Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data
This addresses stock prediction for financial analysts, but it appears incremental as it builds on existing transformer methods with specific enhancements.
The paper tackled stock movement prediction by introducing Higher Order Transformers, a novel architecture for multimodal time-series data, achieving effective results on the Stocknet dataset.
In this paper, we tackle the challenge of predicting stock movements in financial markets by introducing Higher Order Transformers, a novel architecture designed for processing multivariate time-series data. We extend the self-attention mechanism and the transformer architecture to a higher order, effectively capturing complex market dynamics across time and variables. To manage computational complexity, we propose a low-rank approximation of the potentially large attention tensor using tensor decomposition and employ kernel attention, reducing complexity to linear with respect to the data size. Additionally, we present an encoder-decoder model that integrates technical and fundamental analysis, utilizing multimodal signals from historical prices and related tweets. Our experiments on the Stocknet dataset demonstrate the effectiveness of our method, highlighting its potential for enhancing stock movement prediction in financial markets.