FİNANSAL KESİMİN GİDERİLMESİ İÇİN MAKİNA ÖĞRENME MODELLERİ

Öz

İş likiditesindeki zorluklar ve bunun sonucunda ortaya çıkan finansal sıkıntı genellikle aşırı maliyetli ve yıkıcı bir olaydır. Bu nedenle, bu çalışma bir şirketin sürdürülebilirliğini tahmin etmemize yardımcı olabilecek bir dizi özellik sunmaya çalışmaktadır. Bu çalışma, bir dizi şirketin tarihi kesin hesapları (3 ile 5 yıl arasında değişen) üzerinde eğitim aldıktan sonra, diğer modellerin finansal verilerinin niteliğini değerlendirebilecek bir finansal tahmin sisteminin oluşturulmasını içermektedir. Sonuç olarak, aşağıdaki finansal dönemde şirketin finansal durumu tahmin edilir (firmanın aktif olup olmadığı). Firmanın mali sağlığı tahmin edildikten sonra, Karar Ağacı, Naïve Bayes sınıflandırıcı ve Yapay Sinirsel Ağ’ın çıktıları, bu problem için bir dizi özellik bulmakta en doğru algoritmanın hangileri olduğunu görmek için değerlendirilir. Gerçek hayattaki veri kümeleri üzerindeki araştırma bulguları, önerilen modelin seçkin iş başarısızlığını tahmin etmedeki gücünü ve kabiliyetini doğrulamıştır. Ayrıca, baz yıl ve yıldan yıla kıyaslama hem iyi sonuçlar verir, hem de finansal analiz için her iki teknik de kullanılabilir. Optimal özellik seti, tüm kategorilerden alınan oranları, anlamı, şirket karlılığını, likiditesini, kaldıracı, yönetim verimliliğini, endüstri tipini ve şirket büyüklüğünü, zorlama öngörüsü için çok önemlidir. Bu çalışmada uygulanan prototip, ML tekniklerinin finansal sıkıntıyı tahmin edip edemeyeceği ve finansal oranların ve sektör değişkenlerinin finansal sürdürülebilirliğin göstergesi olup olmadığı gibi açık soruları yanıtlamaya çalışmaktadır.

Anahtar Kelimeler

• Finansal Sıkıntı,Mali Oranlar,Kesin Hesaplar,Karar Ağaçları,Naïve Bayes,Yapay Sinir Ağları,Baz Yıl Karşılaştırması,Önceki Yıl Karşılaştırması,Doğruluk

MACHINE LEARNING MODELS FOR PREDICTING FINANCIAL DISTRESS

Abstract

Difficulties in business liquidity and the consequent financial distress are usually an extremely costly and disruptive event. For this reason, this study attempts to provide a set of features that can help us predict the sustainability of a company. This study involves the building of a financial prediction system which after training on a set of companies’ historical final accounts (ranging over a period of 3 to 5 years), the models built are then capable of evaluating the nature of another companies’ financial data. Consequently, the company’s financial position in the following financial period is predicted (whether a company is active or failing). After predicting firm financial health, the outputs of the Decision Tree, the Naïve Bayes classifier and the Artificial Neural Net are evaluated to see which algorithm is the most accurate in finding a set of features for this problem. The research findings over a real-life datasets confirmed the strength and ability of the proposed model in predicting eminent business failure. Moreover, Base-year and year-over-year comparison both produce good results, therefore both techniques can be used for financial analysis. The optimal feature set included ratios from all categories, meaning, company profitability, liquidity, leverage, management efficiency, industry type and company size are all crucial to distress prediction. The prototype implemented in this study attempts to answer open questions, such as whether ML techniques are capable of predicting financial distress and whether financial ratios and industry variables are indicative of financial sustainability

Keywords

• Financial Distress,Financial Ratios,Set of Final Accounts,Decision Trees,Naïve Bayes,Artificial Neural Networks,Base-Year Comparison,Previous-Year Comparison,Accuracy

