Learning Dynamic Multimodal Implicit and Explicit Networks for Multiple Financial Tasks

Many financial forecasting deep learning works focus on the single task of predicting stock returns for trading with unimodal numerical inputs. Investment and risk management however involves multiple financial t asks - forecasts of expected returns, risks and correlations of multiple stocks in portfolios, as well as important events affecting different stocks - to support decision making. Moreover, stock returns are influenced by large volumes of non-stationary time-series information from a variety of modalities and the propagation of such information across inter-company relationship networks. Such networks could be explicit - observed co-occurrences in online news; or implicit -inferred from time-series information. Such networks are often dynamic, i.e. they evolve across time. Therefore, we propose the Dynamic Multimodal Multitask Implicit Explicit (DynMIX) network model, which pairs explicit and implicit networks across multiple modalities for a novel dynamic self-supervised learning approach to improve performance across multiple financial tasks. Our experiments show that DynMIX outperforms other state-of-the-art models on multiple forecasting tasks, and investment and risk management applications.