IDEAS home Printed from https://ideas.repec.org/a/eee/finana/v76y2021ics1057521921001174.html
   My bibliography  Save this article

Chinese corporate distress prediction using LASSO: The role of earnings management

Author

Listed:
  • Li, Chunyu
  • Lou, Chenxin
  • Luo, Dan
  • Xing, Kai

Abstract

Motivated by recently increasing accounting manipulation cases and deteriorating economic condition in China, we investigate the importance of a set of earnings management predictors and develop up-to-date distress prediction model with earnings management consideration for the Chinese market. Employing annual firm-level data from January 2014 to December 2018, we find that real earnings management (REM) is robustly selected out as a key distress predictor via the variable selection technique LASSO. Our results consistently show that REM could improve early warning of distressed companies with a slight sacrifice of accuracy in predicting healthy companies. After considering the cost of misclassification, it is confirmed that REM contains incremental information about a forthcoming corporate distress risk. Meanwhile, our results also detect an interesting finding that in China, aggressive real earnings management signals the lower probability of corporate distress, indicating distressed firms have lower capacity to conduct REM.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:finana:v:76:y:2021:i:c:s1057521921001174
    DOI: 10.1016/j.irfa.2021.101776
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1057521921001174
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irfa.2021.101776?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. Lisic, Ling Lei & Silveri, Sabatino (Dino) & Song, Yanheng & Wang, Kun, 2015. "Accounting fraud, auditing, and the role of government sanctions in China," Journal of Business Research, Elsevier, vol. 68(6), pages 1186-1195.
    2. Yasir Shahab & Collins G Ntim & Farid Ullah, 2019. "The brighter side of being socially responsible: CSR ratings and financial distress among Chinese state and non-state owned firms," Applied Economics Letters, Taylor & Francis Journals, vol. 26(3), pages 180-186, February.
    3. John Y. Campbell & Jens Hilscher & Jan Szilagyi, 2008. "In Search of Distress Risk," Journal of Finance, American Finance Association, vol. 63(6), pages 2899-2939, December.
    4. Lennox, Clive, 1999. "Identifying failing companies: a re-evaluation of the logit, probit and DA approaches," Journal of Economics and Business, Elsevier, vol. 51(4), pages 347-364, July.
    5. Holger Daske & Günther Gebhardt, 2006. "International financial reporting standards and experts’ perceptions of disclosure quality," Abacus, Accounting Foundation, University of Sydney, vol. 42(3‐4), pages 461-498, September.
    6. 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.
    7. Calomiris, Charles W. & Fisman, Raymond & Wang, Yongxiang, 2010. "Profiting from government stakes in a command economy: Evidence from Chinese asset sales," Journal of Financial Economics, Elsevier, vol. 96(3), pages 399-412, June.
    8. Jones, Jj, 1991. "Earnings Management During Import Relief Investigations," Journal of Accounting Research, Wiley Blackwell, vol. 29(2), pages 193-228.
    9. Edward I. Altman, 1968. "The Prediction Of Corporate Bankruptcy: A Discriminant Analysis," Journal of Finance, American Finance Association, vol. 23(1), pages 193-194, March.
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    11. Yasir Shahab & Collins G. Ntim & Ye Chengang & Farid Ullah & Samuel Fosu, 2018. "Environmental policy, environmental performance, and financial distress in China: Do top management team characteristics matter?," Business Strategy and the Environment, Wiley Blackwell, vol. 27(8), pages 1635-1652, December.
    12. Wolfgang Härdle & Yuh-Jye Lee & Dorothea Schäfer & Yi-Ren Yeh, 2009. "Variable selection and oversampling in the use of smooth support vector machines for predicting the default risk of companies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 28(6), pages 512-534.
    13. Liu, Qiao & Lu, Zhou (Joe), 2007. "Corporate governance and earnings management in the Chinese listed companies: A tunneling perspective," Journal of Corporate Finance, Elsevier, vol. 13(5), pages 881-906, December.
    14. Park, Trevor & Casella, George, 2008. "The Bayesian Lasso," Journal of the American Statistical Association, American Statistical Association, vol. 103, pages 681-686, June.
    15. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    16. Walter A. Chudson, 1945. "The Pattern of Corporate Financial Structure: A Cross-Section View of Manufacturing, Mining, Trade, and Construction, 1937," NBER Books, National Bureau of Economic Research, Inc, number chud45-1, March.
    17. Roychowdhury, Sugata, 2006. "Earnings management through real activities manipulation," Journal of Accounting and Economics, Elsevier, vol. 42(3), pages 335-370, December.
    18. Agarwal, Vineet & Taffler, Richard, 2008. "Comparing the performance of market-based and accounting-based bankruptcy prediction models," Journal of Banking & Finance, Elsevier, vol. 32(8), pages 1541-1551, August.
