Stock Prediction: a method based on extraction of news features and recurrent neural networks

TL;DR

This paper introduces a novel stock prediction approach combining news feature extraction based on sentiment polarity with a recurrent neural network to improve prediction accuracy over traditional methods.

Contribution

It presents a new method for extracting news features using sentiment analysis and integrates them with a recurrent neural network for enhanced stock prediction.

Findings

Over 5% improvement in prediction accuracy compared to SVM baseline.

Effective extraction of news sentiment features improves stock forecasting.

Recurrent neural networks effectively model sequential news and price data.

Abstract

This paper proposed a method for stock prediction. In terms of feature extraction, we extract the features of stock-related news besides stock prices. We first select some seed words based on experience which are the symbols of good news and bad news. Then we propose an optimization method and calculate the positive polar of all words. After that, we construct the features of news based on the positive polar of their words. In consideration of sequential stock prices and continuous news effects, we propose a recurrent neural network model to help predict stock prices. Compared to SVM classifier with price features, we find our proposed method has an over 5% improvement on stock prediction accuracy in experiments.