پیش‌بینی ریسک ورشکستگی مالی با استفاده از مدل ترکیبی در بورس اوراق بهادار تهران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار گروه مدیریت بازرگانی، دانشگاه قم

2 کارشناسی‌ارشد مدیریت بازرگانی گرایش مالی، دانشکده مدیریت دانشگاه قم

چکیده

پیش‌بینی ریسک ورشکستگی مالی یکی از مهمترین موضوعات در حوزه تصمیم‌گیری مالی شرکت‌ها است. از این جهت، تاکنون مدل‌های متنوعی که هرکدام از نظر متغیرهای پیش‌بینی‌کننده و تکنیک‌ها متفاوتند، ارائه شده‌اند. استفاده از ترکیب متغیرهای حسابداری و بازاری در مدل به عنوان ورودی، قطعاً بر نتایج و دقت پیش‌بینی‌ها تاثیر مستقیمی خواهد داشت. در این مطالعه، پیش‌بینی با استفاده از مدل ترکیبی (استفاده از متغیرهای حسابداری و بازاری ) و تکنیک شبکه‌های عصبی از نوع مدل پرسپترون چندلایه (MLP) صورت پذیرفت. نمونه پژوهش شامل 90 شرکت پذیرفته شده در بورس اوراق بهادار تهران (31 شرکت ورشکسته طبق ماده 141 قانون تجارت ایران و 59 شرکت غیرورشکسته) طی سال‌های 1393-1386 می‌باشد. نتایج پژوهش نشان می‌دهد که مدل ترکیبی (ترکیب متغیرهای حسابداری و بازاری) با استفاده از تکنیک شبکه عصبی، نسبت به هر کدام از دو مدل حسابداری و بازاری از دقت بالاتری در پیش‌بینی ریسک ورشکستگی مالی برخوردار است. همچنین، مدل بازاری نیز دقت بیشتری نسبت به مدل حسابداری دارد.

کلیدواژه‌ها


عنوان مقاله [English]

The Prediction of the Risk of Financial Bankruptcy Using Hybrid Model in Tehran Stock Exchange

نویسندگان [English]

  • Najmeh Ramooz 1
  • Maryam Mahmoudi 2
1 qom university
2 qom university
چکیده [English]

Predicting the risk of financial bankruptcy is one of the most important issues in the field of companies’ financial decision. Accordingly, a variety of models that each is different in terms of predictor variables and techniques has been introduced so far. The use of the combination of accounting and market-driven variables in the model as input will have definitely a direct impact on the results and accuracy of forecasts. In this study, the prediction was accomplished by using a hybrid model (the use of accounting and market-driven variables) and neural networks technique of multi-layer perceptron model (MLP). The sample of research consists of 90 accepted companies in Tehran Stock Exchange (31 bankrupted companies in accordance with article Iran’s 141 trade laws and 59 non-bankrupted companies) during 2007-2014 period. The research results show that the hybrid model (combination of accounting and market-driven variables) using neural network technique has higher accuracy than each of the two accounting models and market-driven model in predicting the risk of financial bankruptcy. Likewise, the market-driven model is more accurate than accounting model.

کلیدواژه‌ها [English]

