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Credit scores and the performance of newly-listed stocks: an exploration of the Chinese A-share market

  • Charlie X. Cai
  • Paul B. McGuinnessEmail author
  • Qi Zhang
Original Research
  • 279 Downloads

Abstract

This study assesses the power of S&P Global Market Intelligence’s CreditModel (CM) scores in explaining the short- and long-run performance of newly-listed Chinese firms. A unique feature of the data arises from such scores being outside the public domain during the study period. Focus on such a period avoids the signalling and self-selection biases that inevitably plague studies delving into the relevance of publicly-announced credit ratings. We find that CM scores exhibit positive association with post-listing buy-and-hold stock returns. Even stronger associations emerge when considering fundamental accounting performance, especially over longer-run horizons. In respect of the listing of Chinese A-share firms, we conjecture that greater alignment between secondary market prices and fundamentals would likely have arisen had such scores been in the public domain during the study period.

Keywords

Credit score IPO pricing Post IPO performance 

JEL Classification

G24 G31 

Notes

Acknowledgements

We thank S&P Global Market Intelligence for the provision of Credit Model (CM) scores necessary for the completion of this study. This project developed from a request by S&P in February 2014 to assess the power of CM scores in explaining post-IPO performance. Our investigation offers a framework for assessing this question as well as other related themes. We assert that no pecuniary benefit was received from the data supplier for our work, and that our research on the pricing, performance and other effects of the scores was carried-out independently of the data supplier. But we especially thank Clemens Thym, Michelle P. Cheong and Phillip Lee of S&P Global Market Intelligence for earlier discussions, suggestions and input in relation to data queries and CM score construction. Additionally, we express thanks to Daniel Chan and Jack Wong. We also thank an anonymous reviewer for comments rendered on an earlier draft of this paper.

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Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  • Charlie X. Cai
    • 1
  • Paul B. McGuinness
    • 2
    Email author
  • Qi Zhang
    • 3
  1. 1.Management SchoolUniversity of LiverpoolLiverpoolUK
  2. 2.Department of Finance, CUHK Business SchoolThe Chinese University of Hong KongSha TinHong Kong
  3. 3.Business SchoolDurham UniversityDurhamUK

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