Event-Driven Learning of Systematic Behaviours in Stock Markets

TL;DR

This paper presents a neural network-based approach leveraging financial news and event streams to predict stock market movements, achieving higher accuracy and returns than existing models.

Contribution

It introduces a novel pipeline combining event extraction, BERT/ALBERT representations, and hierarchical attention for systematic stock market behavior detection.

Findings

Improved prediction accuracy over state-of-the-art models.

Higher simulated annualized returns in stock index predictions.

Effective detection of latent event-stock linkages.

Abstract

It is reported that financial news, especially financial events expressed in news, provide information to investors' long/short decisions and influence the movements of stock markets. Motivated by this, we leverage financial event streams to train a classification neural network that detects latent event-stock linkages and stock markets' systematic behaviours in the U.S. stock market. Our proposed pipeline includes (1) a combined event extraction method that utilizes Open Information Extraction and neural co-reference resolution, (2) a BERT/ALBERT enhanced representation of events, and (3) an extended hierarchical attention network that includes attentions on event, news and temporal levels. Our pipeline achieves significantly better accuracies and higher simulated annualized returns than state-of-the-art models when being applied to predicting Standard\&Poor 500, Dow Jones, Nasdaq indices and 10 individual stocks.