    19. 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.
    20. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    21. 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.
    22. Sudheer Chava & Robert A. Jarrow, 2008. "Bankruptcy Prediction with Industry Effects," World Scientific Book Chapters, in: Financial Derivatives Pricing Selected Works of Robert Jarrow, chapter 21, pages 517-549, World Scientific Publishing Co. Pte. Ltd..
    23. Altman, Edward I. & Haldeman, Robert G. & Narayanan, P., 1977. "ZETATM analysis A new model to identify bankruptcy risk of corporations," Journal of Banking & Finance, Elsevier, vol. 1(1), pages 29-54, June.
    24. Aharony, J & Lee, CWJ & Wong, TJ, 2000. "Financial packaging of IPO firms in China," Journal of Accounting Research, Wiley Blackwell, vol. 38(1), pages 103-126.
    25. Graham, John R. & Harvey, Campbell R. & Rajgopal, Shiva, 2005. "The economic implications of corporate financial reporting," Journal of Accounting and Economics, Elsevier, vol. 40(1-3), pages 3-73, December.
    26. Zhou, Fangzhao & Zhang, Zenan & Yang, Jun & Su, Yunpeng & An, Yunbi, 2018. "Delisting pressure, executive compensation, and corporate fraud: Evidence from China," Pacific-Basin Finance Journal, Elsevier, vol. 48(C), pages 17-34.
    27. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    28. Zou, Hui, 2006. "The Adaptive Lasso and Its Oracle Properties," Journal of the American Statistical Association, American Statistical Association, vol. 101, pages 1418-1429, December.
    29. Jianguo Chen & Ben R. Marshall & Jenny Zhang & Siva Ganesh, 2006. "Financial Distress Prediction in China," Review of Pacific Basin Financial Markets and Policies (RPBFMP), World Scientific Publishing Co. Pte. Ltd., vol. 9(02), pages 317-336.
    30. Kuo, Jing-Ming & Ning, Lutao & Song, Xiaoqi, 2014. "The Real and Accrual-based Earnings Management Behaviors: Evidence from the Split Share Structure Reform in China," The International Journal of Accounting, Elsevier, vol. 49(1), pages 101-136.
    31. Blum, M, 1974. "Failing Company Discriminant-Analysis," Journal of Accounting Research, Wiley Blackwell, vol. 12(1), pages 1-25.
    32. 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).
    33. Lin, Hsiou-Wei William & Lo, Huai-Chun & Wu, Ruei-Shian, 2016. "Modeling default prediction with earnings management," Pacific-Basin Finance Journal, Elsevier, vol. 40(PB), pages 306-322.
    34. Katherine A. Gunny, 2010. "The Relation Between Earnings Management Using Real Activities Manipulation and Future Performance: Evidence from Meeting Earnings Benchmarks," Contemporary Accounting Research, John Wiley & Sons, vol. 27(3), pages 855-888, September.
    35. 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.
    36. Fan, Joseph P. H. & Wong, T. J., 2002. "Corporate ownership structure and the informativeness of accounting earnings in East Asia," Journal of Accounting and Economics, Elsevier, vol. 33(3), pages 401-425, August.
    37. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    38. Zmijewski, Me, 1984. "Methodological Issues Related To The Estimation Of Financial Distress Prediction Models," Journal of Accounting Research, Wiley Blackwell, vol. 22, pages 59-82.
    39. Bekaert, Geert, 1995. "Market Integration and Investment Barriers in Emerging Equity Markets," The World Bank Economic Review, World Bank, vol. 9(1), pages 75-107, January.
    40. Cohen, Daniel A. & Zarowin, Paul, 2010. "Accrual-based and real earnings management activities around seasoned equity offerings," Journal of Accounting and Economics, Elsevier, vol. 50(1), pages 2-19, May.
    41. Gao, Jie & Gao, Baichao & Wang, Xiao, 2017. "Trade-off between real activities earnings management and accrual-based manipulation-evidence from China," Journal of International Accounting, Auditing and Taxation, Elsevier, vol. 29(C), pages 66-80.
    42. Shumway, Tyler, 2001. "Forecasting Bankruptcy More Accurately: A Simple Hazard Model," The Journal of Business, University of Chicago Press, vol. 74(1), pages 101-124, January.
    43. Tian, Shaonan & Yu, Yan & Guo, Hui, 2015. "Variable selection and corporate bankruptcy forecasts," Journal of Banking & Finance, Elsevier, vol. 52(C), pages 89-100.
    44. Ilia D. Dichev, 1998. "Is the Risk of Bankruptcy a Systematic Risk?," Journal of Finance, American Finance Association, vol. 53(3), pages 1131-1147, June.
    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. He, Wenjian & Cheng, Yu & Lin, Ying & Zhang, Hongxiao, 2022. "Microeconomic effects of designating National Forest Cities: Evidence from China's publicly traded manufacturing companies," Forest Policy and Economics, Elsevier, vol. 136(C).
    2. Xing, Kai & Luo, Dan & Liu, Lanlan, 2023. "Macroeconomic conditions, corporate default, and default clustering," Economic Modelling, Elsevier, vol. 118(C).
    3. Ioannis Dokas & Christos Leontidis & Nicolaos Eriotis & Konstantinos Hazakis, 2021. "Earnings Management. An overview of the relative literature," Bulletin of Applied Economics, Risk Market Journals, vol. 8(2), pages 25-55.
    4. 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.

