Machine Learning for Financial Forecasting, Planning and Analysis: Recent Developments and Pitfalls

TL;DR

This paper reviews recent advances in applying machine learning to financial forecasting, planning, and analysis, highlighting potential pitfalls and the benefits of double machine learning for causal inference.

Contribution

It introduces the double machine learning framework for causal questions in FP&A and discusses how machine learning enhances forecasting and planning with increasing data.

Findings

Double machine learning addresses causal inference challenges.

Forecasting and planning improve with more data points.

Naive machine learning applications often fail in FP&A contexts.

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.