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Journals / SOSYAL BİLİMLER METİNLERİ / 2011 / Cilt: 2011 - Sayı: 2

AN EMPIRICAL STUDY TO MODEL CORPORATE FAILURES IN TURKEY: A MODEL PROPOSAL USING MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS)

Mehmet Sabri TOPAK
Pages
1–21
Publication Date
2011-12-01
DOI
—
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The final model has an overall rate of correct classification of 81,8 % and is proved to be significantly superior to a naïve model.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This paper is prepared to model financial distress cases in Turkey using a non-parametric technique, Multivariate Adaptive Regression Splines (MARS).
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Key Findings & Data:For this purpose, a sample of 114 firms with 665 annual observations between the years 1994 and 2003 was used to predict financial distress for one year prior failure.
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Conclusion & Impact:The final model has an overall rate of correct classification of 81,8 % and is proved to be significantly superior to a naïve model.
🔬Design:Modeling & Simulation
📍Scope:2003 Verileri
⚙️Variables:Financial Distress Prediction · Non-Parametric Modeling · Multivariate Adaptive Regression Splines

Özet

This paper is prepared to model financial distress cases in Turkey using a non-parametric technique, Multivariate Adaptive Regression Splines (MARS). For this purpose, a sample of 114 firms with 665 annual observations between the years 1994 and 2003 was used to predict financial distress for one year prior failure. Our modeling study on 41 independent variables, 39 financial data-based and 2 non-financial, has resulted in a condensed model including 10 basis functions based on 8 original variables. The final model has an overall rate of correct classification of 81,8 % and is proved to be significantly superior to a naïve model. Its Type I and Type II performances are respectively 91,5 % and 80,9 %. Furthermore, profitability performance, capital structure decisions, and macroeconomic conditions are found to be the major determinants that influence Turkish firms’ risk profiles.

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Cite This Article

TOPAK, M. S. (2011). AN EMPIRICAL STUDY TO MODEL CORPORATE FAILURES IN TURKEY: A MODEL PROPOSAL USING MULTIVARIATE ADAPTIVE REGRESSION SPLINES (MARS). SOSYAL BİLİMLER METİNLERİ, Cilt: 2011 - Sayı: 2, 1–21. https://ajindex.com/articles/421759

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References (8)

  1. Altman, Edward I., “Financial Ratios, Discriminant Analysis, and Prediction of Corporate Bankruptcy”, Journal of Finance, Vol:4, 1968, pp.589 – 609. Altman, Edward I., Corporate Financial Distress: A Complete Guide to Predicting, Avoiding, and Dealing with Bankruptcy, New York: John Wiley and Sons, 1993.
  2. Andrade, Gregor and Steven N.Kaplan, “How Costly is Financial (Not Economic) Distress?: Evidence from Highly Leveraged Transactions that Became Distressed”, Journal of Finance, Vol: 53, Issue: 5, 1998, pp.1443 – 1493. Aziz, Abdul, David C.Emanuel and Gerald H.Lawson, “Bankruptcy Prediction – An Investigation of Cash Flow-Based Models” Journal of Management Studies, Vol:25, Issue:5, 1988, pp.419 – 437. Beaver, William H., “Financial Ratios as Predictors of Failures”, Journal of Accounting Research, 1966, pp.71 – 111. Blum, Marc, “Failing Company Discriminant Analysis”, Journal of Accounting Research, Vol: 13, 1974, pp. 1 – 25. Boritz, J., “The Going-Concern Assumption: Accounting and Auditing Implications”, Research Report, Canadian Institute of Chartered Accountants (CICA), 1991.
  3. Brealey, Richard A. and Stewart C.Myers, Principles of Corporate Finance, 2nd Edition, New York: Dryden Press, 1984.
  4. Brigham, Eugene F. and Louis C. Gapenski, Financial Management: Theory & Practice, 8th Edition, Fort Worth: Dryden Press, 1997.
  5. Casey, Cornelius and Norman Bartczak, “Using Operating Cash Flow Data to Predict Financial Distress: Some Extensions”, Journal of Accounting Research, Vol: 23, Issue:1, 1985, pp.384 – 401. Charitou, Andreas and Lenos Trigeorgis, “Option-Based Bankruptcy Prediction”, 1996, http://papers.ssrn.com/paper.
  6. Clark, Truman A. and Mark L.Weinstein, “The Behavior of Common Stocks of Bankrupt Firms”, Journal of Finance, Vol: 38, Issue: 2, 1983, pp. 489 – 504. Coats, Pamela K. ve J.Franklin Fant, “Recognizing Financial Distress Patterns Using a Neural Network Tool”, Financial Management, Autumn 1993, p.152. Craven, P. ve G.Wabha, "Smoothing Noisy Data with Spline Functions – Estimating the Correct Degree of Smoothing by Method of Generalized Cross- Validation", Numerische Mathematik, Vol: 31, Issue: 4, pp.317-403.
  7. Deakin, Edward B., “A Discriminant Analysis of Predictors of Business Failure”, Journal of Accounting Research, Vol: 10, Issue: 1, pp.167 – 179. Friedman, Jerome H., "Multivariate Adaptive Regression Splines", Annals of Statistics, Vol: 19, Issue: 1, 1991, pp.1-67.
  8. Frydman, H., Edward Altman and D.Kao, “Introducing the Recursive Partitioning