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

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.

Revenue forecast models using hybrid intelligent methods

“…Model Hyperparameter Values "min_samples_leaf": [3,4,5,6] min_samples_leaf: 5 "min_samples_split": [3,4,5,6] min_samples_split: 5 "n_estimators": [50,100,200] n_estimators:100…”

“…Models had less than 5% error; regression equations in machine learning models performed better. In [6], authors explained the detail of machine learning applications for financial forecasting. Machine learning appears to be ideally adapted to enhance forecasting, analysis and planning by substantially automating the information extraction process from massive datasets.…”