Die Prognosefähigkeit von Machine Learning-Verfahren und Managementschätzungen im Rahmen von finanziellen Unternehmenskennzahlen. Ein Vergleich
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Masterarbeit aus dem Jahr 2022 im Fachbereich BWL - Controlling, Note: 1,3, Universität Bremen (Lehrstuhl für Allgemeine Betriebswirtschaftslehre, Unternehmensrechnung und Controlling), Veranstaltung: Masterarbeit, Sprache: Deutsch, Abstract: Der Einsatz von Machine Learning (ML) gewinnt seit Jahren an Bedeutung. Auch im Financial Accounting & Auditing bietet ML vielfache Einsatzmöglichkeiten, wie z. B. die Vorhersage von Insolvenzen oder das Aufdecken von Bilanzbetrug. Ein bislang recht unerforschter, derzeit aber mehr in den Fokus rückender Anwendungsbereich im Financial Accounting, ist die Prognose von finanziellen Kennzahlen und ihrer zukünftigen Entwicklung. Grundsätzlich basiert eine Vielzahl der finanziellen Kennzahlen in der Berichterstattung auf Schätzungen, die durch das Management erfolgen. Vorangegangene Untersuchungen stellen allerdings signifikante Abweichungen zwischen Managementschätzung und dem tatsächlich eingetretenen Wert fest. Aus diesem Grund stehen derartige Kennzahlprognosen im Fokus der vorliegenden Masterarbeit. Das Ziel der Arbeit ist, die Prognosefähigkeit von ML-Verfahren für die zukünftige Entwicklung von finanziellen Kennzahlen mit der von Managementschätzungen zu vergleichen. Über die Prognose hinaus werden die Ursachen für mögliche Unterschiede in der Prognosefähigkeit der Verfahren mithilfe von ML analysiert. Die Umsetzung der ML-Prognosen erfolgt in Matlab. Es werden mit den ML-Verfahren Random Forest (RF) und Neuronale Netze (NN) die Prognosen der Kennzahlen für das jeweils nächste Jahr erstellt. Nach einem Vergleich der Prognosegüte der ML-Modelle untereinander erfolgt anschlie end eine Gegenüberstellung dieser mit der Managementschätzung. Anknüpfend an diese Ergebnisse erfolgt eine Interpretation der unterschiedlichen Prognosefähigkeit der Verfahren. Hierzu wird zum einen untersucht, wie sich durch die Hinzunahme der Managementschätzung als Attribut in den Kennzahlprognosen der ML-Verfahren die Prognosefähigkeit dieser veränd
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