IDEAS home Printed from https://ideas.repec.org/a/kap/compec/v62y2023i2d10.1007_s10614-022-10328-5.html
   My bibliography  Save this article

Role of Comprehensive Income in Predicting Bankruptcy

Author

Listed:
  • Asyrofa Rahmi

    (National Central University)

  • Hung-Yuan Lu

    (California State University)

  • Deron Liang

    (National Central University)

  • Dinda Novitasari

    (National Central University)

  • Chih-Fong Tsai

    (National Central University)

Abstract

This study examines the role of comprehensive income and its components, in addition to net income, as inputs to forecast bankruptcy. Using a matched sample of 466 (233 pairs) U.S. bankrupt and non-bankrupt firms from 1993 to 2014, we build a bankruptcy prediction model using random forest classification. Compared with the benchmark model, our proposed model’s accuracy increases by 1.5% and the Type I error decreases by up to 3%. A variable importance analysis reveals that comprehensive income is consistently the most useful variable for bankruptcy prediction. A variable interaction analysis shows that the top interaction pair includes one Altman variable and comprehensive income. Finally, we analyze bankrupt firms that our model identifies but the benchmark model misclassifies; we find that such firm’ other comprehensive income is consistently negative, suggesting that firms’ macroeconomic risk exposure plays a key role in bankruptcy prediction.

Suggested Citation

  • Asyrofa Rahmi & Hung-Yuan Lu & Deron Liang & Dinda Novitasari & Chih-Fong Tsai, 2023. "Role of Comprehensive Income in Predicting Bankruptcy," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 689-720, August.
    • Handle: RePEc:kap:compec:v:62:y:2023:i:2:d:10.1007_s10614-022-10328-5
    DOI: 10.1007/s10614-022-10328-5
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s10614-022-10328-5
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s10614-022-10328-5?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Prodosh Simlai, 2021. "Accrual mispricing, value-at-risk, and expected stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 57(4), pages 1487-1517, November.
    2. Joanna Wieprow & Agnieszka Gawlik, 2021. "The Use of Discriminant Analysis to Assess the Risk of Bankruptcy of Enterprises in Crisis Conditions Using the Example of the Tourism Sector in Poland," Risks, MDPI, vol. 9(4), pages 1-11, April.
    3. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    5. Sumaira Ashraf & Elisabete G. S. Félix & Zélia Serrasqueiro, 2019. "Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress?," JRFM, MDPI, vol. 12(2), pages 1-17, April.
    6. Romain Boulland & Gerald J. Lobo & Luc Paugam, 2019. "Do Investors Pay Sufficient Attention to Banks’ Unrealized Gains and Losses on Available-for-sale Securities?," European Accounting Review, Taylor & Francis Journals, vol. 28(5), pages 819-848, October.
    7. Liang, Deron & Tsai, Chih-Fong & Lu, Hung-Yuan (Richard) & Chang, Li-Shin, 2020. "Combining corporate governance indicators with stacking ensembles for financial distress prediction," Journal of Business Research, Elsevier, vol. 120(C), pages 137-146.
