Effects of Accounting and Non-Accounting Indices on Financial Distress Prediction: Comparing Parametric and Non-parametric Methods

Document Type : Research Paper

Authors

1 Associate Professor of Accounting, Tehran university, Tehran, Iran

2 Associate Professor of Accounting, Mazandaran university, Babolsar, Iran

3 Phd. student of Accounting, Mazandaran university, Babolsar, Iran

Abstract

This study aims to investigate the effects of accounting and non-accounting indices on financial distress prediction and also to compare parametric and non-parametric methods. Therefore, the sample consists of 211 distressed firms selected by special distress criteria and 211 healthy firms listed in Tehran Stock Exchange during 2006-2015. This study applies 32 accounting and 20 non-accounting indices and uses 2 parametric methods including Logistic Regression and Multivariate Discriminate Analysis and 7 non-parametric methods including Support Vector Machine, Artificial Neural Network, Decision Tree (with 4 algorithms) and Bayesian Network to predict financial distress. The results show that the models extracted from accounting indices have significantly more predicting accuracy than those from non-accounting indices, and adding non-accounting indices to the models based on accounting indices does not significantly increases their predicting ability. Also, since the average predicting ability of non-parametric methods is more than parametric ones, this difference is not statistically significant.

Keywords

Main Subjects

References

Alizadeh, S. & Malek Mohamadi, S. (2012). Data mining & knowledge discovery. Tehran. Khajeh nasir toosi university of technology. (in Persian)
Bredart, X. (2014). Financial Distress and Corporate Governance around Lehman Brothers Bankruptcy. International Business Research, 7 (5): 1-8
Chen, M. Y. (2011). Predicting corporate financial distress based on integration of decision tree classification and logistic regression. Expert Systems with Applications, 38 (9): 11261-11272.
Chi, X. , Lou, C. , & Yu, X. (2011). Financial distress prediction based on SVM and MDA methods: the case of Chinese listed companies. Quality and Quantity, 45 (3): 671-686
Esmaeilzadeh Mogheri, A. & Shakeri, H. (2015). Financial distress prediction of companies accepted in Tehran stock exchange with using of naiva bayesian network and its comparison with data envelopment analysis. Financial Engineering and Securities Management. 6 (22): 1–27. (in Persian)
Etemadi, H. , Anvari Rostamy, A. A. , & Dehkordi, H. F. (2009). A genetic programming model for bankruptcy prediction: empirical evidence from Iran. Expert Systems with Applications, 36 (2) , 3199–3207.
Gestel, T. V. , Baesens, B. , Suykens, J. , Poel, D. V. , Baestaens, D. E. , & Willekens, M. (2006). Bayesian Kernel Based Classification For Financial Distress Detection. European Journal Of Operational Research, 172, 979 – 1003.
Hu, H. , & Sathye, M. (2015). Predicting Financial Distress in the Hong Kong Growth Enterprises Market from the Perspective of Financial Sustainability. Sustainability, 7, 1186-1200.
Jantadej, P. (2006). Using the combinations of cash flow components to predict financial distress. (Unpublished doctoral dissertation). The graduate college at the university of Nebraska , USA.
Khalifeh soltani, S. A. & Esmaili, F. (2014). Business Cycle and Stability of Bankruptcy Prediction Models. Journal of Empirical Research in Accounting, 4 (13): 1–22. (in Persian)
Kubíčková, D. & Nulíček, V. (2016). Predictors of Financial Distress and Bankruptcy Model Construction. International Journal of Management Science and Business Administration, 2 (6): 34-42.
Lee, M. (2015). Comparision of Wavelet Network and Logistics Regression in Predicting Enterprise Financial Distress. International Journal of Computer Science & Information Technology (IJCSIT) , 7 (3): 83-96.
Liu, Z. J. & Wang, Y. S. (2016). Corporate failure prediction models for advanced research in china: identifying the optimal cut off point. Asian Economic and Financial Review, 6 (1): 54-65.
Mansourfar, Gh. Ghayour, F. & Lotfi, B. (2015). The Ability of Support Vector Machine (SVM) in Financial Distress Prediction. Journal of Empirical Research in Accounting, 5 (1): 177-195. (in Persian)
Mohseni, R. Agha babee, R. & Mohammad Ghorbani, V. (2013). Financial Distress Prediction with the Use of the Efficiency as a Predictor Variable. Quarterly Journal of Economic Research and Policies, 21 (65): 123-146. (in Persian)
Mokhatab Rafiei, F. , Manzari, S. M. , & Bostanian, S. (2011). Financial health prediction models using artificial neural networks, genetic algorithm and multivariate discriminant analysis: Iranian evidence. Expert Systems with Applications, 38 (8): 10210–10217.
Moradi, M. Shafiee Sardasht, M. & Ebrahimipour, M. (2012). Bankruptcy prediction by support vector machines and multiple discriminate analysis models. Quarterly Journal of Securities Exchange. 5 (18): 113-136. (in Persian)
Opler, T. , & Titman, S. (1994). Financial Distress and Corporate Performance. The Journal of Finance, 49 (3): 1015-1040
Raie, R. & Fallahpour, S. (2009). Support vector machines application in financial distress prediction of companies using financial Ratios. The Iranian Accounting and Auditing Review, 15 (4): 17-34. (in Persian)
Sadeghi, H. Rahimi, P. & Salmani, Y. (2014). The effect of macroeconomic and governance factors on financial distress in manufacture firms listed in Tehran stock exchange. Financial Monetary Economics. 21 (8): 107-127. (in Persian)
Shleifer, A. , & Vishny, R. W. (1997). A survey of corporate governance. Journal of Finance, 52 (2): 737–783
Xu, X. , & Wang, Y. (2009). Financial Failure Prediction Using Efficiency as a Predictor. Expert Systems With Applications, 36,366-373.
Zohra, K. F. , Mohamed, B. , Elhamoud, T. , Garaibeh, M. , Ilhem, A. , & Naimi, H. (2015). Using Financial Ratios to Predict Financial Distress of Jordanian Industrial Firms,''Empirical Study Using Logistic Regression''. Academic Journal of Interdisciplinary Studies, 4 (2): 137-142
Empirical Research in Accounting
Volume 9, Issue 4 - Serial Number 34
February 2020
Pages 49-72

Files

History

  • Receive Date: 15 January 2017
  • Revise Date: 19 December 2017
  • Accept Date: 27 May 2017

Share

How to cite

Statistics