A Hybrid Deep Learning Framework for Stock Price Prediction Considering the Investor Sentiment of Online Forum Enhanced by Popularity

TL;DR

This paper introduces a hybrid deep learning framework that combines investor sentiment analysis from online forums, including post popularity, with technical indicators to improve stock price prediction accuracy.

Contribution

It presents a novel hybrid deep learning model integrating sentiment analysis and technical indicators for stock prediction, enhancing prediction accuracy over traditional methods.

Findings

The framework outperforms baseline models in predicting stock prices.

Investor sentiment and post popularity significantly influence stock movements.

The approach is validated on Chinese stock market data.

Abstract

Stock price prediction has always been a difficult task for forecasters. Using cutting-edge deep learning techniques, stock price prediction based on investor sentiment extracted from online forums has become feasible. We propose a novel hybrid deep learning framework for predicting stock prices. The framework leverages the XLNET model to analyze the sentiment conveyed in user posts on online forums, combines these sentiments with the post popularity factor to compute daily group sentiments, and integrates this information with stock technical indicators into an improved BiLSTM-highway model for stock price prediction. Through a series of comparative experiments involving four stocks on the Chinese stock market, it is demonstrated that the hybrid framework effectively predicts stock prices. This study reveals the necessity of analyzing investors' textual views for stock price prediction.