Explainable Text-Driven Neural Network for Stock Prediction
This addresses the need for explainable AI in financial market prediction, which is incremental as it builds on existing news-driven methods by adding interpretability.
The paper tackles the problem of predicting stock prices using financial news by proposing a dual-layer attention-based neural network that not only predicts but also explains the predictions, demonstrating superiority over state-of-the-art methods in empirical studies.
It has been shown that financial news leads to the fluctuation of stock prices. However, previous work on news-driven financial market prediction focused only on predicting stock price movement without providing an explanation. In this paper, we propose a dual-layer attention-based neural network to address this issue. In the initial stage, we introduce a knowledge-based method to adaptively extract relevant financial news. Then, we use input attention to pay more attention to the more influential news and concatenate the day embeddings with the output of the news representation. Finally, we use an output attention mechanism to allocate different weights to different days in terms of their contribution to stock price movement. Thorough empirical studies based upon historical prices of several individual stocks demonstrate the superiority of our proposed method in stock price prediction compared to state-of-the-art methods.