    8. Li, Yuanhui & Li, Xiao & Xiang, Erwei & Geri Djajadikerta, Hadrian, 2020. "Financial distress, internal control, and earnings management: Evidence from China," Journal of Contemporary Accounting and Economics, Elsevier, vol. 16(3).
    9. Edward I. Altman, 1968. "Financial Ratios, Discriminant Analysis And The Prediction Of Corporate Bankruptcy," Journal of Finance, American Finance Association, vol. 23(4), pages 589-609, September.
    10. Dhaliwal, Dan & Subramanyam, K. R. & Trezevant, Robert, 1999. "Is comprehensive income superior to net income as a measure of firm performance?1," Journal of Accounting and Economics, Elsevier, vol. 26(1-3), pages 43-67, January.
    11. Chad R. Larson & Richard Sloan & Jenny Zha Giedt, 2018. "Defining, measuring, and modeling accruals: a guide for researchers," Review of Accounting Studies, Springer, vol. 23(3), pages 827-871, September.
    12. Philippe Jardin & David Veganzones & Eric Séverin, 2019. "Forecasting Corporate Bankruptcy Using Accrual-Based Models," Computational Economics, Springer;Society for Computational Economics, vol. 54(1), pages 7-43, June.
    13. Nguyet T. M. Nguyen & Abdullah Iqbal & Radha K. Shiwakoti, 2022. "The context of earnings management and its ability to predict future stock returns," Review of Quantitative Finance and Accounting, Springer, vol. 59(1), pages 123-169, July.
    14. Amrizah Kamaluddin & Norhafizah Ishak & Nor Farizal Mohammed, 2019. "Financial Distress Prediction Through Cash Flow Ratios Analysis," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 10(3), pages 63-76, May.
    15. Steven Cahan & Dirk E. Black & Steven Cahan, 2016. "Other comprehensive income: a review and directions for future research," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 56(1), pages 9-45, March.
    16. Zhiyong Li & Chen Feng & Ying Tang, 2022. "Bank efficiency and failure prediction: a nonparametric and dynamic model based on data envelopment analysis," Annals of Operations Research, Springer, vol. 315(1), pages 279-315, August.
    17. Messinger, Paul R., 2016. "The role of fairness in competitive supply chain relationships: An experimental studyAuthor-Name: Choi, Sungchul," European Journal of Operational Research, Elsevier, vol. 251(3), pages 798-813.
    18. Melinda Malau & Etty Murwaningsari, 2018. "The Effect Of Accrual Market Pricing, Foreign Ownership, Financial Distress And Leverage To The Integrity Of Financial Statements," Economic Annals, Faculty of Economics and Business, University of Belgrade, vol. 63(217), pages 129-140, April – J.
    19. Tanaka, Katsuyuki & Kinkyo, Takuji & Hamori, Shigeyuki, 2016. "Random forests-based early warning system for bank failures," Economics Letters, Elsevier, vol. 148(C), pages 118-121.
    20. Kanagaretnam, Kiridaran & Mathieu, Robert & Shehata, Mohamed, 2009. "Usefulness of comprehensive income reporting in Canada," Journal of Accounting and Public Policy, Elsevier, vol. 28(4), pages 349-365, July.
    Full references (including those not matched with items on IDEAS)

