对比24种模型，发现深度学习在公司基本面预测上更准，还能估计不确定性。
Forecasting Company Fundamentals
- 用24种模型对比预测公司基本面，涵盖经典与深度学习方法。
- 深度学习在准确性与不确定性估计上优于传统模型，接近分析师水平。
- 结果可用于自动化股票配置，适合量化投资与金融建模研究者。
公司基本面是评估企业财务状况与整体稳定性的关键指标，在投资与计量经济学等领域具有重要意义。尽管统计方法和现代机器学习已广泛应用于时间序列任务，但针对这一极具挑战性数据特征的模型比较仍显不足。为此，本文系统评估了24种确定性与概率性公司基本面预测模型在真实公司数据上的理论性质与实际表现。结果表明，深度学习模型在预测性能上显著优于传统模型，尤其在不确定性估计方面优势明显。为验证结论，我们将其与人工分析师预期对比，发现其准确率相当。此外，高质量预测可有效支持自动化股票配置。最后，我们探讨了如何融合领域专家知识以进一步提升性能与可靠性。
原文摘要 · Abstract (English)
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.
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