Assessing the financial health of organizations remains a topic of great interest to economists, financial institutions, and invested stakeholders. For more than a century, research into financial distress has focused primarily on traditional applications of statistical analysis; however, modern advances in computational efficiency have created a significant opportunity for more sophisticated approaches. This thesis investigates the application of artificial intelligence on company bankruptcy prediction. The proposed neural network model is evaluated using the Polish Companies Bankruptcy dataset and yields a 5-year prediction accuracy of 96.5% and an AUC (area under receiver operating characteristic curve) measure of 92.4%.
| Identifer | oai:union.ndltd.org:CALPOLY/oai:digitalcommons.calpoly.edu:theses-3915 |
| Date | 01 June 2021 |
| Creators | Magdefrau, Walter D |
| Publisher | DigitalCommons@CalPoly |
| Source Sets | California Polytechnic State University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Master's Theses |
Page generated in 0.0016 seconds