Machine Learning for Financial Forecasting, Planning and Analysis:
Recent Developments and Pitfalls
- URL: http://arxiv.org/abs/2107.04851v1
- Date: Sat, 10 Jul 2021 14:54:36 GMT
- Title: Machine Learning for Financial Forecasting, Planning and Analysis:
Recent Developments and Pitfalls
- Authors: Helmut Wasserbacher and Martin Spindler
- Abstract summary: This article is an introduction to machine learning for financial forecasting, planning and analysis (FP&A)
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.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- 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.
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