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Estimation of misreporting probability in corporate credit rating: A nonparametric approach

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  • Ruichang Lu
  • Yao Luo
  • Ruli Xiao

Abstract

There has been heated debate regarding credit‐rating agencies' (CRAs') reporting accuracy of corporate credit ratings, which is essential for investors because they rely on those crediting ratings to make investment decisions. We estimate the reporting accuracy using the data on corporate ratings from Standard & Poor from January 1986 to December 2011. First, there is a U‐shape in the overall misreporting pattern: the left‐hand side (the high‐rating groups) has a lower misreporting probability (3%), the middle has no misreporting, and the right‐hand side has a high misreporting probability (6%). Second, we find that there is a significant difference across the industries. The financial sector has the highest misreporting probability (35% in the lowest rating group) and misreporting magnitude (rating rank jump between true rating and reported rating), and the energy industry has the lowest misreporting probability. Last, when the economic condition is good, CRAs are likelier to inflate the rating.

Suggested Citation

  • Ruichang Lu & Yao Luo & Ruli Xiao, 2023. "Estimation of misreporting probability in corporate credit rating: A nonparametric approach," International Studies of Economics, John Wiley & Sons, vol. 18(3), pages 260-276, September.
    • Handle: RePEc:wly:intsec:v:18:y:2023:i:3:p:260-276
    DOI: 10.1002/ise3.51
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    References listed on IDEAS

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