Stock Recommendations for Individual Investors: A Temporal Graph Network Approach with Mean-Variance Efficient Sampling

ArXiv ID: 2404.07223 “View on arXiv”

Authors: Unknown

Abstract

Recommender systems can be helpful for individuals to make well-informed decisions in complex financial markets. While many studies have focused on predicting stock prices, even advanced models fall short of accurately forecasting them. Additionally, previous studies indicate that individual investors often disregard established investment theories, favoring their personal preferences instead. This presents a challenge for stock recommendation systems, which must not only provide strong investment performance but also respect these individual preferences. To create effective stock recommender systems, three critical elements must be incorporated: 1) individual preferences, 2) portfolio diversification, and 3) the temporal dynamics of the first two. In response, we propose a new model, Portfolio Temporal Graph Network Recommender PfoTGNRec, which can handle time-varying collaborative signals and incorporates diversification-enhancing sampling. On real-world individual trading data, our approach demonstrates superior performance compared to state-of-the-art baselines, including cutting-edge dynamic embedding models and existing stock recommendation models. Indeed, we show that PfoTGNRec is an effective solution that can balance customer preferences with the need to suggest portfolios with high Return-on-Investment. The source code and data are available at https://github.com/youngandbin/PfoTGNRec.

Keywords: Recommender System, Portfolio Diversification, Temporal Graph Networks, Dynamic Embedding, Individual Preferences, Equities

Complexity vs Empirical Score

• Math Complexity: 6.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper uses advanced graph neural networks (temporal graph networks) and optimization theory (mean-variance efficient sampling), indicating substantial math complexity. It is evaluated on real-world individual trading data with source code and data available, demonstrating strong empirical backing and implementation readiness.

  flowchart TD
    A["Research Goal: <br>Stock Recommendations for Individual Investors<br>1. Individual Preferences 2. Portfolio Diversification 3. Temporal Dynamics"] --> B
    
    subgraph B ["Methodology: PfoTGNRec Model"]
        B1["Temporal Graph Network"] --> B2["Dynamic Embedding"] --> B3["Diversification-Enhanced Sampling"]
    end
    
    C["Input Data: <br>Real-world Individual Trading Data"] --> B
    
    B --> D{"Computational Process: <br>Time-Varying Collaborative Signals<br>+<br>Mean-Variance Efficient Sampling"}
    
    D --> E
    
    subgraph E ["Key Findings/Outcomes"]
        E1["Superior Performance<br>vs. State-of-the-Art Baselines"]
        E2["Balanced Trade-off:<br>Preferences + High ROI"]
        E3["Effective Solution<br>for Dynamic Stock Recommendations"]
    end