Öznitelik Seçim Yöntemleri ve Makine Öğrenmesi Kullanarak Şirket Bilanço Verilerine Dayalı İflas Riski Tahmini

Yıl 2019, Cilt: 12 Sayı: 2, 20 - 29, 17.12.2019

Necip Bulut , Saber Shakeri , Seçil Yüzük Mehmet Sıddık Aktaş

Öz

Bir şirketin başarısı hem firmanın iç
muhatapları hem de yatırımcılar ve üçüncü kişilerce büyük önem taşımaktadır.
Finansal olarak başarısızlık kimi zaman iflaslar ile sonuçlanabilmekte ve
firmanın muhatapları üzerinde yıkıcı etkiler yaratabilmektedir. Yatırımcılar,
finansörler, yöneticiler bazen de politika yapıcıları için firmaların iflas
risklerini tahmin etmek oldukça önemlidir. Literatürde iflas riskinin tahmini
için birçok yöntem geliştirilse de Ohlson O-skoru ve Altman Z-skoru iflas
riskini tahmin için oldukça sık kullanılan iki yöntemdir. Bu iki modelin hem
lineer model olmaları hem de firmaların yalnızca son bilançolarıyla
ilgilenmeleri bazen hatalı tahminlere yol açabilmektedir. İflas olgusunun bir
süreç olduğu düşünüldüğünde şirketin sadece son finansal raporlarının
incelenmesi bir takım sakıncalar barındırır. Bu sebeple iflas risklerini doğru
tahmin etmek için şirketlerin geçmiş finansal raporlarının da incelenmesi
gerekmektedir. Literatürdeki bu iki iflas riski tahmin yöntemi şirketlerin
sadece son finansal raporlarıyla ilgilenmektedir. Ayrıca bu iki modelde
şirketin başarısına dair karar verilemeyen gri alanlar bulunmaktadır. Bu
çalışmada literatürdeki klasik lineer modeller yerine, lineer olmayan makine
öğrenmesi algoritmaları kullanılarak şirketlerin iflas riskleri tahmin edilmeye
çalışılmıştır. Bu amaç doğrultusunda öznitelik seçim metodu olarak Bilgi
Kazanımı ve Temel Bileşenler Analizi, Lineer Diskriminant Analizi ile
birleştirilerek ve makine öğrenmesi metodu olarak Lojistik Regresyon, Karar
Destek Vektörleri ve Rassal Orman algoritması kullanılmıştır. Bu bağlamda
şirketlerin iflas riskini makine öğrenmesi algoritmalarıyla tahmin etmenin,
lineer klasik modellerden başarılı olduğu sonucuna ulaşılmıştır.

Anahtar Kelimeler

Ohlson O-skoru, Altman Z-Skoru, Şirket İflas Riski Tahmini, Öznitelik Çıkarımı, Makine Öğrenmesi

Bankruptcy Risk Forecasting Based on Company Balance Sheet Data Using Feature Selection Methods And Machine Learning

Öz

The success of a company has a significant issue for both the
interlocutors of companies and other related persons. Financial failure
sometimes end up bankrupt and can have a critical effect on the company’s
interlocutors. Prediction of bankruptcy is significant for investors, backers,
directors and sometimes policymakers. Although there are a lot of models to
predict bankruptcy in the financial literature, Ohlson O-score and Altman
Z-score are models that are used quite often. The fact that these two
models are both linear models and companies are only interested in their latest
balance sheets can sometimes lead to incorrect predictions. Considering bankruptcy as
a process, to interest in only the latest financial reports of the companies
has some drawbacks. For this reason, in addition to the current, previously
financial reports of companies should be interested to predict bankruptcy risk
of the company correctly. In the literature, these two classical models
interest in only the current financial reports of companies. Additionally,
there are grey areas that are not decided about the bankruptcy of companies in
these two classical models.   In this
study, it is tried to predict the bankruptcy risk of companies by using
non-linear machine learning algorithms rather than classical linear models in
the financial literature. In line with this main purpose, as feature selection
methods Information Gain, Principle Component Analysis algorithms by combining
Linear Discrimination Analysis algorithm and as machine learning methods
Logistic Regression, Support Vector Machine, and Random Forest algorithms are
used. It has been found that predicting the bankruptcy risk of companies by
using non-linear machine learning algorithms is more successful than linear
classical models.

