Yapay Sinir Ağları Ve Karar Ağacı Yöntemleri İle Muhasebeye Dayalı Oranlar Kullanılarak Firma Performansının Ölçülmesi: Borsa İstanbul’da Bir Uygulama

Year 2023, Volume: 7 Issue: 1, 89 - 102, 30.06.2023

Mustafa Kıllı , Yusuf Işık

Abstract

Firmaların en temel amaçlarından biri olan sürdürülebilirlik, yüksek şirket performansı ile sağlanabilir. Firma performansını ölçmek için birçok finansal ve finansal olmayan performans ölçüm aracı kullanılabilir. Performans ölçümü, geleceğe yönelik kararların sağlam temellere dayanmasını sağlayan bir süreç olarak önemli bir yönetim muhasebesi aracıdır. Oran analizi, firma performansını ölçmek için kullanılan finansal ölçüm araçlarından biridir. Kimi zaman öncü gösterge niteliğinde olan bu oranlar, şirketlerin çeşitli önlemler alması gerektiğinin göstergesi olabilir. Bu çalışmada, veri madenciliği tekniklerinden yapay sinir ağları ve karar ağacı yöntemleri kullanılarak muhasebe tabanlı oranlar ile firma performansı arasındaki ilişkinin belirlenmesi amaçlanmıştır. Bu amaçla Borsa İstanbul (BİST) holding ve yatırım şirketleri sektöründe yer alan şirketlerin 2021 yılı mali tabloları kullanılarak elde edilen oranlar üzerinde analizler yapılmıştır. Sonuç olarak her iki veri madenciliği yöntemi karşılaştırıldığında yapay sinir ağları ile yapılan analiz tahmin değerlerinin karar ağacı modeline göre daha tutarlı olduğu görülmüştür.

Keywords

Yönetim Muhasebesi, Finansal Performans Ölçümü, Yapay Sinir Ağları, Karar Ağacı

MEASUREMENT OF FIRM PERFORMANCE USING ACCOUNTING BASED RATIOS WITH ARTIFICIAL NEURAL NETWORKS AND DECISION TREE METHODS: A STUDY IN BORSA ISTANBUL

Abstract

Sustainability, which is one of the most basic purpose of companies, can be achieved with high company performance. Many financial and non-financial performance measurement tools can be used to measure firm performance. Performance evaluation is an important management accounting tool as a process that allows future decisions to be based on solid foundations. Ratio analysis is one of the financial measurement tools used to measure firm performance. These ratios, which are sometimes a leading indicator, may indicate that companies should take various measures. This study aimed to determine the relationship between accounting-based ratios and firm performance using artificial neural networks and decision tree methods, which are data mining techniques. For this purpose, analyzes were carried out on the ratios obtained by using the 2021 financial statements of the companies in the Borsa Istanbul (BIST) holdings and investment companies sector. As a result, when both data mining methods were compared, it was seen that the analysis prediction values made with artificial neural networks were more consistent than the decision tree model.

Keywords

Management Accounting, Financial Performance Measurement, Artificial Neural Networks, Decision Tree

