IDEAS home Printed from https://ideas.repec.org/a/wly/jforec/v43y2024i5p1374-1398.html
   My bibliography  Save this article

Incorporating media news to predict financial distress: Case study on Chinese listed companies

Author

Listed:
  • Lifang Zhang
  • Mohammad Zoynul Abedin
  • Zhenkun Liu

Abstract

Financial distress prediction has been a prominent research field for several decades. Accurate prediction of financial distress not only helps to safeguard the interests of investors but also improves the ability of managers to manage financial risks. Prior studies predominantly rely on accounting metrics derived from financial statements to predict financial distress. Our research takes a step further by incorporating media news to enhance the accuracy of financial distress prediction. Based on the data from Chinese listed companies, seven classifiers are established to verify the additional value of media news in improving the financial distress prediction performance of models. Experimental results demonstrate that the inclusion of media news in predictive models is effective as it contributes to better performance compared with models that solely rely on accounting features. Moreover, random forest model is a reliable tool in financial distress prediction due to its superior ability to capture complex feature relationships. Evaluation indicators, statistical tests, and Bayesian A/B tests further confirm that the inclusion of media news can significantly improve the identification of financially distressed companies.

Suggested Citation

  • Lifang Zhang & Mohammad Zoynul Abedin & Zhenkun Liu, 2024. "Incorporating media news to predict financial distress: Case study on Chinese listed companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1374-1398, August.
    • Handle: RePEc:wly:jforec:v:43:y:2024:i:5:p:1374-1398
    DOI: 10.1002/for.3089
    as

    Download full text from publisher

    File URL: https://doi.org/10.1002/for.3089
    Download Restriction: no
    File URL: https://libkey.io/10.1002/for.3089?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
    ---><---

