# Explainable Text-Driven Neural Network for Stock Prediction

arXiv: https://arxiv.org/abs/1902.04994 · HTML: https://mlsanity.com/paper/1902.04994/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Feb 2019.

## Built on by (most cited first, 13 of 13)

- [Trust in AI: Progress, Challenges, and Future Directions](https://mlsanity.com/paper/2403.14680/) (2024)
- [NumHTML: Numeric-Oriented Hierarchical Transformer Model for Multi-task Financial Forecasting](https://mlsanity.com/paper/2201.01770/) (2022)
- [A Survey of Explainable Artificial Intelligence (XAI) in Financial Time Series Forecasting](https://mlsanity.com/paper/2407.15909/) (2024)
- [A Comprehensive Review on Financial Explainable AI](https://mlsanity.com/paper/2309.11960/) (2023)
- [Ploutos: Towards interpretable stock movement prediction with financial large language model](https://mlsanity.com/paper/2403.00782/) (2024)
- [AMA-LSTM: Pioneering Robust and Fair Financial Audio Analysis for Stock Volatility Prediction](https://mlsanity.com/paper/2407.18324/) (2024)
- [Measuring Financial Time Series Similarity With a View to Identifying Profitable Stock Market Opportunities](https://mlsanity.com/paper/2107.03926/) (2021)
- [Measuring Consistency in Text-based Financial Forecasting Models](https://mlsanity.com/paper/2305.08524/) (2023)
- [Enhancing Few-Shot Stock Trend Prediction with Large Language Models](https://mlsanity.com/paper/2407.09003/) (2024)
- [Fact Check: Analyzing Financial Events from Multilingual News Sources](https://mlsanity.com/paper/2106.15221/) (2021)
- [FETILDA: An Effective Framework For Fin-tuned Embeddings For Long Financial Text Documents](https://mlsanity.com/paper/2206.06952/) (2022)
- [Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions](https://mlsanity.com/paper/2312.16223/) (2023)
- [Cross-Lingual News Event Correlation for Stock Market Trend Prediction](https://mlsanity.com/paper/2410.00024/) (2024)

## Abstract (arXiv)

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.