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Journals / Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi / 2025 / Sayı: 72

Value-based firm rating: The case of Turkish cement firms with machine learning methods

Değere dayalı firma derecelendirmesi: Makine öğrenmesi yöntemleriyle Türk çimento firmaları örneği

Müge Peştere-akçay(Pamukkale Üniversitesi)0000-0003-4700-9769
İlhan Küçükkaplan(PAMUKKALE ÜNİVERSİTESİ)0000-0001-6926-3659
Şaban Nazlıoğlu(Pamukkale Üniversitesi)0000-0002-3607-3434
Pages
31–37
Publication Date
2025-12-31
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The empirical results indicate that the K-nearest neighbors (K-NN) machine learning method delivers superior predictive accuracy when combined with a value-based rating model; models relying solely on financial ratios exhibit the weakest performance.

📌 3 Key Takeaways

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Objective & Hypothesis:This study conducts a value-based firm rating analysis utilizing advanced machine learning techniques.
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Key Findings & Data:The analysis is based on balance sheet and income statement data spanning 2006 to 2019 for 12 Turkish cement firms.
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Conclusion & Impact:The empirical results indicate that the K-nearest neighbors (K-NN) machine learning method delivers superior predictive accuracy when combined with a value-based rating model; models relying solely on financial ratios exhibit the weakest performance.
🔬Design:Modeling & Simulation
📍Scope:2006 Verileri
⚙️Variables:irma derecelendirme · makine öğrenmesi · firma oranı

Abstract

This study conducts a value-based firm rating analysis utilizing advanced machine learning techniques. The analysis is based on balance sheet and income statement data spanning 2006 to 2019 for 12 Turkish cement firms. We develop a value-based rating model that integrates a comprehensive set of micro- and macroeconomic variables, including firm assets, country risk, sector risk, and market dynamics. Furthermore, we assess and compare the predictive performance of five alternative rating models: (i) discounted cash flow (DCF) value-based rating, (ii) market value-based rating, (iii) financial ratios-based rating, (iv) financial ratios and DCF value-based rating, and (v) financial ratios and market value-based rating. The empirical results indicate that the K-nearest neighbors (K-NN) machine learning method delivers superior predictive accuracy when combined with a value-based rating model; models relying solely on financial ratios exhibit the weakest performance. Moreover, incorporating firm value into financial ratio-based models significantly enhances their predictive power, while excluding firm value may lead to systematic biases. These findings highlight the importance of integrating valuation-based information into data-driven rating systems, suggesting that models capturing both firm fundamentals and market conditions offer a more reliable framework for evaluating corporate financial health, and thus provide deeper insights for decision-makers, particularly under uncertainty.

Özet

Bu çalışma, gelişmiş makine öğrenmesi tekniklerini kullanarak değer temelli firma derecelendirmesi analizi gerçekleştirmektedir. Analiz, 2006–2019 dönemini kapsayan ve 12 Türk çimento firmasına ait bilanço ve gelir tablosu verilerine dayanmaktadır. Çalışmada, firma varlıkları, ülke riski, sektör riski ve piyasa dinamikleri gibi kapsamlı bir mikro ve makroekonomik değişken setini içeren değer temelli bir derecelendirme modeli geliştirilmektedir. Ayrıca, beş alternatif derecelendirme modelinin öngörü performansları değerlendirilmekte ve karşılaştırılmaktadır: (i) indirgenmiş nakit akımı (DCF) temelli değer derecelendirmesi, (ii) piyasa değeri temelli derecelendirme, (iii) finansal oranlara dayalı derecelendirme, (iv) finansal oranlar ile DCF değerine dayalı derecelendirme ve (v) finansal oranlar ile piyasa değerine dayalı derecelendirme. Ampirik bulgular, K-en yakın komşu (KNN) makine öğrenmesi yönteminin değer temelli bir derecelendirme modeliyle birlikte kullanıldığında en yüksek öngörü doğruluğunu sağladığını; yalnızca finansal oranlara dayalı modellerin ise en zayıf performansı gösterdiğini ortaya koymaktadır. Ayrıca, firma değerinin finansal oran temelli modellere dâhil edilmesi bu modellerin öngörü gücünü belirgin biçimde artırmakta, firma değerinin dışarıda bırakılması ise sistematik yanlılıklara yol açabilmektedir. Bu bulgular, değer temelli bilgilerin veri odaklı derecelendirme sistemlerine entegre edilmesinin önemini vurgulamakta; firma temelleri ile piyasa koşullarını birlikte yakalayan modellerin kurumsal finansal sağlığın değerlendirilmesinde daha güvenilir bir çerçeve sunduğunu ve belirsizlik ortamlarında karar alıcılara daha derinlemesine içgörüler sağladığını göstermektedir.

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Cite This Article

Peştere-akçay, M., Küçükkaplan, İ., Nazlıoğlu, Ş. (2025). Value-based firm rating: The case of Turkish cement firms with machine learning methods. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, Sayı: 72, 31–37. https://doi.org/10.18070/erciyesiibd.1664645

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