GraphCNNpred: A stock market indices prediction using a Graph based deep learning system

ArXiv ID: 2407.03760 “View on arXiv”

Authors: Unknown

Abstract

The application of deep learning techniques for predicting stock market prices is a prominent and widely researched topic in the field of data science. To effectively predict market trends, it is essential to utilize a diversified dataset. In this paper, we give a graph neural network based convolutional neural network (CNN) model, that can be applied on diverse source of data, in the attempt to extract features to predict the trends of indices of \text{“S”}&\text{“P”} 500, NASDAQ, DJI, NYSE, and RUSSEL. The experiments show that the associated models improve the performance of prediction in all indices over the baseline algorithms by about $4% \text{" to “} 15%$, in terms of F-measure. A trading simulation is generated from predictions and gained a Sharpe ratio of over 3.

Keywords: Graph Neural Networks (GNN), Convolutional Neural Networks (CNN), Stock market prediction, Time series forecasting, Indices, Indices

Complexity vs Empirical Score

• Math Complexity: 7.0/10
• Empirical Rigor: 8.0/10
• Quadrant: Holy Grail
• Why: The paper introduces advanced graph neural network architectures (GCN, GAT) with convolutional layers and mathematical formulations for message passing, indicating high math complexity. It also demonstrates empirical rigor by testing on multiple indices, reporting specific performance metrics (F-measure improvements, Sharpe ratio >3), and detailing a trading simulation.

  flowchart TD
    A["Research Goal: Predict stock market indices<br/>using diverse data sources with a<br/>Graph Neural Network & CNN model"] --> B["Data Collection & Graph Construction"]
    B --> C["Data: S&P 500, NASDAQ, DJI, NYSE, RUSSEL<br/>Input: Historical Price & Volume Data"]
    C --> D["Model Architecture: GraphCNNpred"]
    D --> E["Graph Convolution & Feature Extraction"]
    E --> F["Time Series Prediction<br/>(Trend Prediction)"]
    F --> G["Trading Simulation<br/>(Sharpe Ratio Analysis)"]
    G --> H["Key Outcomes: 4-15% Improvement in F-Measure<br/>Sharpe Ratio > 3 vs Baseline Algorithms"]