Stock-UniBERT: A News-based Cost-sensitive Ensemble BERT Model for Stock Trading

Abstract

Financial news plays an important role in investors' decisions and then influences stock markets. Previous studies mainly focus on establishing sentiment index from financial text and then making stock return prediction and trading strategy based on the index. This procedure demands costly manual label and may not directly correspond to actual stock market reaction. This paper solves this problem by using labels of stocks' residual return as sentiment labels for BERT model training. Distinct from ordinary task, buying or selling action will be taken after judgement of the stock news' sentiment. Hence, weighted cross-entropy loss and cost-sensitive accuracy are used to reveal influence and cost of judgement. Different settings of weighted cross-entropy loss are applied to learn self-adaptively and a selection method is designed to seek capable base classifiers for ensemble learning. This paper then develops a stock trading strategy based on the ensemble BERT model. Experiments and ablation study show the robust effectiveness of our strategy.