Event-Driven Learning of Systematic Behaviours in Stock Markets
This work addresses stock market prediction for investors, but it is incremental as it builds on existing methods with enhancements.
The paper tackled the problem of predicting stock market movements by leveraging financial event streams, achieving significantly better accuracies and higher simulated annualized returns compared to state-of-the-art models on major indices and individual stocks.
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.