Methoden vergleichen

	Altman Z-Score: Vorhersage von Unternehmensinsolvenzen	Lineare Diskriminanzanalyse (LDA)
Fachgebiet	Finanzwirtschaft	Maschinelles Lernen
Familie	Regression model	Latent structure
Entstehungsjahr	1968	1936
Urheber	Edward Altman	Fisher, R. A.
Typ	Multiple discriminant analysis scoring model	Supervised dimensionality reduction and linear classifier
Wegweisende Quelle	Altman, E. I. (1968). Financial ratios, discriminant analysis and the prediction of corporate bankruptcy. The Journal of Finance, 23(4), 589–609.	Fisher, R. A. (1936). The use of multiple measurements in taxonomic problems. Annals of Eugenics, 7(2), 179–188.
Aliasnamen	Altman's Z-Score Model, Multiple Discriminant Analysis Bankruptcy Model, Z-Score Financial Distress Model, Altman Z-Skoru	LDA, Fisher's discriminant analysis, Fisher linear discriminant, normal discriminant analysis
Verwandt	3	4
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.	Linear Discriminant Analysis is a supervised method for dimensionality reduction and classification, introduced by Ronald A. Fisher in 1936, that finds linear combinations of features which maximally separate predefined classes while preserving as much class-discriminatory information as possible. It simultaneously serves as a feature-projection technique and a probabilistic classifier, making it one of the foundational methods in pattern recognition and statistical learning.