Bitte verwenden Sie diesen Link, um diese Publikation zu zitieren, oder auf sie als Internetquelle zu verweisen: https://hdl.handle.net/10419/287710 
Erscheinungsjahr: 
2021
Quellenangabe: 
[Journal:] Digital Finance [ISSN:] 2524-6186 [Volume:] 4 [Issue:] 1 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2021 [Pages:] 63-88
Verlag: 
Springer International Publishing, Cham
Zusammenfassung: 
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.
Schlagwörter: 
Financial planning
Machine learning
Forecasting
Causal machine learning
Big data
Double machine learning
Primary G17
G31
C53
C55
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Dokumentart: 
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Dokumentversion: 
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