IDEAS home Printed from https://ideas.repec.org/a/wly/coacre/v9y1993i2p667-694.html
   My bibliography  Save this article

Financial Ratios and Corporate Endurance: A Case of the Oil and Gas Industry

Author

Listed:
  • KEVIN C.W. CHEN
  • CHI†WEN JEVONS LEE

Abstract

. A major function of financial statement analysis is to assess the risk of financial distress. Since Beaver's (1966) and Altaian's (1968) pioneering works, voluminous studies have been devoted to exploring the use of accounting information in predicting business failure. We apply survival analysis to study a class of financial distress when a financial analyst can identify an event that sets off the dynamic process of business adversity and would like to find out how long a firm can endure the adversity. We use the case of the oil and gas industry during the turmoil of the early 1980s and apply survival analysis to study how long a firm can endure this drastic oil price decline before facing financial distress. Our results indicate that the liquidity ratio, leverage ratio, operating cash flows, success in exploration, age, and size are significant factors affecting corporate endurance. Résumé. Une fonction majeure de l'analyse des états financiers consiste à évaluer le risque de difficultés financières. Depuis les travaux d'amorce de Beaver et Altman, de volumineuses études ont été consacrées à l'analyse approfondie de l'utilisation de l'information comptable dans la prédiction des faillites d'entreprises. Les auteurs appliquent l'analyse de survie à l'étude d'une catégorie de difficultés financières pour laquelle l'analyste financier parvient à déterminer un événement qui déclenche le processus dynamique des difficultés de l'entreprise et aimerait déterminer pendant combien de temps cette dernière pourra résister à ces difficultés. Les auteurs évoquent le cas du secteur pétrolier et gazier au cours de la période tumultueuse du début des années 80 et appliquent l'analyse de survie à l'étude du temps pendant lequel une entreprise pouvait résister à un déclin radical du prix du pétrole avant d'éprouver des difficultés financières. Les résultats de l'étude démontrent que le ratio de liquidité, le ratio de levier, les flux monétaires provenant de l'exploitation, le succès des activités d'exploration, l'âge et la taille de l'entreprise sont des facteurs importants qui influent sur sa résistance.

Suggested Citation

  • Kevin C.W. Chen & Chi†Wen Jevons Lee, 1993. "Financial Ratios and Corporate Endurance: A Case of the Oil and Gas Industry," Contemporary Accounting Research, John Wiley & Sons, vol. 9(2), pages 667-694, March.
  • Handle: RePEc:wly:coacre:v:9:y:1993:i:2:p:667-694
    DOI: 10.1111/j.1911-3846.1993.tb00903.x
    as

    Download full text from publisher

    File URL: https://doi.org/10.1111/j.1911-3846.1993.tb00903.x
    Download Restriction: no

