Forecasting Company Fundamentals

ArXiv ID: 2411.05791 “View on arXiv”

Authors: Unknown

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.

Keywords: Company Fundamentals, Deep Learning, Probabilistic Forecasting, Time Series Analysis, Automated Allocation, Equities

Complexity vs Empirical Score

• Math Complexity: 4.5/10
• Empirical Rigor: 8.0/10
• Quadrant: Street Traders
• Why: The paper focuses on applying and comparing existing deep learning and statistical models to real financial data, with a strong emphasis on backtesting and practical investment outcomes rather than developing new complex mathematical theory. It presents empirical results on 24 models and validates findings against human analyst expectations and portfolio allocation strategies.

  flowchart TD
    A["Research Goal<br>Compare Forecasting Models<br>for Company Fundamentals"] --> B{"Data Input<br>Real Company Fundamentals<br>Time Series Data"}
    B --> C["Methodology<br>Evaluate 24 Models<br>Statistical, ML, & Deep Learning"]
    C --> D["Computational Process<br>Probabilistic & Deterministic Forecasting<br>with Uncertainty Estimation"]
    D --> E["Key Findings<br>Deep Learning Superior to Classical Models"]
    D --> F["Validation<br>Accuracy Comparable to Human Analysts"]
    D --> G["Outcome<br>Improved Automated Stock Allocation"]