Leveraging Financial News for Stock Trend Prediction with Attention-Based Recurrent Neural Network

TL;DR

This paper introduces an attention-based bidirectional LSTM model that leverages financial news to improve stock trend prediction, capturing semantic and contextual information to outperform traditional methods.

Contribution

It presents a novel deep neural network approach using attention mechanisms to extract meaningful features from news text for stock prediction, surpassing existing sentiment-based models.

Findings

The model achieves competitive accuracy in predicting S&P 500 and individual stocks.

Attention mechanisms improve the relevance of extracted features.

Deep NLP techniques enhance financial forecasting performance.

Abstract

Stock market prediction is one of the most attractive research topic since the successful prediction on the market's future movement leads to significant profit. Traditional short term stock market predictions are usually based on the analysis of historical market data, such as stock prices, moving averages or daily returns. However, financial news also contains useful information on public companies and the market. Existing methods in finance literature exploit sentiment signal features, which are limited by not considering factors such as events and the news context. We address this issue by leveraging deep neural models to extract rich semantic features from news text. In particular, a Bidirectional-LSTM are used to encode the news text and capture the context information, self attention mechanism are applied to distribute attention on most relative words, news and days. In terms of predicting directional changes in both Standard & Poor's 500 index and individual companies stock price, we show that this technique is competitive with other state of the art approaches, demonstrating the effectiveness of recent NLP technology advances for computational finance.