References

• Albayrak, A. S. and Yılmaz, Ş. K. (2009). Data Mining: Decision Tree Algorithms And An Application On ISE Data. Suleyman Demirel University The Journal of Faculty of Economics and Administrative Sciences, 14(1), 31-52.
• Ataseven, B. (2013). Forecasting By Using Artificial Neural Networks. Öneri Journal of Marmara University Institute of Social Sciences, 10(39),101-115.
• Appiahene, P., Missah, Y. M., and Najim, U. (2020). Predicting bank operational efficiency using machine learning algorithm: comparative study of decision tree, random forest, and neural networks. Advances in fuzzy systems, https://doi.prg/10.1155/2020/8581202.
• Aksoy, B. (2021). Pay senedi fiyat yönünün makine öğrenmesi yöntemleri ile tahmini: Borsa İstanbul örneği. Business and Economics Research Journal, 12(1), 89-110.
• Arslantürk Çöllü, D., Akgün, L. and Eyduran, E. (2020). Financial Failure Prediction with Decision Tree Algorithms: Textile, Wearing Apparel and Leather Sector Application. International Journal of Economics and Innovation, 6(2), 225-246.
• Bardi, Ş. (2020). The Efficiency Analysis of Companies on BIST Food Beverage Index By Using Data Envelopment And Data Mining. Suleyman Demirel University Visionary Journal, 11, 185-199.
• Bellovary, J. Giacomino, D. and Akers, M. (2007). A review of bankruptcy prediction studies: 1930–present. Journal of Finance Education, 33, 1–42.
• Büyükarıkan, U. and Büyükarıkan, B. (2014). Analysis of Firms Operating in The It Sector Financial Distress Prediction Model’s, Akademic Review, 46(7), 160-172.
• Büyükmirza, K. (2010). Maliyet ve Yönetim Muhasebesi. Gazi Kitapevi (15.Ed), Ankara.
• Can, N. S. and Şahin, A. Ş. (2021). Daily dew point temperature estimation with artificial neural networks method. Gümüşhane University Journal of Science and Technology, 11(4), 1154-1163.
• Chen, F. H., Chi, D. J., and Wang, Y. C. (2015). Detecting biotechnology industry's earnings management using Bayesian network, principal component analysis, back propagation neural network, and decision tree. Economic Modelling, 46, 1-10.
• Delen, D., Kuzey, C. and Uyar, A. (2013). Measuring firm performance using financial ratios: A decision tree approach. Expert Systems with Applications, 40, 3970–3983.
• Durgut, M. (2017). Use and Reporting of Non-Financial Data in Performance Evaluation within the Scope of Management Accounting. Research Journal of Politics, Economics And Management, 5(2), 245-255.
• Ece, O. (2018). The Determination of Relationship Between Value-Based Management And Corporate Sustainability For Value Maximization: An Application in BIST. Manas Journal of Social Studies, 7(1), 299-330.
• Fitzpatrick, F. (1932) A Comparison of Ratios of Successful Industrial Enterprises with Those of Failed Firm. Certified Public Accountant, 6, 727-731.
• Hateffard, F., Dolati, P., Heidari, A., and Zolfaghari, A. A. (2019). Assessing the performance of decision tree and neural network models in mapping soil properties. Journal of Mountain Science, 16(8), 1833-1847.
• Hidayati, R., Kanamori, K., Feng, L., and Ohwada, H. (2016). Combining feature selection with decision tree criteria and neural network for corporate value classification. In Pacific Rim Knowledge Acquisition Workshop (pp. 31-42). Springer, Cham.
• Ibrahim, Z., and Rusli, D. (2007, September). Predicting students’ academic performance: comparing artificial neural network, decision tree and linear regression. In 21st Annual SAS Malaysia Forum, 5th September.
• Karaoğlan, S. and Şahin, S. (2017). The Evaluation of Financial Performances of BIST XKMYA Companies by Multi-Criteria Decision-Making Methods and Comparison of Methods. Ege Akademic Review, 18 (1), 63-80. Kirkos, E., Spathis, C., and Manolopoulos, Y. (2007). Data mining techniques for the detection of fraudulent financial statements. Expert Systems with Applications, 32(4), 995-1003.
• Lam, M. (2004). Neural network techniques for financial performance prediction: integrating fundamental and technical analysis. Decision support systems, 37(4), 567-581.
• Martin, A., Gayathri, V., Saranya, G., Gayathri, P. and Venkatesan, P. (2011). A hybrid model for bankruptcy prediction using genetic algorithm, fuzzy c-means and mars. International Journal on Soft Computing, 2(1), 12-24.
• Mishra, M. K., and Dash, R. (2014). A comparative study of chebyshev functional link artificial neural network, multi-layer perceptron and decision tree for credit card fraud detection. International Conference on Information Technology, 228-233. https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=7033327
• Mirzaey, M., Jamshidi, M. and Hojatpour, Y. (2017). Applications of artificial neural networks in information system of management accounting. International Journal of Mechatronics, Electrical and Computer Technology (IJMEC), 7(25), 3523-3530.
• Olson, D. and Mossman, C. (2003). Neural network forecasts of Canadian stock returns using accounting ratios. International Journal of Forecasting, 19, 453-465.
• Omar, N., Johari, Z. A., and Smith, M. (2017). Predicting fraudulent financial reporting using artificial neural network. Journal of Financial Crime, 24(2), 362-387.
• Pekin, S. (2020). Finansal performans tahmininde metin madenciliğinin kullanimi: bist imalat sanayi işletmelerinde bir araştırma. (Doktora tezi). Eskişehir Üniversitesi, Sosyal Bilimler Enstitüsü
• Seyrek, İ. H. and Ata, H.A. (2010). Efficiency Measurement in Deposit Banks Using Data Envelopment Analysis and Data Mining. Journal of BRSA Banking and Financial Markets, 4(2), 67-84.
• Singh, Y. and Chauhan, A.S. (2010). Neural networks ın data mining. Journal of Theoretical and Applied Information Technology, 5(1), 37-42
• Tsai, C. F., and Chiou, Y. J. (2009). Earnings management prediction: A pilot study of combining neural networks and decision trees. Expert systems with applications, 36(3), 7183-7191.
• Tunç, A. and Ülger, İ. (2016). Implementation Of Normalization Process Using Binning and Five Number Summary Methods to Financial Value for Feature Selection in Data Mining Applications, 18. Akademic Informatics Conference, Proceedings Book, p. 47-58.
• Tung, K. Y., Huang, C., Chen, S. L., and Shih, C. T. (2005). Mining the generation xers' job attitudes by artificial neural network and decision tree—empirical evidence in taiwan. Expert Systems with Applications, 29(4), 783-794.
• Uğur, A. and Kınacı, A. C. (2006). Yapay Zeka Teknikleri Ve Yapay Sinir Ağları Kullanılarak Web Sayfalarının Sınıflandırılması. XI. "Türkiye'de İnternet" Konferansı Bildirileri (inet-tr'06), Ankara. http://inet-tr.org.tr/inetconf11/kitap/_inet06.pdf
• Ünkaya, G., and Sayin, G. (2019). Going Concern Prediction Via Decision Tree Models Among Non-Finance Public Companies. Mali Çözüm Dergisi, 29(156).
• Weygandt, J.J., Kimmel, P.D. and Kieso, D.E. (2010). Managerial accounting. Massachusetts: John Wiley & Sons. Wu, Y. and Feng, J. (2018). Development and Application of Artificial Neural Network. Wireless Pers Commun. 102, 1645–1656. https://doi.org/10.1007/s11277-017-5224-x.
• Yürük, M. F. and Ekşi, İ. H. (2019). Financial Failure Prediction of Companies Using Artificial Intelligence Methods: An Application in BIST Manufacturing Sector. Mukaddime, 10(1), 393-422.