Yazar "Atan, Murat" seçeneğine göre listele

Listeleniyor 1 - 2 / 2
  • Küçük Resim
    Öğe
    A comparative study of corporate credit rating prediction with machine learning
    (Wroclaw University of Science and Technology, 2022) Doğan, Seyyide; Büyükkör, Yasin; Atan, Murat
    Credit scores are critical for financial sector investors and government officials, so it is important to develop reliable, transparent and appropriate tools for obtaining ratings. This study aims to predict company credit scores with machine learning and modern statistical methods, both in sectoral and aggregated data. Analyses are made on 1881 companies operating in three different sectors that applied for loans from Turkey's largest public bank. The results of the experiment are compared in terms of classification accuracy, sensitivity, specificity, precision and Mathews correlation coefficient. When the credit ratings are estimated on a sectoral basis, it is observed that the classification rate considerably changes. Considering the analysis results, it is seen that logistic regression analysis, support vector machines, random forest and XGBoost have better performance than decision tree and k-nearest neighbour for all data sets.
  • [ X ]
    Öğe
    Financial distress prediction using support vector machines and logistic regression
    (Springer Science and Business Media Deutschland GmbH, 2022) Doğan, Seyyide; Koçak, Deniz; Atan, Murat
    Financial distress and bankruptcies are highly costly and devastating processes for all parts of the economy. Prediction of distress is notable both for the functioning of the general economy and for the firm’s partners, investors, and lenders at the micro-level. This study aims to develop an effective prediction model with Support Vector Machine and Logistic Regression Analysis. As the field of the study, 172 firms that are traded in Borsa İstanbul, have been chosen. Besides, two basic prediction methods, LRA was also used as a feature selection method and the results of this model were compared. The empirical results show us, both methods achieve a good prediction model. However, the SVM model in which the feature selection phase is applied shows the best performance.