Forecasting Company Fundamentals

TL;DR

This paper evaluates 24 forecasting models for company fundamentals, finding deep learning models outperform classical ones, especially in uncertainty estimation, and showing their forecasts are comparable to human analysts for stock allocation.

Contribution

It provides a comprehensive comparison of deterministic and probabilistic models for forecasting company fundamentals, highlighting deep learning's advantages and integration opportunities.

Findings

Deep learning models outperform classical models in forecasting accuracy.

Uncertainty estimation improves model performance.

Forecasts are comparable to human analyst expectations.

Abstract

Company fundamentals are key to assessing companies' financial and overall success and stability. Forecasting them is important in multiple fields, including investing and econometrics. 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.