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A Temporal Graph Network Approach for Personalized Portfolio Recommendations

In: Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024)

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  • Ziyu Feng

    (Wuhan No. 39 Middle School)

Abstract

In volatile financial markets, individual investors face challenges as traditional recommendation systems focus on individual stocks and rely mainly on historical data, overlooking social media sentiment, news, and expert opinions. This paper presents a framework using temporal graph networks (TGN) to capture evolving stock dynamics and investor preferences. By integrating user preferences-such as risk tolerance and investment goals-with alternative data, the model offers personalized portfolio recommendations. Evaluated on a large dataset of stock prices, transactions, and alternative data, the framework outperforms traditional methods in risk-adjusted returns, diversification, and alignment with investor goals.

Suggested Citation

  • Ziyu Feng, 2024. "A Temporal Graph Network Approach for Personalized Portfolio Recommendations," Advances in Economics, Business and Management Research, in: Qiujing Wu & Songsong Liu & Guoliang Wang & Jia Li (ed.), Proceedings of the 2024 3rd International Conference on Public Service, Economic Management and Sustainable Development (PESD 2024), pages 520-526, Springer.
    • Handle: RePEc:spr:advbcp:978-94-6463-598-0_54
    DOI: 10.2991/978-94-6463-598-0_54
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