Early warning models against bankruptcy risk for Central European and Latin American enterprises

By Tomasz Korol. 2013

110 citations recorded by the source corpus.

Subfields: Accounting, Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/577eb4b08b27c820f6c1f7a675bf56e2e22ee74c

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Accounting

1. Bernardete Ribeiro
2. An Chen
3. Z¨¦lia Serrasqueiro
4. Ningshan Chen
5. Elisabete G. S. Félix
6. Sumaira Ashraf

In Electrical and Electronic Engineering

1. Bernardete Ribeiro
2. An Chen
3. Z¨¦lia Serrasqueiro
4. Ningshan Chen
5. Elisabete G. S. Félix
6. Sumaira Ashraf

Citations in the corpus

• Artificial neural networks in business: Two decades of research (2016, unknown): “Financial distress & bankruptcy (76) [8, 13, 16, 17, 30, 31, 38, 40, 60, 62, 64, 65, 74, 77, 78, 86, 90, 97, 98, 107, 128, 129, 147, 145, 158, 159, 167, 168, 176, 177, 179, 180, 181, 183, 185, 189, 193, 194, 201, 203, 204, 212, 213, 218, 220, 223, 227, 243, 253, 254, 257, 266, 274, 288, 308, 318, 32”
• Financial credit risk assessment: a recent review (2015, background): “Korol (2013) compared the difference on bankruptcy risk forecasting between Latin America and Central Europe using DA, DT, and ANN models.”
• Do Traditional Financial Distress Prediction Models Predict the Early Warning Signs of Financial Distress? (2019, methodology): “For instance, Tykvová and Borell (2012) employed a set of liquidity, profitability, and solvency ratios; moreover, Korol (2013) used a set of profitability, liquidity, and activity ratios with a strong theoretical background.”