For many years, researchers have been exploring the use of deep learning in the forecasting of financial time series. However, they have continued to rely on the conventional econometric approach for model optimization, optimizing the deep learning models on individual assets. In this paper, we use the stock volatility forecast as an example to illustrate global training - optimizes the deep learning model across a wide range of stocks - is both necessary and beneficial for any academic or industry practitioners who is interested in employing deep learning to forecast financial time series. Furthermore, a pre-trained foundation model for volatility forecast is introduced, capable of making accurate zero-shot forecasts for any stocks.

翻译：多年来，研究人员一直在探索将深度学习用于金融时间序列预测。然而，他们仍依赖传统的计量经济学方法进行模型优化，即针对单个资产优化深度学习模型。本文以股票波动率预测为例，阐明全局训练——在广泛股票上优化深度学习模型——对于任何有意利用深度学习预测金融时间序列的学术界或业界从业者而言，既是必要的也是有益的。此外，本文还引入了一个用于波动率预测的预训练基础模型，该模型能够对任意股票进行准确的零样本预测。