A0267
Title: Data scaling effect of deep learning in financial time series forecasting
Authors: Chen Liu - The University of Sydney (Australia) [presenting]
Minh-Ngoc Tran - University of Sydney (Australia)
Chao Wang - The University of Sydney (Australia)
Richard Gerlach - University of Sydney (Australia)
Robert Kohn - University of New South Wales (Australia)
Abstract: 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. The stock volatility forecast is used 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.
