Publication
Forecasting Company Fundamentals
Felix Divo; Eric Endress; Kevin Endler; Kristian Kersting; Devendra Singh Dhami
In: Transactions on Machine Learning Research (TMLR), Vol. 2025, Pages 1-26, Journal of Machine Learning Research, 2025.
Abstract
Company fundamentals are key to assessing companies’ financial and overall success and
stability. Forecasting them is important in multiple fields, including investing and econo-
metrics. While statistical and contemporary machine learning methods have been applied to
many time series tasks, there is a lack of comparison of these approaches on this particularly
challenging data regime. To this end, we try to bridge this gap and thoroughly evaluate
the theoretical properties and practical performance of 24 deterministic and probabilistic
company fundamentals forecasting models on real company data. We observe that deep
learning models provide superior forecasting performance to classical models, in particular
when considering uncertainty estimation. To validate the findings, we compare them to
human analyst expectations and find that their accuracy is comparable to the automatic
forecasts. We further show how these high-quality forecasts can benefit automated stock
allocation. We close by presenting possible ways of integrating domain experts to further
improve performance and increase reliability.