Publication Type

Conference Proceeding Article

Version

publishedVersion

Publication Date

12-2022

Abstract

Many financial f orecasting d eep l earning w orks 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 - f orecasts o f 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-ofthe-art models on multiple forecasting tasks, and investment and risk management applications.

Keywords

Graph neural networks, transformers, attention mechanisms, time-series forecasting, networks, multimodality, embeddings, finance

Discipline

Databases and Information Systems | OS and Networks

Research Areas

Data Science and Engineering

Publication

Proceedings of the 2022 IEEE International Conference on Big Data (Big Data), Osaka, Japan, December 17-20

First Page

825

Last Page

834

ISBN

9781665480468

Identifier

10.1109/BigData55660.2022.10020722

Publisher

IEEE

City or Country

Piscataway, NJ

Additional URL

https://doi.org/10.1109/BigData55660.2022.10020722

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