NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting. (arXiv:2201.01770v1 [cs.LG])

Financial forecasting has been an important and active area of machine
learning research because of the challenges it presents and the potential
rewards that even minor improvements in prediction accuracy or forecasting may
entail. Traditionally, financial forecasting has heavily relied on quantitative
indicators and metrics derived from structured financial statements. Earnings
conference call data, including text and audio, is an important source of
unstructured data that has been used for various prediction tasks using deep
earning and related approaches. However, …

arxiv financial financial forecasting forecasting transformer