Identifying Economic Drivers of Credit Default : An Analysis of Austrian SMEs in the Construction Sector - Moore, Anne Carrington; Sadílek, Tomáš; Vysoká škola ekonomická v Praze. Fakulta mezinárodních vztahů (2026)
This thesis examines credit default prediction amongst Austrian small and medium enterprises (SMEs) in the construction industry, aiming to enhance understanding of financial distress determinants. Using firm-level balance sheet data from the Austrian credit agency, KSV, for 2019-2022, combined with macroeconomic indicators from the Eurostat database, this study evaluates the predictive power of these two types of variables across one- and two-year horizons. Logistic regression and Random Forest models are applied to compare predictive performance and assess whether the inclusion of macroeconomic variables improves accuracy. The results show that financial ratios remain the most influential predictors, while macroeconomic factors- particularly GDP growth and EUR/USD exchange rate – gain significance over longer horizons. Random Forest models outperform logistic regression in the short term, whereas logistic regression achieves stronger stability over two years. Prediction accuracy declines with longer horizons, reflecting the challenges of extended forecasting. The findings provide novel empirical evidence for Austrian SMEs and practical insights for improving credit risk assessment and early-warning systems.
Vedoucí práce: Tomáš Sadílek
Diplomová práce (Ing.)—Vysoká škola ekonomická v Praze. Fakulta mezinárodních vztahů, 2026
Obsahuje bibliografii
Textový (vysokoškolská kvalifikační práce)
Rok obhajoby 2026
Způsob přístupu: Internet
Keywords: international business - central european business realities [obor dipl. práce]; diplomové práce; master's theses