FİNANSAL KESİMİN GİDERİLMESİ İÇİN MAKİNA ÖĞRENME MODELLERİ

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.

MACHINE LEARNING MODELS FOR PREDICTING FINANCIAL DISTRESS

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

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