Predicting Stock Movement with BERTweet and Transformers
Michael Charles Albada, Mojolaoluwa Joshua Sonola
TL;DR
The paper tackles predicting stock movements by leveraging public information from Twitter alongside historical prices. It evaluates BERTweet embeddings within multiple transformer-based architectures on the StockNet dataset, replacing prior GRU-based components and incorporating both text and price signals. The cross-attention transformer achieved the strongest MCC (0.1114) and high accuracy (56.9%), establishing a new baseline without external data, while auxiliary-target variants offered higher accuracy at times. This work demonstrates the value of language-model-based representations for financial time-series and invites future exploration of hybrid approaches that also integrate adversarial sampling and graph-based signals for improved forecasting.
Abstract
Applying deep learning and computational intelligence to finance has been a popular area of applied research, both within academia and industry, and continues to attract active attention. The inherently high volatility and non-stationary of the data pose substantial challenges to machine learning models, especially so for today's expressive and highly-parameterized deep learning models. Recent work has combined natural language processing on data from social media to augment models based purely on historic price data to improve performance has received particular attention. Previous work has achieved state-of-the-art performance on this task by combining techniques such as bidirectional GRUs, variational autoencoders, word and document embeddings, self-attention, graph attention, and adversarial training. In this paper, we demonstrated the efficacy of BERTweet, a variant of BERT pre-trained specifically on a Twitter corpus, and the transformer architecture by achieving competitive performance with the existing literature and setting a new baseline for Matthews Correlation Coefficient on the Stocknet dataset without auxiliary data sources.