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Do going concern opinions provide incremental information to predict corporate defaults?

Author

Listed:
  • Elizabeth Gutierrez

    (Universidad de Chile)

  • Jake Krupa

    (University of Miami)

  • Miguel Minutti-Meza

    (University of Miami)

  • Maria Vulcheva

    (Florida International University)

Abstract

Investors, regulators, and academics question the usefulness of going concern opinions (GCOs). We assess whether GCOs provide incremental information, relative to other predictors of corporate default. Our measure of incremental information is the additional predictive power that GCOs give to a default model. Using data from 1996 to 2015, initially we find no difference in predictive power between GCOs alone and a default model that includes financial ratios. However, there is an imperfect overlap between GCOs and other predictors. We show that GCOs increase the predictive power of several models that include ratios, market variables, probability of default estimates, and credit ratings. Using a model that includes ratios and market variables, GCOs increase the number of predicted defaults by 4.4%, without increasing Type II errors. Our findings suggest that GCOs summarize a complex set of conditions not captured by other predictors of default.

Suggested Citation

  • Elizabeth Gutierrez & Jake Krupa & Miguel Minutti-Meza & Maria Vulcheva, 2020. "Do going concern opinions provide incremental information to predict corporate defaults?," Review of Accounting Studies, Springer, vol. 25(4), pages 1344-1381, December.
    • Handle: RePEc:spr:reaccs:v:25:y:2020:i:4:d:10.1007_s11142-020-09544-x
    DOI: 10.1007/s11142-020-09544-x
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    Cited by:

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    3. Der-Jang Chi & Chien-Chou Chu, 2021. "Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
    4. Ben Lourie & N. Bugra Ozel & Alexander Nekrasov & Chenqi Zhu, 2024. "Consensus credit ratings: a view from banks," Review of Accounting Studies, Springer, vol. 29(3), pages 2391-2436, September.
    5. Camacho-Miñano, María-del-Mar & Muñoz-Izquierdo, Nora & Pincus, Morton & Wellmeyer, Patricia, 2024. "Are key audit matter disclosures useful in assessing the financial distress level of a client firm?," The British Accounting Review, Elsevier, vol. 56(2).
    6. Der-Jang Chi & Zong-De Shen, 2022. "Using Hybrid Artificial Intelligence and Machine Learning Technologies for Sustainability in Going-Concern Prediction," Sustainability, MDPI, vol. 14(3), pages 1-18, February.
    7. Proho Mahir, 2023. "Going concern assessment: a literature review," Journal of Forensic Accounting Profession, Sciendo, vol. 3(2), pages 48-62, December.

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