (2011).

  39. Paetzmann and Lippl (2013).

  40. Hail (2011).

  41. The CAPM was already applied to the life insurance industry by Hoyt and Trieschmann (1991).

  42. White (1980).

  43. For example, Fama and French (1997) and Elyasiani et al. (2011).

  44. Cummins and Phillips (2005).

  45. See description in Fama and French (1993).

  46. See, for example, Gompers et al. (2010) and Hearn et al. (2010) for recent studies employing the same model with a similarly small sample.

  47. This also corresponds to the intended usage of embedded values, see CFO Forum (2009a).

  48. To form the EVI factor, all equities within the sample are ranked using accounting equity and embedded value data from fiscal year t and the market value as of the beginning of July in t+1. The highest 30 per cent of the firms are selected as the value portfolio, and the lowest 30 per cent as growth portfolio. Following the approach of Francis et al. (2005), the Fama and French (1993) second and third risk factors are based on financial accounting valuation. That is, the additional EVI portfolios are not used to create additional SMB and HML sub-portfolios, as this would result in 18 portfolios (Ernstberger and Vogler, 2008). Owing to our limited sample size, we are unable to form equations at the intersection of the three named portfolios.

  49. Vuong (1989).

  50. For two reasons we do not compare the model R2s: First, the coefficient of determination does not play the dominant role in market model evaluation as it does in, for example, value relevance research. Second, we do not inquire into the overall explanatory power of the models, but into the one of explanatory variables.

  51. We analysed the properties of firms disclosing and not disclosing embedded values. Untabulated results show that both subsamples significantly differ in terms of size (measured as total assets), but not in terms of BE/ME and profitability (measured as return on assets, operating return and financial return). Larger firms thus tend to provide the embedded value information, which we explain by the cost of voluntary disclosures. As disclosing embedded values is invariant to the other major tested firm characteristics, the difference in size is not likely to be material for our analysis, in particular since we do not compare both subsamples.

  52. To calculate the equal-weighted sample mean regarding the share of the life business, we used the 2010 financial reports of the sample firms and extracted the total revenues along with those revenues pertaining to life business lines as disclosed in the segment reporting section.

  53. Gompers et al. (2010); Hearn et al. (2010).

  54. Chen et al. (2001).

  55. Fama and French (1996).

  56. Kaplan and Peterson (1998).

  57. Jegadeesh and Titman (1993).

  58. De Bondt and Thaler (1985).

  59. See for recent applications of the momentum factor, e.g. L'Her et al. (2004), Chordia and Shivakumar (2006) and Fama and French (2010).

  60. Carhart (1997).

  61. For example, Rozeff and Kinney (1976); Tinic and West (1984) and Chordia and Shivakumar (2006).

  62. Gultekin and Gultekin (1983) and Chen and Singal (2004).

  63. See for a review—also for other hypotheses on seasonality in returns—Chen and Singal (2004).

  64. Daniel and Titman (1997).

  65. Ours is similar to the approach of Chan and Faff (2003).

  66. Botosan (2006); Easley and O’Hara (2004) and Leuz and Verrecchia (2004).

  67. Brooks et al. (1998).

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Acknowledgements

The authors would like to thank workshop participants at the EAA Annual Congress 2011, the referees and workshop participants of the VHB Annual Congress 2011, as well as Kerstin Lopatta for their valuable comments and suggestions on earlier versions of the paper. The views expressed in this paper are solely those of the authors and not necessarily those of the Federal Financial Supervisory Authority of Germany or of PricewaterhouseCoopers.

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Authors and Affiliations

  1. Faculty of Business Studies and Economics, University of Bremen, Hochschulring 4, Bremen, 28359, Germany

    Jochen Zimmermann

  2. Federal Financial Supervisory Authority of Germany (Bundesanstalt für Finanzdienstleistungsaufsicht—BaFin), Marie-Curie-Straße 24-28, Frankfurt am Main, 60439, Germany

    Stefan Veith

  3. PricewaterhouseCoopers, Friedrichstraße 14, Stuttgart, 70174, Germany

    Johannes Schymczyk

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Zimmermann, J., Veith, S. & Schymczyk, J. Measuring Risk Premiums Using Financial Reports and Actuarial Disclosures. Geneva Pap Risk Insur Issues Pract 40, 209–231 (2015). https://doi.org/10.1057/gpp.2014.17

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