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

ArXiv ID: 2309.06559 “View on arXiv”

Authors: Unknown

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.

Keywords: graph neural networks, sentiment analysis, hierarchical temporal, S&P 500, market prediction

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Holy Grail
• Why: The paper employs advanced deep learning architectures (GAT, LSTM) with mathematical formulations for attention mechanisms, while also presenting a detailed empirical setup with specific datasets, train/val/test splits, and backtesting results (28% improvement in cumulative returns).

  flowchart TD
    Start["Research Goal<br>Predict stock movements using<br>non-traditional media data"] --> Inputs["Data Inputs<br>Financial Data + Social Media<br>Posts & Stock Correlations"]
    Inputs --> GNN["Graph Neural Network<br>Model Relationships between Stocks"]
    GNN --> HTL["Hierarchical Temporal Learning<br>Analyze Time-based Media & Price Patterns"]
    HTL --> Sentiment["Sentiment Analysis<br>Quantify Social Media Sentiment"]
    Sentiment --> Prediction["Stock Movement Classification<br>Up vs Down Prediction"]
    Prediction --> Outcome["Key Outcome<br>28% Improvement in Cumulative Returns<br>S&P 500 Validation"]