    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. Jiaming Liu & Chengzhang Li & Peng Ouyang & Jiajia Liu & Chong Wu, 2023. "Interpreting the prediction results of the tree‐based gradient boosting models for financial distress prediction with an explainable machine learning approach," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1112-1137, August.
    2. Li, Chunyu & Lou, Chenxin & Luo, Dan & Xing, Kai, 2021. "Chinese corporate distress prediction using LASSO: The role of earnings management," International Review of Financial Analysis, Elsevier, vol. 76(C).
    3. Ashraf, Sumaira & Félix, Elisabete G.S. & Serrasqueiro, Zélia, 2020. "Development and testing of an augmented distress prediction model: A comparative study on a developed and an emerging market," Journal of Multinational Financial Management, Elsevier, vol. 57.
    4. Pavol Durana & Lucia Michalkova & Andrej Privara & Josef Marousek & Milos Tumpach, 2021. "Does the life cycle affect earnings management and bankruptcy?," Oeconomia Copernicana, Institute of Economic Research, vol. 12(2), pages 425-461, June.
    5. Veganzones, David & Séverin, Eric & Chlibi, Souhir, 2023. "Influence of earnings management on forecasting corporate failure," International Journal of Forecasting, Elsevier, vol. 39(1), pages 123-143.
    6. Youssef Zizi & Amine Jamali-Alaoui & Badreddine El Goumi & Mohamed Oudgou & Abdeslam El Moudden, 2021. "An Optimal Model of Financial Distress Prediction: A Comparative Study between Neural Networks and Logistic Regression," Risks, MDPI, vol. 9(11), pages 1-24, November.
    7. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2022. "Corporate Bankruptcy Prediction Using Machine Learning Methodologies with a Focus on Sequential Data," Computational Economics, Springer;Society for Computational Economics, vol. 59(3), pages 1231-1249, March.
    8. Jun Hyeok Choi & Saerona Kim & Dong-Hoon Yang & Kwanghee Cho, 2021. "Can Corporate Social Responsibility Decrease the Negative Influence of Financial Distress on Accounting Quality?," Sustainability, MDPI, vol. 13(19), pages 1-19, October.
    9. Umair Bin Yousaf & Khalil Jebran & Irfan Ullah, 2024. "Corporate governance and financial distress: A review of the theoretical and empirical literature," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 29(2), pages 1627-1679, April.
    10. Tijana Matejić & Snežana Knežević & Vesna Bogojević Arsić & Tijana Obradović & Stefan Milojević & Miljan Adamović & Aleksandra Mitrović & Marko Milašinović & Dragoljub Simonović & Goran Milošević & Ma, 2022. "Assessing the Impact of the COVID-19 Crisis on Hotel Industry Bankruptcy Risk through Novel Forecasting Models," Sustainability, MDPI, vol. 14(8), pages 1-44, April.
    11. Youssef Zizi & Mohamed Oudgou & Abdeslam El Moudden, 2020. "Determinants and Predictors of SMEs’ Financial Failure: A Logistic Regression Approach," Risks, MDPI, vol. 8(4), pages 1-21, October.
    12. Iotti, Mattia, 2023. "Financial evaluation and credit access of agricultural firms," Economia agro-alimentare / Food Economy, Italian Society of Agri-food Economics/Società Italiana di Economia Agro-Alimentare (SIEA), vol. 25(2), October.
    13. Elena G. Shershneva, Min Zhou Hao, 2024. "Russian Banks Financial Stability Loss Diagnostic: Multidimensional Logit-Model Approach," Journal of Applied Economic Research, Graduate School of Economics and Management, Ural Federal University, vol. 23(2), pages 476-498.
    14. Fedorova, Elena & Ledyaeva, Svetlana & Drogovoz, Pavel & Nevredinov, Alexandr, 2022. "Economic policy uncertainty and bankruptcy filings," International Review of Financial Analysis, Elsevier, vol. 82(C).
    15. Li Xian Liu & Shuangzhe Liu & Milind Sathye, 2021. "Predicting Bank Failures: A Synthesis of Literature and Directions for Future Research," JRFM, MDPI, vol. 14(10), pages 1-24, October.
    16. Elena G. Shershneva, 2024. "CAMELS parameters’ impact on the risk of losing financial stability: The case of Russian banks," Journal of New Economy, Ural State University of Economics, vol. 25(2), pages 130-152, July.
    17. Black, Dirk & Neururer, Thaddeus, 2024. "Do analysts provide information about other comprehensive income in book value forecasts for financial firms?," Advances in accounting, Elsevier, vol. 64(C).
    18. Simone Poli & Marco Gatti, 2024. "The relevance of cash flow information in predicting corporate bankruptcy in Italian private companies," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2024(1), pages 179-202.
    19. Zhao, Qi & Xu, Weijun & Ji, Yucheng, 2023. "Predicting financial distress of Chinese listed companies using machine learning: To what extent does textual disclosure matter?," International Review of Financial Analysis, Elsevier, vol. 89(C).
    20. Mattia Iotti, 2023. "Financial evaluation and credit access of agricultural firms," Economia agro-alimentare, FrancoAngeli Editore, vol. 25(2), pages 31-67.

    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:kap:compec:v:62:y:2023:i:2:d:10.1007_s10614-022-10328-5. 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: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.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.