03097ntm a22004697i 4500 000730654 CZ-PrVSE 20260419164828.0 m d cr n|||||||||| 260419s2026 xr fsbm 000 0 eng d NEZPRACOVANÝ IMPORT ABA006 cze ABA006 ABA006 rda Moore, Anne Carrington ISIS:183439 dis Identifying Economic Drivers of Credit Default : An Analysis of Austrian SMEs in the Construction Sector eng Identifying Economic Drivers of Credit Default : An Analysis of Austrian SMEs in the Construction Sector Anne Carrington Moore 2026 ?? stran : digital, PDF soubor 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 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. Způsob přístupu: Internet international business - central european business realities [obor dipl. práce] diplomové práce fd132022 czenas master's theses eczenas SME default credit risk logistic regression Random Forest Sadílek, Tomáš ISIS:115983 ths Vysoká škola ekonomická v Praze. Fakulta mezinárodních vztahů kn20010709400 dgg https://insis.vse.cz/zp/95345/podrobnosti VŠKP v InSIS https://insis.vse.cz/zp/95345 Hlavní práce https://insis.vse.cz/zp/95345/podrobnosti dc:identifier NEPOSILAT VSKP vse95345 260418 95345