A Multimodal Embedding-Based Approach to Industry Classification in Financial Markets

TL;DR

This paper introduces a multimodal neural model that combines historical pricing data and financial news to create objective company embeddings, improving industry classification accuracy in financial markets.

Contribution

It presents a novel multimodal neural approach for training company embeddings that integrate diverse data sources for better industry classification.

Findings

Embeddings capture nuanced company relationships.

Improved accuracy in industry classification.

Demonstrated utility through case studies.

Abstract

Industry classification schemes provide a taxonomy for segmenting companies based on their business activities. They are relied upon in industry and academia as an integral component of many types of financial and economic analysis. However, even modern classification schemes have failed to embrace the era of big data and remain a largely subjective undertaking prone to inconsistency and misclassification. To address this, we propose a multimodal neural model for training company embeddings, which harnesses the dynamics of both historical pricing data and financial news to learn objective company representations that capture nuanced relationships. We explain our approach in detail and highlight the utility of the embeddings through several case studies and application to the downstream task of industry classification.