Neural networks have revolutionized many empirical fields, yet their application to financial time series forecasting remains controversial. In this study, we demonstrate that the conventional practice of estimating models locally in data-scarce environments may underlie the mixed empirical performance observed in prior work. By focusing on volatility forecasting, we employ a dataset comprising over 10,000 global stocks and implement a global estimation strategy that pools information across cross-sections. Our econometric analysis reveals that forecasting accuracy improves markedly as the training dataset becomes larger and more heterogeneous. Notably, even with as little as 12 months of data, globally trained networks deliver robust predictions for individual stocks and portfolios that are not even in the training dataset. Furthermore, our interpretation of the model dynamics shows that these networks not only capture key stylized facts of volatility but also exhibit resilience to outliers and rapid adaptation to market regime changes. These findings underscore the importance of leveraging extensive and diverse datasets in financial forecasting and advocate for a shift from traditional local training approaches to integrated global estimation methods.

翻译：神经网络已在众多实证领域引发革命，但其在金融时间序列预测中的应用仍存争议。本研究证明，在数据稀缺环境下对模型进行局部估计的传统做法，可能是导致先前研究中观测到的混合实证表现的根本原因。通过聚焦于波动率预测，我们采用包含逾10,000支全球股票的数据集，并实施一种跨截面信息聚合的全局估计策略。计量经济学分析表明，随着训练数据集规模扩大且异质性增强，预测精度显著提升。值得注意的是，即使仅使用12个月的数据，全局训练的网络仍能为未包含在训练集中的个股及投资组合提供稳健预测。此外，对模型动态的解读表明，这些网络不仅能捕捉波动率的关键典型事实，还对异常值具有鲁棒性，并能快速适应市场状态转换。这些发现凸显了在金融预测中利用广泛多样化数据集的重要性，并倡导从传统的局部训练方法转向整合式的全局估计方法。