  • Risk of Financial Bankruptcy
  • Accounting Variables
  • Market-Driven Variables
  • Hybrid Model
  • Neural Network
-       Ahmadpour, A. and Mirzayi Asromi, H. (2013). »Compared with Multiple Discriminate Analysis Model and neural network Models in Predicting Bankruptcy of the listed Companies in Tehran Stock Exchange«. Auditing and accounting researches, Vol. 4, No. 19, pp. 2-31. [In Persian]
-       Alizadeh, V., Ghasemi, A. and Rahnamaflaverjany, R. (2015). » A model to predict the risk of bankruptcy of companies using Multi Layer Perceptron (MLP) Neural Network with Supervision by Genetic Algorithm«. National conference on new approaches in science of management, economics and accounting, Mazandaran, Research Institute of Koomeh Elmavarane Danesh. [In Persian]
-       Altman, E. I. and Hotchkiss, E. (2010). Corporate financial distress and bankruptcy: Predict and avoid bankruptcy, analyze and invest in distressed debt, John Wiley & Sons, Vol. 289.
-       Atefatdoost, A. and Faqih, N. (2005). Artificial intelligence to predict Stop the production line (application of artificial neural networks), Tehran, the ZAR Publications. [In Persian]
-       Bauer, J. (2012). »Bankruptcy Risk Prediction and Pricing: Unravelling the Negative Distress Risk Premium. PHD Thesis, Cranfield University.
-       Campbell, J. Y., Hilscher, J., and Szilagyi, J. (2008). »In search of distress risk«. The Journal of Finance, Vol. 63, No. 6, pp. 2899-2939.
-       Christidis, A. And Gregory, A. (2010). »Some new models for financial distress prediction in the UK«. Xfi-Centre for Finance and Investment Discussion, Paper 10.
-       Danilov, K. (2014). »Corporate Bankruptcy: Assessment, Analysis and Prediction of Financial Distress, Insolvency, and Failure«. Master's Thesis, University of Massachusetts Amherst.
-       Das, S. R., Hanouna, P., and Sarin, A. (2007). »Accounting-based versus market-based cross-sectional models of CDS spreads«. Journal of Banking & Finance, Vol. 33, No. 4, pp. 719-730.
-       Fadayinezhad, M. and Eskandari, R. (2011). » Designing and explaining model to predict bankruptcy in Tehran Stock Exchange«. Auditing and accounting researches, Vol. 3, No. 9, pp. 38-55. [In Persian]
-       Fedorova, E., Gilenko, E., and Dovzhenko, S. (2013). »Bankruptcy prediction for Russian companies: Application of combined classifiers«. Expert systems with applications, Vol. 40, No. 18, pp. 7285-7293.
-       Feyz Mohammadi, R. (2014). »Financial Distress and Bankruptcy Prediction of Tehran Securities and Exchange ̓s Firms using Accounting, Market and Macroeconomic Variables«. Master's Thesis, Tarbiat Modares University. [In Persian]
-       Haseli, J. (2011). »A comparative study of bankruptcy prediction models of Shumway and Ohlson in Listed Companies on Tehran Stock Exchange«. Master's Thesis, Islamic Azad University, Kermanshah. [in Persian]
-       Iturriaga, F. J. L., & Sanz, I. P. (2015). »Bankruptcy visualization and prediction using neural networks: A study of US commercial banks«. Expert Systems with applications, Vol. 42, No. 6, pp. 2857-2869.
-       Jahangir, M. (2015). Commercial law with the law of e-commerce, law and anti-money laundering regulations, new law of cheque of 1382/6/2, corrective Regulations for registration non-commercial organizations and institutions, Tehran, the DIDAR Publications, edition. 140. [In Persian]
-       Jenkins, A. S., Wiklund, J., & Brundin, E. (2014). »Individual responses to firm failure: Appraisals, grief, and the influence of prior failure experience«. Journal of Business Venturing, Vol. 29, No. 1, pp. 17-33.
-       Komeijani, A. and Saadatfar, J. (2006). «The application of neural network models to predict economic bankruptcy in stock market companies». Economic Essays, Vol. 3, No. 6, pp. 11-43. [In Persian]
-       Kordestani, G., Tatli, R. and Kosarifar, H. (2014). «Assessing the predictive power of the model modified Altman from levels of Newton financial distress and bankruptcy». Journal of knowledge of investments, Vol. 3, No. 9, pp. 83-100. [In Persian]
-       López-Gutiérrez, C., Sanfilippo-Azofra, S., & Torre-Olmo, B. (2015). »Investment decisions of companies in financial distress«. BRQ Business Research Quarterly, Vol. 18, No. 3, pp. 174-187.
-       Makiyan, N., Almodarresi, M. and Karimi Takallo, S. (2010). «Comparing model of artificial neural networks with discriminant analysis and logistic regression methods to predict bankruptcy of companies». Journal of economic researches, Vol. 10, No. 2, pp. 141-161. [In Persian]
-       Martin, S. And Peat, M. (2009). »A Comparison of the Information Content of Accounting and Market Measures in Distress Prediction«. INFINITI Conference on International Finance.
-       Pourzamani, Z. and Pouyanrad, M. (2012). »The relationship between earnings management and insolvency«. Financial knowledge of Analysis of Securities, Vol. 5, No. 16, pp. 77-88. [In Persian]
-       Salehi, M. and Bzrgr, H. (2015). » The relationship between earnings quality and bankruptcy«. The Journal of financial management strategy, Vol. 3, No. 8, pp. 81-108. [In Persian]
-       Sánchez, C. P. , de Llano Monelos, P., & López, M. R. (2013). »A parsimonious model to forecast financial distress, based on audit evidence«. Contaduría y Administración, Vol. 58, No. 4, pp. 151-173.
-       Sayari, N., & Mugan, C. S. (2016). »Industry specific financial distress modeling«. BRQ Business Research Quarterly, Vol. 20, No. 1, pp. 45-62.
-       Shumway, T. (2001). »Forecasting bankruptcy more accurately: A simple hazard model«. The Journal of Business, Vol. 74, No. 1, pp. 101-124.
-       Tinoco Hernandez, M. And Wilson, N. (2013). »Financial distress and bankruptcy prediction among listed companies using accounting, market and macroeconomic variables«. International Review of Financial Analysis, No. 30, pp. 394-419.
-       Vassalou, M. And Xing, Y. (2004). »Default risk in equity returns«. The Journal of Finance, Vol. 59, No. 2, pp. 831-868