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How to Differentiate the Difference - A quantitative study on the determinants of discrepancies between expected and actual credit ratings in insurance companies

Abstract
We study the determinants of discrepancies between expected and actual credit ratings among insurance companies. We analyze 124 public insurance companies with an assigned credit rating, and model discrepancies as the difference between expected and actual credit ratings. We find relatively strong evidence that embedded value has no association with a positive or negative difference, and that embedded value facilitates more accurate credit ratings. We find weak evidence supporting that earnings management is associated with overestimated ratings relative to financial strength. Firms reporting under IFRS are found to be significantly associated with overestimated ratings. Interpretations suggest that this relationship is explained by the opportunistic and discretionary nature of IFRS 4. Finally, we find that life insurers exhibit overestimated ratings. Life insurers’ financial statements are underlined to be difficult to assess, and in that, profitability is hard to derive. Increased complex risk exposure for life insurers might also entail that rating agencies cannot, or do not, acknowledge the actual risk exposure of life insurers. Determinants of discrepancies between expected and actual ratings have not been addressed until now. As such, our findings have apparent benefits for users of financial statements and for rating agencies, as well as users of credit ratings. We contribute not only by filling this gap, but to direct future research towards exploring this framework further.
Degree
Master 2-years
Other description
MSc in Accounting and Financial Management
URI
http://hdl.handle.net/2077/65377
Collections
  • Master theses
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gupea_2077_65377_1.pdf (1.604Mb)
Date
2020-07-01
Author
Hellman, Gustav
Walldén Persson, Henrik
Keywords
Expected ratings
credit ratings
financial strength
discrepancy
earnings management
embedded value
IFRS
life insurers
Series/Report no.
Master Degree Project
2020:30
Language
eng
Metadata
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