Methoden vergleichen

	Altman Z-Score: Vorhersage von Unternehmensinsolvenzen	XGBoost
Fachgebiet	Finanzwirtschaft	Maschinelles Lernen
Familie	Regression model	Machine learning
Entstehungsjahr	1968	2016
Urheber	Edward Altman	Chen, T. & Guestrin, C.
Typ	Multiple discriminant analysis scoring model	Ensemble (gradient-boosted decision trees)
Wegweisende Quelle	Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.	Chen, T. & Guestrin, C. (2016). XGBoost: A Scalable Tree Boosting System. Proceedings of the 22nd ACM SIGKDD, 785–794.
Aliasnamen	Altman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skoru	XGBoost, extreme gradient boosting, scalable tree boosting
Verwandt	3	5
Zusammenfassung	The Altman Z-Score is a linear discriminant model developed by Edward I. Altman in 1968 to predict corporate bankruptcy using five accounting-based financial ratios. Derived through multiple discriminant analysis on a matched sample of 66 US manufacturing firms, the model combines liquidity, profitability, leverage, solvency, and activity ratios into a single composite score that classifies firms as financially sound, distressed, or in a grey zone.	XGBoost (Extreme Gradient Boosting) is a scalable tree-boosting algorithm introduced by Tianqi Chen and Carlos Guestrin in 2016. It builds a strong predictor by adding decision trees one at a time, each correcting the errors left by the trees before it, and is a powerful prediction method widely used in competitions.