[go: up one dir, main page]
IDEAS home Printed from https://ideas.repec.org/p/unp/wpaman/201105.html
   My bibliography  Save this paper

Comparison Of Predicting Financial Distress Using Hazard Model Without And Incorporating Macroeconomic Variable As Baseline Hazard Rate

Author

Listed:
  • Denissa Satriavi

    (Department of Management and Business, Padjadjaran University)

Abstract
Hazard model was argued having advantages compared with previous financial distress prediction model because it can produce consistent and accurate estimates. In use, hazard model can include a form of macro-dependencies e.g. macroeconomic variables to capture the effects of the changes of macroeconomics, that have equal value for each sample, to the probability of financial distress, especially in times of crisis or economic recession. By using 95 samples and 40 hold-out sample of Indonesian firms listed in IDX on three industries (agribusiness, mining, and manufacturing) during the years 2000-2009, this study aims to determine what type of hazard model that most suitable applied to predict financial distress on crisis period in the economic climate such as Indonesia. The case of crisis study here is global financial crisis of 2008. Net income to total assets and working capital to total assets jointly built the ordinary hazards models. Working capital to total assets, losses its significance on hazards models that incorporating macroeconomic variables as baseline hazard rate. The results of this study indicate that the hazard model with macroeconomic variables as the baseline hazard rate is not always deliver a higher level of accuracy than the ordinary hazards models even in period of crisis. This can be a suggestion, that it is better first to analyze the magnitude of crisis’s impact on the of the economic foundations of a country. This is because, if the economy remains strong foundation, then the macroeconomic changes will have a little impact on a company's financial condition, particularly the real sector. The effects of crises on the company's financial health conditions more lead to indirect effects, rather than direct effects caused by changes in macroeconomic indicators as well.

Suggested Citation

  • Denissa Satriavi, 2011. "Comparison Of Predicting Financial Distress Using Hazard Model Without And Incorporating Macroeconomic Variable As Baseline Hazard Rate," Working Papers in Business, Management and Finance 201105, Department of Management and Business, Padjadjaran University, revised Dec 2011.
    • Handle: RePEc:unp:wpaman:201105
    as

    Download full text from publisher

    File URL: http://lp3e.fe.unpad.ac.id/wpaman/201105.pdf
    File Function: First version, 2011
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    2. Chae Woo Nam & Tong Suk Kim & Nam Jung Park & Hoe Kyung Lee, 2008. "Bankruptcy prediction using a discrete-time duration model incorporating temporal and macroeconomic dependencies," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 27(6), pages 493-506.
    3. 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.
    4. 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.
    5. 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.
    6. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    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. Maria H. Kim & Graham Partington, 2015. "Dynamic forecasts of financial distress of Australian firms," Australian Journal of Management, Australian School of Business, vol. 40(1), pages 135-160, February.
    2. 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.
    3. 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.
    4. Han Chulwoo & Kang Hyeongmook & Kim Gamin & Yi Joseph, 2012. "Logit Regression Based Bankruptcy Prediction of Korean Firms," Asia-Pacific Journal of Risk and Insurance, De Gruyter, vol. 7(1), pages 1-28, December.
    5. Mãdãlina Ecaterina POPESCU, 2015. "Proposal for a Decision Support System to Predict Financial Distress," REVISTA DE MANAGEMENT COMPARAT INTERNATIONAL/REVIEW OF INTERNATIONAL COMPARATIVE MANAGEMENT, Faculty of Management, Academy of Economic Studies, Bucharest, Romania, vol. 16(1), pages 112-118, March.
    6. 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.
    7. Alam, Nurul & Gao, Junbin & Jones, Stewart, 2021. "Corporate failure prediction: An evaluation of deep learning vs discrete hazard models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 75(C).
    8. 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.
    9. Ilyes Abid & Farid Mkaouar & Olfa Kaabia, 2018. "Dynamic analysis of the forecasting bankruptcy under presence of unobserved heterogeneity," Annals of Operations Research, Springer, vol. 262(2), pages 241-256, March.
    10. Arvind Shrivastava & Kuldeep Kumar & Nitin Kumar, 2018. "Business Distress Prediction Using Bayesian Logistic Model for Indian Firms," Risks, MDPI, vol. 6(4), pages 1-15, October.
    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. Jairaj Gupta & Andros Gregoriou & Jerome Healy, 2015. "Forecasting bankruptcy for SMEs using hazard function: To what extent does size matter?," Review of Quantitative Finance and Accounting, Springer, vol. 45(4), pages 845-869, November.
    13. Hyeongjun Kim & Hoon Cho & Doojin Ryu, 2020. "Corporate Default Predictions Using Machine Learning: Literature Review," Sustainability, MDPI, vol. 12(16), pages 1-11, August.
    14. 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.
    15. Giordani, Paolo & Jacobson, Tor & Schedvin, Erik von & Villani, Mattias, 2014. "Taking the Twists into Account: Predicting Firm Bankruptcy Risk with Splines of Financial Ratios," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 49(4), pages 1071-1099, August.
    16. 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).
    17. Ahsan Habib & Mabel D' Costa & Hedy Jiaying Huang & Md. Borhan Uddin Bhuiyan & Li Sun, 2020. "Determinants and consequences of financial distress: review of the empirical literature," Accounting and Finance, Accounting and Finance Association of Australia and New Zealand, vol. 60(S1), pages 1023-1075, April.
    18. Giesecke, Kay & Longstaff, Francis A. & Schaefer, Stephen & Strebulaev, Ilya, 2011. "Corporate bond default risk: A 150-year perspective," Journal of Financial Economics, Elsevier, vol. 102(2), pages 233-250.
    19. 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.
    20. Amin Jan & Maran Marimuthu & Muhammad Kashif Shad & Haseeb ur-Rehman & Muhammad Zahid & Ahmad Ali Jan, 2019. "Bankruptcy profile of the Islamic and conventional banks in Malaysia: a post-crisis period analysis," Economic Change and Restructuring, Springer, vol. 52(1), pages 67-87, February.

    More about this item

    Keywords

    model; financial ratio; macroeconomic variable; crisis;
    All these keywords.

    JEL classification:

    • E0 - Macroeconomics and Monetary Economics - - General
    • M0 - Business Administration and Business Economics; Marketing; Accounting; Personnel Economics - - General

    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:unp:wpaman:201105. 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: Aldrin Herwany (email available below). General contact details of provider: https://edirc.repec.org/data/dmpadid.html .

    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.