    References listed on IDEAS

    as
    1. Ahmed Al‐Hadi & Bikram Chatterjee & Ali Yaftian & Grantley Taylor & Mostafa Monzur Hasan, 2019. "Corporate social responsibility performance, financial distress and firm life cycle: evidence from Australia," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 59(2), pages 961-989, June.
    2. Zhao, Shuping & Xu, Kai & Wang, Zhao & Liang, Changyong & Lu, Wenxing & Chen, Bo, 2022. "Financial distress prediction by combining sentiment tone features," Economic Modelling, Elsevier, vol. 106(C).
    3. Jiang, Ping & Liu, Zhenkun & Abedin, Mohammad Zoynul & Wang, Jianzhou & Yang, Wendong & Dong, Qingli, 2024. "Profit-driven weighted classifier with interpretable ability for customer churn prediction," Omega, Elsevier, vol. 125(C).
    4. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    5. Doumpos, Michalis & Andriosopoulos, Kostas & Galariotis, Emilios & Makridou, Georgia & Zopounidis, Constantin, 2017. "Corporate failure prediction in the European energy sector: A multicriteria approach and the effect of country characteristics," European Journal of Operational Research, Elsevier, vol. 262(1), pages 347-360.
    6. Jiang, Cuiqing & Lyu, Ximei & Yuan, Yufei & Wang, Zhao & Ding, Yong, 2022. "Mining semantic features in current reports for financial distress prediction: Empirical evidence from unlisted public firms in China," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1086-1099.
    7. Geng, Ruibin & Bose, Indranil & Chen, Xi, 2015. "Prediction of financial distress: An empirical study of listed Chinese companies using data mining," European Journal of Operational Research, Elsevier, vol. 241(1), pages 236-247.
    8. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    9. Janet Rosenbaum, 2010. "Bayesian Methods for Measures of Agreement," Journal of the Royal Statistical Society Series A, Royal Statistical Society, vol. 173(1), pages 270-270, January.
    10. 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.
    11. Hernandez Tinoco, Mario & Wilson, Nick, 2013. "Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables," International Review of Financial Analysis, Elsevier, vol. 30(C), pages 394-419.
    12. Liang, Deron & Lu, Chia-Chi & Tsai, Chih-Fong & Shih, Guan-An, 2016. "Financial ratios and corporate governance indicators in bankruptcy prediction: A comprehensive study," European Journal of Operational Research, Elsevier, vol. 252(2), pages 561-572.
    13. Hamid Reza Khedmatgozar & Arezoo Shahnazi, 2018. "The role of dimensions of perceived risk in adoption of corporate internet banking by customers in Iran," Electronic Commerce Research, Springer, vol. 18(2), pages 389-412, June.
    14. Balakrishnan, Ramji & Qiu, Xin Ying & Srinivasan, Padmini, 2010. "On the predictive ability of narrative disclosures in annual reports," European Journal of Operational Research, Elsevier, vol. 202(3), pages 789-801, May.
    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. Jiang, Cuiqing & Zhou, Yiru & Chen, Bo, 2023. "Mining semantic features in patent text for financial distress prediction," Technological Forecasting and Social Change, Elsevier, vol. 190(C).
    2. Soumya Ranjan Sethi & Dushyant Ashok Mahadik & Rajkiran V. Bilolikar, 2024. "Exploring Trends and Advancements in Financial Distress Prediction Research: A Bibliometric Study," International Journal of Economics and Financial Issues, Econjournals, vol. 14(1), pages 164-179, January.
    3. Mohammad Mahdi Mousavi & Jamal Ouenniche, 2018. "Multi-criteria ranking of corporate distress prediction models: empirical evaluation and methodological contributions," Annals of Operations Research, Springer, vol. 271(2), pages 853-886, December.
    4. Mai, Feng & Tian, Shaonan & Lee, Chihoon & Ma, Ling, 2019. "Deep learning models for bankruptcy prediction using textual disclosures," European Journal of Operational Research, Elsevier, vol. 274(2), pages 743-758.
    5. Yue Qiu & Jiabei He & Zhensong Chen & Yinhong Yao & Yi Qu, 2024. "A novel semisupervised learning method with textual information for financial distress prediction," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2478-2494, November.
    6. Ben Jabeur, Sami & Serret, Vanessa, 2023. "Bankruptcy prediction using fuzzy convolutional neural networks," Research in International Business and Finance, Elsevier, vol. 64(C).
    7. 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.
    8. Apostolos G. Katsafados & Dimitris Anastasiou, 2024. "Short-term prediction of bank deposit flows: do textual features matter?," Annals of Operations Research, Springer, vol. 338(2), pages 947-972, July.
    9. Zhou, Fanyin & Fu, Lijun & Li, Zhiyong & Xu, Jiawei, 2022. "The recurrence of financial distress: A survival analysis," International Journal of Forecasting, Elsevier, vol. 38(3), pages 1100-1115.
    10. Serrano-Cinca, Carlos & Gutiérrez-Nieto, Begoña & Bernate-Valbuena, Martha, 2019. "The use of accounting anomalies indicators to predict business failure," European Management Journal, Elsevier, vol. 37(3), pages 353-375.
    11. Mohammad Mahdi Mousavi & Jamal Ouenniche & Kaoru Tone, 2023. "A dynamic performance evaluation of distress prediction models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(4), pages 756-784, July.
    12. Salwa Kessioui & Michalis Doumpos & Constantin Zopounidis, 2023. "A Bibliometric Overview of the State-of-the-Art in Bankruptcy Prediction Methods and Applications," World Scientific Book Chapters, in: Emilios Galariotis & Alexandros Garefalakis & Christos Lemonakis & Marios Menexiadis & Constantin Zo (ed.), Governance and Financial Performance Current Trends and Perspectives, chapter 6, pages 123-153, World Scientific Publishing Co. Pte. Ltd..
    13. Lenka Papíková & Mário Papík, 2022. "Effects of classification, feature selection, and resampling methods on bankruptcy prediction of small and medium‐sized enterprises," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 29(4), pages 254-281, October.
    14. 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.
    15. Xiaobo Tang & Shixuan Li & Mingliang Tan & Wenxuan Shi, 2020. "Incorporating textual and management factors into financial distress prediction: A comparative study of machine learning methods," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 769-787, August.
    16. Deng, Shangkun & Luo, Qunfang & Zhu, Yingke & Ning, Hong & Shimada, Tatsuro, 2024. "Financial risk forewarning with an interpretable ensemble learning approach: An empirical analysis based on Chinese listed companies," Pacific-Basin Finance Journal, Elsevier, vol. 85(C).
    17. Alberto Tron & Maurizio Dallocchio & Salvatore Ferri & Federico Colantoni, 2023. "Corporate governance and financial distress: lessons learned from an unconventional approach," Journal of Management & Governance, Springer;Accademia Italiana di Economia Aziendale (AIDEA), vol. 27(2), pages 425-456, June.
    18. Eric Séverin & David Veganzones, 2021. "Can earnings management information improve bankruptcy prediction models?," Annals of Operations Research, Springer, vol. 306(1), pages 247-272, November.
    19. 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).
    20. Modina, Michele & Pietrovito, Filomena & Gallucci, Carmen & Formisano, Vincenzo, 2023. "Predicting SMEs’ default risk: Evidence from bank-firm relationship data," The Quarterly Review of Economics and Finance, Elsevier, vol. 89(C), pages 254-268.

    More about this item

    Statistics

    Access and download statistics

    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:wly:jforec:v:43:y:2024:i:5:p:1374-1398. 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: Wiley Content Delivery (email available below). General contact details of provider: http://www3.interscience.wiley.com/cgi-bin/jhome/2966 .

    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.