    File URL: https://libkey.io/10.1111/j.1911-3846.1993.tb00903.x?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. Morck, Randall & Shleifer, Andrei & Vishny, Robert W., 1988. "Management ownership and market valuation," Scholarly Articles 29407535, Harvard University Department of Economics.
    2. Deakin, Eb, 1972. "Discriminant Analysis Of Predictors Of Business Failure," Journal of Accounting Research, Wiley Blackwell, vol. 10(1), pages 167-179.
    3. Shrieves, Ronald E. & Stevens, Donald L., 1979. "Bankruptcy Avoidance as a Motive For Merger," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 14(3), pages 501-515, September.
    4. 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.
    5. Dhaliwal, Dan S. & Salamon, Gerald L. & Dan Smith, E., 1982. "The effect of owner versus management control on the choice of accounting methods," Journal of Accounting and Economics, Elsevier, vol. 4(1), pages 41-53, July.
    6. 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.
    7. Ohlson, Ja, 1980. "Financial Ratios And The Probabilistic Prediction Of Bankruptcy," Journal of Accounting Research, Wiley Blackwell, vol. 18(1), pages 109-131.
    8. 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.
    9. repec:bla:jfinan:v:44:y:1989:i:4:p:909-22 is not listed on IDEAS
    10. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure - Reply," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 123-127.
    11. repec:bla:jfinan:v:44:y:1989:i:2:p:375-92 is not listed on IDEAS
    12. Heckman, James J. & Singer, Burton, 1984. "Econometric duration analysis," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 63-132.
    13. Morck, Randall & Shleifer, Andrei & Vishny, Robert W., 1988. "Management ownership and market valuation : An empirical analysis," Journal of Financial Economics, Elsevier, vol. 20(1-2), pages 293-315, January.
    14. Beaver, Wh, 1966. "Financial Ratios As Predictors Of Failure," Journal of Accounting Research, Wiley Blackwell, vol. 4, pages 71-111.
    15. Schipper, K, 1977. "Financial Distress In Private Colleges," Journal of Accounting Research, Wiley Blackwell, vol. 15, pages 1-53.
    16. Lancaster, Tony, 1979. "Econometric Methods for the Duration of Unemployment," Econometrica, Econometric Society, vol. 47(4), pages 939-956, 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. Chih‐Chun Chen & Chun‐Da Chen & Donald Lien, 2020. "Financial distress prediction model: The effects of corporate governance indicators," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(8), pages 1238-1252, December.
    2. 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.
    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. Ali DERAN & Omer ISKENDEROGLU & Incilay ERDURU, 2014. "Regional Differences and Financial Ratios: A Comparative Approach on Companies of ISE City Indexes," International Journal of Economics and Financial Issues, Econjournals, vol. 4(4), pages 946-955.
    5. Sanjay Sehgal & Ritesh Kumar Mishra & Ajay Jaisawal, 2021. "A search for macroeconomic determinants of corporate financial distress," Indian Economic Review, Springer, vol. 56(2), pages 435-461, December.
    6. 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.
    7. Soo Young Kim, 2018. "Predicting hospitality financial distress with ensemble models: the case of US hotels, restaurants, and amusement and recreation," Service Business, Springer;Pan-Pacific Business Association, vol. 12(3), pages 483-503, September.
    8. fernández, María t. Tascón & gutiérrez, Francisco J. Castaño, 2012. "Variables y Modelos Para La Identificación y Predicción Del Fracaso Empresarial: Revisión de La Investigación Empírica Reciente," Revista de Contabilidad - Spanish Accounting Review, Elsevier, vol. 15(1), pages 7-58.
    9. Khoja, Layla & Chipulu, Maxwell & Jayasekera, Ranadeva, 2019. "Analysis of financial distress cross countries: Using macroeconomic, industrial indicators and accounting data," International Review of Financial Analysis, Elsevier, vol. 66(C).
    10. Emil Exenberger & Michaela Kav?áková, 2020. "Evaluation of financial health of companies through data envelopment analysis: Selection of variables for the DEA model in R," Proceedings of Economics and Finance Conferences 10913067, International Institute of Social and Economic Sciences.
    11. Ben Jabeur, Sami, 2017. "Bankruptcy prediction using Partial Least Squares Logistic Regression," Journal of Retailing and Consumer Services, Elsevier, vol. 36(C), pages 197-202.
    12. Chien-Min Kang & Ming-Chieh Wang & Lin Lin, 2022. "Financial Distress Prediction of Cooperative Financial Institutions—Evidence for Taiwan Credit Unions," IJFS, MDPI, vol. 10(2), pages 1-25, April.
    13. Fayçal Mraihi & Inane Kanzari & Mohamed Tahar Rajhi, 2015. "Development of a Prediction Model of Failure in Tunisian Companies: Comparison between Logistic Regression and Support Vector Machines," International Journal of Empirical Finance, Research Academy of Social Sciences, vol. 4(3), pages 184-205.
    14. Jackson, Richard H.G. & Wood, Anthony, 2013. "The performance of insolvency prediction and credit risk models in the UK: A comparative study," The British Accounting Review, Elsevier, vol. 45(3), pages 183-202.
    15. Bhanu Pratap Singh & Alok Kumar Mishra, 2016. "Re-estimation and comparisons of alternative accounting based bankruptcy prediction models for Indian companies," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 2(1), pages 1-28, December.
    16. Andrzej Geise & Magdalena Kuczmarska & Jarosław Pawlowski, 2021. "Corporate Failure Prediction of Construction Companies in Poland: Evidence from Logit Model," European Research Studies Journal, European Research Studies Journal, vol. 0(1), pages 99-116.
    17. 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).
    18. 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.
    19. Enrico Supino & Nicola Piras, 2022. "Le performance dei modelli di credit scoring in contesti di forte instabilit? macroeconomica: il ruolo delle Reti Neurali Artificiali," MANAGEMENT CONTROL, FrancoAngeli Editore, vol. 2022(2), pages 41-61.
    20. Trueck, Stefan & Rachev, Svetlozar T., 2008. "Rating Based Modeling of Credit Risk," Elsevier Monographs, Elsevier, edition 1, number 9780123736833.

    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:coacre:v:9:y:1993:i:2:p:667-694. 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: https://doi.org/10.1111/(ISSN)1911-3846 .

    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.