References

1. Accord.NET Framework. (2017). Retrieved from: http://accord-framework.net/ index.html
2. Altman, E. I. (1968). Financial Ratios, Discriminant Analysis and the Prediction of Corporate Bankruptcy. The Journal of Finance, 23(4): 589–609.
3. Altman, E. I., Marco, G. and Varetto, F. (1994). Corporate Distress Diagnosis: Comparisons Using Linear Discriminant Analysis and Neural Networks. Journal of Banking and Finance, 18(3): 505-529.
4. Baharin, I. and Sentosa, I. (2013). Capital Structure and the Post Performance Factors of Malaysian PN 17 Firms. International Journal of Business and Management Invention, 2(3): 50–56.
5. Balcaen, S., and Ooghe, H. (2006). 35 Years of Studies on Business Failure: an Overview of the Classic Statistical Methodologies and their Related Problems. The British Accounting Review, 38(1): 63–93.
6. Beaver, W. H. (1966). Financial Ratios as Predictors of Failure. Journal of Accounting Research, 4: 71–111.
7. Berend, D. and Kontorovich, A. (2015). A Finite Sample Analysis of the Naive Bayes Classifier. Journal of Machine Learning Research, 16:1519–1545.
8. Bunn, P. and Redwood, V. (2003). Company Accounts-based Modelling of Business Failures and the Implications for Financial Stability. Bank of England Working Paper No. 210. Available at SSRN: https://ssrn.com/abstract=598276

9. 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.
10. Dua, S. and Du, X. (2016). Data Mining and Machine Learning in Cybersecurity. CRC press.
11. EDGAR Online DataFied API. (2017). Retrieved from: http://developer.edgar-online.com
12. Hernandez Tinoco, M., Holmes, P. and Wilson, N. (2015). Polytomous Response Financial Distress Models: The role of Accounting, Market and Macroeconomic Variables. International Review of Financial Analysis.
13. Hu, H. and Sathye, M. (2015). Predicting Financial Distress in the Hong Kong Growth Enterprises Market from the Perspective of Financial Sustainability. Sustainability, 7(2): 1186–1200.
14. Kim, M. J. and Kang, D. K. (2010). Ensemble with Neural Networks for Bankruptcy Prediction. Expert Systems with Applications, 37(4): 3373-3379.
15. Koonce, L. and Lipe, M. G. (2010). Earnings Trend and Performance Relative to Benchmarks: How Consistency Influences their Joint Use. Journal of Accounting Research, 48(4): 859–884.
16. Mahama, M. (2015). Assessing the State of Financial Distress in Listed Companies in Ghana: Signs, Sources, Detection and Elimination – A Test of Altman’s Z-Score. European Journal of Business and Management, 7(3): 1–11.
17. National Center for the Middle Market. (2017). Promoting Growth of the U.S. Middle Market. Retrieved from: https://www.middlemarketcenter.org/Media/Documents/NCMM_InfoSheet_2017_FINAL_web.pdf
18. Odom, M. and Sharda, R. (1990). Bankruptcy Prediction Using Neural Networks. In Proceedings of IEEE International Conference on Neural Networks. San Diego: 133-168.
19. Ohlson, J. A. (1980). Financial Ratios and the Probabilistic Prediction of Bankruptcy. Journal of Accounting Research: 109–131.
20. Olson, D. L., Delen, D., and Meng, Y. (2012). Comparative Analysis of Data Mining Methods for Bankruptcy Prediction. Decision Support Systems, 52(2): 464– 473.
21. Peng, Y., Wang, G., Kou, G., and Shi, Y. (2011). An Empirical Study of Classification Algorithm Evaluation for Financial Risk Prediction. Applied Soft Computing, 11(2): 2906–2915.
22. Platt, M. L. and Glimcher, P. W. (1999). Neural Correlates of Decision Variables in Parietal Cortex. Nature, 400(6741): 233.
23. Refait, C. (2004). La Prévision de la Faillite Fondée sur l’analyse Financière de l’entreprise : un état des lieux. Economie et prévision, 162(1): 129-147.
24. Russell, S. J. and Norvig, P. (1995). Artificial Intelligence: A Modern Approach. Malaysia: Pearson Education Limited.
25. Sayari, N. and Mugan, C. S. (2017). Industry Specific Financial Distress Modeling. BRQ Business Research Quarterly, 20(1): 45–62.
26. Shalev-Shwartz, S. and Ben-David, S. (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge University Press.
27. Tseng, F. M. and Hu, Y. C. (2010). Comparing Four Bankruptcy Prediction Models: Logit, Quadratic Interval Logit, Neural and Fuzzy Neural Networks. Expert Systems with Applications, 37(3): 1846-1853.
28. Zäpfel, G., Braune, R. and Bögl, M. (2010). Metaheuristic Search Concepts: A Tutorial with Applications to Production and Logistics. Springer Science and Business Media.
29. Zheng, Q. and Yanhui, J. (2007, August). Financial Distress Prediction based on Decision Tree Models. In Service Operations and Logistics, and Informatics, 2007. SOLI 2007. IEEE International Conference on (1-6). IEEE.
30. Zmijewski, M. E. (1984). Methodological Issues Related to the Estimation of Financial Distress Prediction Models. Journal of Accounting Research, 22: 59-86.