IDEAS home Printed from https://ideas.repec.org/a/gam/jsusta/v13y2021i21p11631-d661439.html
   My bibliography  Save this article

Artificial Intelligence in Corporate Sustainability: Using LSTM and GRU for Going Concern Prediction

Author

Listed:
  • Der-Jang Chi

    (Department of Accounting, Chinese Culture University, Taipei City 11114, Taiwan)

  • Chien-Chou Chu

    (Graduate Institute of International Business Administration, Chinese Culture University, Taipei City 11114, Taiwan)

Abstract

“Going concern” is a professional term in the domain of accounting and auditing. The issuance of appropriate audit opinions by certified public accountants (CPAs) and auditors is critical to companies as a going concern, as misjudgment and/or failure to identify the probability of bankruptcy can cause heavy losses to stakeholders and affect corporate sustainability. In the era of artificial intelligence (AI), deep learning algorithms are widely used by practitioners, and academic research is also gradually embarking on projects in various domains. However, the use of deep learning algorithms in the prediction of going concern remains limited. In contrast to those in the literature, this study uses long short-term memory (LSTM) and gated recurrent unit (GRU) for learning and training, in order to construct effective and highly accurate going-concern prediction models. The sample pool consists of the Taiwan Stock Exchange Corporation (TWSE) and the Taipei Exchange (TPEx) listed companies in 2004–2019, including 86 companies with going concern doubt and 172 companies without going concern doubt. In other words, 258 companies in total are sampled. There are 20 research variables, comprising 16 financial variables and 4 non-financial variables. The results are based on performance indicators such as accuracy, precision, recall/sensitivity, specificity, F1-scores, and Type I and Type II error rates, and both the LSTM and GRU models perform well. As far as accuracy is concerned, the LSTM model reports 96.15% accuracy while GRU shows 94.23% accuracy.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11631-:d:661439
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2071-1050/13/21/11631/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2071-1050/13/21/11631/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Anthony Brabazon & Peter Keenan, 2004. "A hybrid genetic model for the prediction of corporate failure," Computational Management Science, Springer, vol. 1(3), pages 293-310, October.
    2. Persakis, Anthony & Iatridis, George Emmanuel, 2015. "Earnings quality under financial crisis: A global empirical investigation," Journal of Multinational Financial Management, Elsevier, vol. 30(C), pages 1-35.
    3. Elizabeth Gutierrez & Miguel Minutti-Meza & Kay W. Tatum & Maria Vulcheva, 2018. "Consequences of adopting an expanded auditor’s report in the United Kingdom," Review of Accounting Studies, Springer, vol. 23(4), pages 1543-1587, December.
    4. Suduan Chen, 2019. "An effective going concern prediction model for the sustainability of enterprises and capital market development," Applied Economics, Taylor & Francis Journals, vol. 51(31), pages 3376-3388, July.
    5. Sanoran, Kanyarat (Lek), 2018. "Auditors’ going concern reporting accuracy during and after the global financial crisis," Journal of Contemporary Accounting and Economics, Elsevier, vol. 14(2), pages 164-178.
    6. Chyan-Long Jan, 2021. "Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    7. Reynolds, J. Kenneth & Francis, Jere R., 2000. "Does size matter? The influence of large clients on office-level auditor reporting decisions," Journal of Accounting and Economics, Elsevier, vol. 30(3), pages 375-400, December.
    8. Chyan-long Jan, 2018. "An Effective Financial Statements Fraud Detection Model for the Sustainable Development of Financial Markets: Evidence from Taiwan," Sustainability, MDPI, vol. 10(2), pages 1-14, February.
    9. 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.
    10. DeFond, Mark & Zhang, Jieying, 2014. "A review of archival auditing research," Journal of Accounting and Economics, Elsevier, vol. 58(2), pages 275-326.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. Proho Mahir, 2023. "Going concern assessment: a literature review," Journal of Forensic Accounting Profession, Sciendo, vol. 3(2), pages 48-62, December.
