Machine learning for financial forecasting, planning and analysis: recent developments and pitfalls

Artikel

This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A). Machine learning appears well suited to support FP&A with the highly automated extraction of information from large amounts of data. However, because most traditional machine learning techniques focus on forecasting (prediction), we discuss the particular care that must be taken to avoid the pitfalls of using them for planning and resource allocation (causal inference). While the naive application of machine learning usually fails in this context, the recently developed double machine learning framework can address causal questions of interest. We review the current literature on machine learning in FP&A and illustrate in a simulation study how machine learning can be used for both forecasting and planning. We also investigate how forecasting and planning improve as the number of data points increases.

Angaben zum Objekt

• Sprache: Englisch

Verweise und Beziehungen

• Erschienen in: Journal: Digital Finance ; ISSN: 2524-6186 ; Volume: 4 ; Year: 2021 ; Issue: 1 ; Pages: 63-88 ; Cham: Springer International Publishing

Klassifikation und Themen

• Klassifikation: Wirtschaft ; Capital Budgeting; Fixed Investment and Inventory Studies; Capacity ; Forecasting Models; Simulation Methods ; Large Data Sets: Modeling and Analysis
• Thema: Financial planning ; Machine learning ; Forecasting ; Causal machine learning ; Big data ; Double machine learning ; Primary G17

Beteiligte, Orts- und Zeitangaben

• Ereignis: Geistige Schöpfung
• (wer): Wasserbacher, Helmut ; Spindler, Martin
• Ereignis: Veröffentlichung
• (wer): Springer International Publishing
• (wo): Cham
• (wann): 2021

Weitere Informationen

• DOI: doi:10.1007/s42521-021-00046-2
• Letzte Aktualisierung: 10.03.2025, 11:43 MEZ