Please use this identifier to cite or link to this item: https://hdl.handle.net/10419/287710 
Year of Publication: 
2021
Citation: 
[Journal:] Digital Finance [ISSN:] 2524-6186 [Volume:] 4 [Issue:] 1 [Publisher:] Springer International Publishing [Place:] Cham [Year:] 2021 [Pages:] 63-88
Publisher: 
Springer International Publishing, Cham
Abstract: 
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.
Subjects: 
Financial planning
Machine learning
Forecasting
Causal machine learning
Big data
Double machine learning
Primary G17
G31
C53
C55
Persistent Identifier of the first edition: 
Creative Commons License: 
cc-by Logo
Document Type: 
Article
Document Version: 
Published Version

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