    3. Chyan-Long Jan, 2021. "Detection of Financial Statement Fraud Using Deep Learning for Sustainable Development of Capital Markets under Information Asymmetry," Sustainability, MDPI, vol. 13(17), pages 1-20, September.
    4. Salman Arif & John D. Kepler & Joseph Schroeder & Daniel Taylor, 2022. "Audit process, private information, and insider trading," Review of Accounting Studies, Springer, vol. 27(3), pages 1125-1156, September.
    5. Liang Tan & Santhosh Ramalingegowda & Yong Yu, 2022. "Third-Party Consequences of Changes in Managerial Fiduciary Duties: The Case of Auditors’ Going Concern Opinions," Management Science, INFORMS, vol. 68(2), pages 1556-1572, February.
    6. Rita Lamboglia & Daniela Mancini, 2021. "The relationship between auditors’ human capital attributes and the assessment of the control environment," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 25(4), pages 1211-1239, December.
    7. Xiong, Hao & Hou, Fei & Li, Hanwen & Wang, Huabing, 2020. "Does rice farming shape audit quality: Evidence from signing auditors level analysis," Economic Modelling, Elsevier, vol. 91(C), pages 403-420.
    8. Kharuddin, Khairul Ayuni Mohd & Basioudis, Ilias G & Farooque, Omar Al, 2021. "Effects of the Big 4 national and city-level industry expertise on audit quality in the United Kingdom," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 43(C).
    9. Chen, Peter F. & He, Shaohua & Ma, Zhiming & Stice, Derrald, 2016. "The information role of audit opinions in debt contracting," Journal of Accounting and Economics, Elsevier, vol. 61(1), pages 121-144.
    10. Nitai Chandra Debnath & Suman Paul Chowdhury & Safaeduzzaman Khan, 2022. "The impact of audit quality on real earnings management: evidence from Bangladesh," International Journal of Disclosure and Governance, Palgrave Macmillan, vol. 19(2), pages 218-231, June.
    11. Sanoran, Kanyarat (Lek), 2018. "Auditors’ going concern reporting accuracy during and after the global financial crisis," Journal of Contemporary Accounting and Economics, Elsevier, vol. 14(2), pages 164-178.
    12. Isaac Selasi Awuye, 2022. "The impact of audit quality on earnings management: Evidence from France," Post-Print hal-03824396, HAL.
    13. Guangming Gong & Liang Xiao & Si Xu & Xun Gong, 2019. "Do Bond Investors Care About Engagement Auditors’ Negative Experiences? Evidence from China," Journal of Business Ethics, Springer, vol. 158(3), pages 779-806, September.
    14. Francis, Jere R., 2023. "Going big, going small: A perspective on strategies for researching audit quality," The British Accounting Review, Elsevier, vol. 55(2).
    15. Li, Valerie & Luo, Yan, 2023. "Costs and benefits of auditors' disclosure of critical audit matters: Initial evidence from the United States," Advances in accounting, Elsevier, vol. 60(C).
    16. Jian Cao & Feng Chen & Julia L. Higgs, 2016. "Late for a very important date: financial reporting and audit implications of late 10-K filings," Review of Accounting Studies, Springer, vol. 21(2), pages 633-671, June.
    17. Ting Zhang & So Yean Kwack & Yi Si & Gaoliang Tian, 2023. "Non‐GAAP earnings reporting following going‐concern opinions," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 63(3), pages 3217-3252, September.
    18. Alhababsah, Salem & Alhaj-Ismail, Alaa, 2023. "Does shared tenure between audit committee chair and engagement partner affect audit outcomes? Evidence from the UK," The British Accounting Review, Elsevier, vol. 55(2).
    19. Tong, Lijing & Wu, Bin & Zhang, Min, 2022. "Do auditors’ early-life socioeconomic opportunities improve audit quality? Evidence from China," The British Accounting Review, Elsevier, vol. 54(2).
    20. Aobdia, Daniel, 2019. "Do practitioner assessments agree with academic proxies for audit quality? Evidence from PCAOB and internal inspections," Journal of Accounting and Economics, Elsevier, vol. 67(1), pages 144-174.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jsusta:v:13:y:2021:i:21:p:11631-:d:661439. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.