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.
Highlights
Accurate financial forecasts and plans for effective and efficient resource allocation are core deliverables of the finance function in modern companies
We report the forecast accuracy in terms of average root-mean-squared error (RMSE) over all simulation runs both on the in-sample and the out-of-sample data set
We have provided an introductory overview of machine learning in this context
Summary
Accurate financial forecasts and plans for effective and efficient resource allocation are core deliverables of the finance function in modern companies. Represent tasks of a different nature, because they require understanding the effect of an active intervention in a system, such as the market for a product For this reason, they are causal problems, which are harder to model with machine learning. Given the importance of financial forecasting, planning and analysis (FP&A) in modern corporations, most larger companies have dedicated teams for these tasks within their finance function, even though the exact organizational design and naming of the department may vary.. The exact nature and granularity of these data depends largely on the product or question under analysis, as well as the investment required to access the relevant data (Gray and Alles 2015) It is not uncommon in the consumer goods industry to have access to transaction-level data (Taddy 2019), covering one’s own and competitor products. For data that are more numerous, available more quickly, and are more diverse and of better quality than in the past, FP&A needs to choose adequate tools, such as those provided by machine learning
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