The development of the bankruptcy or financial distress prediction
model has long been regarded as an important research in the
academic and business entities. Financial distress of companies
imposes many costs to the companies. One method that can help
companies to prevent from financial distress is prediction of financial
distress. This prediction also can help banks and other financial
institution to have better credit scoring and rating systems.
In this study we used Support Vector Machines (SVM) for predicting
financial distress of companies and Logistic Regression (LR) as a
comparative method. We found that SVM has a better performance
than LR. Results show that SVM not only has a better accuracy rate of
prediction but also has a better generalization power.
(2009). Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios. Accounting and Auditing Review, 15(4), -.
MLA
. "Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios", Accounting and Auditing Review, 15, 4, 2009, -.
HARVARD
(2009). 'Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios', Accounting and Auditing Review, 15(4), pp. -.
VANCOUVER
Support Vector Machines Application in Financial Distress Prediction of Companies Using Financial Ratios. Accounting and Auditing Review, 2009; 15(4): -.
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