Media Moments and Corporate Connections: A Deep Learning Approach to Stock Movement Classification

TL;DR

This paper presents a deep learning model that integrates financial data, social media sentiment, and stock correlations using a graph neural network to improve stock movement prediction, demonstrating a 28% increase in cumulative returns.

Contribution

It introduces a novel hierarchical temporal graph neural network that combines diverse data sources for stock movement classification, including social media and stock correlations.

Findings

28% improvement in cumulative returns

Effective integration of social media sentiment

Enhanced stock movement prediction accuracy

Abstract

The financial industry poses great challenges with risk modeling and profit generation. These entities are intricately tied to the sophisticated prediction of stock movements. A stock forecaster must untangle the randomness and ever-changing behaviors of the stock market. Stock movements are influenced by a myriad of factors, including company history, performance, and economic-industry connections. However, there are other factors that aren't traditionally included, such as social media and correlations between stocks. Social platforms such as Reddit, Facebook, and X (Twitter) create opportunities for niche communities to share their sentiment on financial assets. By aggregating these opinions from social media in various mediums such as posts, interviews, and news updates, we propose a more holistic approach to include these "media moments" within stock market movement prediction. We introduce a method that combines financial data, social media, and correlated stock relationships via a graph neural network in a hierarchical temporal fashion. Through numerous trials on current S&P 500 index data, with results showing an improvement in cumulative returns by 28%, we provide empirical evidence of our tool's applicability for use in investment decisions.