标题： 预测公司基本面 显示英文标题

标题： Forecasting Company Fundamentals

摘要： 公司基本面是评估公司财务状况以及整体成功与稳定性的关键因素。在投资和计量经济学等多个领域，预测这些基本面至关重要。 尽管统计学和现代机器学习方法已被应用于许多时间序列任务，但在这种特别具有挑战性的数据集上，这些方法之间的对比研究却很匮乏。 为此，我们尝试弥合这一差距，并全面评估了24种确定性和概率性公司基本面预测模型在真实公司数据上的理论特性和实际表现。 我们观察到，深度学习模型相较于经典模型提供了更出色的预测性能，尤其是在考虑不确定性估计时。 为了验证这一发现，我们将其与人类分析师的预期进行了比较，结果表明其准确性可与自动预测相媲美。 我们进一步展示了这些高质量的预测如何能够提升自动化股票配置的效益。 最后，我们提出了可能的方法，通过整合领域专家来进一步提高性能并增强可靠性。 显示英文摘要

摘要： 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.