    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. B Korcan Ak & Patricia M Dechow & Yuan Sun & Annika Yu Wang, 2013. "The use of financial ratio models to help investors predict and interpret significant corporate events," Australian Journal of Management, Australian School of Business, vol. 38(3), pages 553-598, December.
    2. 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.
    3. 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.
    4. 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.
    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. Paulo V. Carvalho & José D. Curto & Rodrigo Primor, 2022. "Macroeconomic determinants of credit risk: Evidence from the Eurozone," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(2), pages 2054-2072, April.
    7. Bai, Qing & Tian, Shaonan, 2020. "Innovate or die: Corporate innovation and bankruptcy forecasts," Journal of Empirical Finance, Elsevier, vol. 59(C), pages 88-108.
    8. Evangelos C. Charalambakis & Ian Garrett, 2016. "On the prediction of financial distress in developed and emerging markets: Does the choice of accounting and market information matter? A comparison of UK and Indian Firms," Review of Quantitative Finance and Accounting, Springer, vol. 47(1), pages 1-28, July.
    9. 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.
    10. Evangelos C. Charalambakis & Ian Garrett, 2019. "On corporate financial distress prediction: What can we learn from private firms in a developing economy? Evidence from Greece," Review of Quantitative Finance and Accounting, Springer, vol. 52(2), pages 467-491, February.
    11. Wu, Y. & Gaunt, C. & Gray, S., 2010. "A comparison of alternative bankruptcy prediction models," Journal of Contemporary Accounting and Economics, Elsevier, vol. 6(1), pages 34-45.
    12. Tian, Shaonan & Yu, Yan, 2017. "Financial ratios and bankruptcy predictions: An international evidence," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 510-526.
    13. Adriana Csikosova & Maria Janoskova & Katarina Culkova, 2020. "Application of Discriminant Analysis for Avoiding the Risk of Quarry Operation Failure," JRFM, MDPI, vol. 13(10), pages 1-14, September.
    14. Hamid Waqas & Rohani Md-Rus, 2018. "Predicting financial distress: Applicability of O-score model for Pakistani firms," Business and Economic Horizons (BEH), Prague Development Center, vol. 14(2), pages 389-401, April.
    15. Duc Hong Vo & Binh Ninh Vo Pham & Chi Minh Ho & Michael McAleer, 2019. "Corporate Financial Distress of Industry Level Listings in Vietnam," JRFM, MDPI, vol. 12(4), pages 1-17, September.
    16. Stewart Jones, 2017. "Corporate bankruptcy prediction: a high dimensional analysis," Review of Accounting Studies, Springer, vol. 22(3), pages 1366-1422, September.
    17. Dong, Manh Cuong & Tian, Shaonan & Chen, Cathy W.S., 2018. "Predicting failure risk using financial ratios: Quantile hazard model approach," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 204-220.
    18. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    19. 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.
    20. Frank Ranganai Matenda & Mabutho Sibanda & Eriyoti Chikodza & Victor Gumbo, 2022. "Bankruptcy prediction for private firms in developing economies: a scoping review and guidance for future research," Management Review Quarterly, Springer, vol. 72(4), pages 927-966, December.

    More about this item

    Keywords

    Corporate distress; Earnings management; Financial information;
    All these keywords.

    JEL classification:

    • G17 - Financial Economics - - General Financial Markets - - - Financial Forecasting and Simulation
    • G33 - Financial Economics - - Corporate Finance and Governance - - - Bankruptcy; Liquidation

    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:eee:finana:v:76:y:2021:i:c:s1057521921001174. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/inca/620166 .

    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.