  For years, researchers investigated the applications of deep learning in
forecasting financial time series. However, they continued to rely on the
conventional econometric approach for model training that optimizes the deep
learning models on individual assets. This study highlights the importance of
global training, where the deep learning model is optimized across a wide
spectrum of stocks. Focusing on stock volatility forecasting as an exemplar, we
show that global training is not only beneficial but also necessary for deep
learning-based financial time series forecasting. We further demonstrate that,
given a sufficient amount of training data, a globally trained deep learning
model is capable of delivering accurate zero-shot forecasts for any stocks.
