Explainable Text-Driven Neural Network for Stock Prediction

TL;DR

This paper introduces an explainable neural network model that uses attention mechanisms to incorporate financial news into stock prediction, providing interpretability and improved accuracy over existing methods.

Contribution

The paper presents a dual-layer attention-based neural network that adaptively extracts relevant news and explains stock movements, advancing interpretability in news-driven stock prediction models.

Findings

Outperforms state-of-the-art methods in stock prediction accuracy.

Effectively identifies influential news and days contributing to stock movements.

Provides interpretable insights into news impact on stock prices.

Abstract

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.