FİNANSAL KESİMİN GİDERİLMESİ İÇİN MAKİNA ÖĞRENME MODELLERİ

Year 2018, Volume: 2 Issue: 2, 174 - 185, 15.10.2018

Joseph Bonello Xavier Brédart Vanessa Vella

https://doi.org/10.24954/JORE.2018.22

Abstract

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.

Keywords

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

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