Full-text resources of CEJSH and other databases are now available in the new Library of Science.
Visit https://bibliotekanauki.pl

Results found: 1

first rewind previous Page / 1 next fast forward last

Search results

help Sort By:

help Limit search:
first rewind previous Page / 1 next fast forward last
1
Publication available in full text mode
Content available

A comparative study of corporate credit ratings prediction with machine learning

100%
DOĞAN S., BÜYÜKKÖR Y., ATAN M.
Operations Research and Decisions
|
2022
|
vol. 32
|
issue 1
25-47
EN
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.
first rewind previous Page / 1 next fast forward last
JavaScript is turned off in your web browser. Turn it on to take full advantage of this site, then refresh the page.