Anahtar Kelimeler

Ohlson O-score, Altman Z-score, Bankruptcy Risk Prediction, Feature Extraction, Machine Learning

Kaynakça

• [1] Ohlson, James A. "Financial ratios and the probabilistic prediction of bankruptcy." Journal of accounting research ,1980, pp. 109-131
• [2] Altman, Edward I. "Financial ratios, discriminant analysis and the prediction of corporate bankruptcy." The journal of finance 23.4 , 1968, pp. 589-609
• [3] Taffler, Richard J. "Empirical models for the monitoring of UK corporations." Journal of Banking & Finance 8.2, 1984, pp. 199-227
• [4] Imelda, Elsa, and Ignacia Alodia. "The Analysis of Altman Model and Ohlson Model in Predicting Financial Distress of Manufacturing Companies in the Indonesia Stock Exchange." Indian-Pacific Journal of Accounting and Finance 1.1, 2017, pp. 51-63
• [5] Wang, Nanxi. "Bankruptcy prediction using machine learning." Journal of Mathematical Finance 7.04, 2017, pp. 908
• [6] Altaş, Dilek, and Selay Giray. "Mali Başarısızlığın Çok Değişkenli İstatistiksel Yöntemlerle Belirlenmesi: Tekstil Sektörü Örneği.", 2005
• [7] Muzir, Erol and Caglar, Nazan (2009), “The Accuracy of Financial Distress Prediction Models in Turkey: A Comparative Investigation with Simple Model Proposals”, Anadolu University Journal of Social Sciences, 9(2): 15-48
• [8] Raut, Sneha, Milind Tiwari, and Kuldeep Kumar. "Financial Distress Prediction Using Cutting-Edge Statistical Techniques: A Study of Australian Real Estate Sector." Shodh-Amrit: JKLU Journal of Engineering & Management 1.2, 2018, pp. 2-32
• [9] Viviani, Jean‐Laurent, and Hong LE Hanh. "Why Do Banks Fail?-The Explanation from Text Analytics Technique.", SSRN Electronic Journal, 2018
• [10] Lin, Wei‐Chao, Yu‐Hsin Lu, and Chih‐Fong Tsai. "Feature selection in single and ensemble learning‐based bankruptcy prediction models." Expert Systems 36.1, 2019, e12335
• [11] Vu, Loan Thi, et al. "Feature selection methods and sampling techniques to financial distress prediction for Vietnamese listed companies." Investment Management & Financial Innovations16.1, 2019, pp. 276
• [12] AKPINAR, Onur, and Gökçe AKPINAR. "Finansal Başarısızlık Riskinin Belirleyicileri: Borsa İstanbul’da Bir Uygulama.", Journal of Business Research – Turk, Volume 9, Issue 4 2017
• [13] Zeytinoglu, Emin, and Yasemin Deniz Akarim. "Financial failure prediction using financial ratios: An empirical application on Istanbul Stock Exchange." Journal of Applied Finance and Banking 3.3, 2013, pp. 107
• [14] Bellovary, Jodi L., Don E. Giacomino, and Michael D. Akers. "A review of bankruptcy prediction studies: 1930 to present." Journal of Financial education, 2007, pp. 1-42
• [15] Acosta-González, Eduardo, Fernando Fernández-Rodríguez, and Hicham Ganga. "Predicting corporate financial failure using macroeconomic variables and accounting data." Computational Economics 53.1, 2019, pp. 227-257
• [16] Chen, James Ming. "Models for Predicting Business Bankruptcies and Their Application to Banking and to Financial Regulation." Available at SSRN 3329147, 2019
• [17] Shakeri, S., & Ashouraei, M. (2016). NeurAda: Combining artificial neural network and Adaboost for accurate object detection. International Journal of Next-Generation Computing, 7(2), pp. 155-163
• [18] Christopoulos, Apostolos G., et al. "An implementation of Soft Set Theory in the Variables Selection Process for Corporate Failure Prediction Models. Evidence from NASDAQ Listed Firms." Bulletin of Applied Economics 6.1, 2019, pp. 1-20
• [19] Fernández, Manuel Ángel, et al. "Focused vs unfocused models for bankruptcy prediction: Empirical evidence for Spain." Contaduría y Administración 64.2, 2019, e96
• [20] Varatharajan, R., Manogaran, G., & Priyan, M. K. (2018). A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications, 77(8), 10195-10215
• [21] Zengin T., Shakeri S., Bulut N., Yüzük1 S. and Aktaş M.S. (2019, September). Bankruptcy Risk Forecast Based on Company Balance Sheet Data Using Machine Learning. In 2019 4rd International Conference on Computer Science and Engineering (UBMK). IEEE. 2019