TY - GEN
T1 - The Network of Mutual Funds
T2 - 4th ACM International Conference on AI in Finance, ICAIF 2023
AU - Jiang, Siqi
AU - Uddin, Ajim
AU - Wei, Zhi
AU - Yu, Dantong
N1 - Publisher Copyright:
© 2023 Owner/Author.
PY - 2023/11/27
Y1 - 2023/11/27
N2 - Mutual funds are interconnected to each other through multiple types of links, including but not limited to co-investment, advisors, firms, and managers. These connections enable information flow among network entities, influence investment decisions, and ultimately impact mutual fund managers' performance. In this paper, we propose a dynamic graph neural network approach to model these heterogeneous relationships and their contributions to mutual fund performance. Using the graph attention mechanism, our model learns latent embedding for mutual funds and their invested assets dynamically in each month and then uses the embedding to estimate future returns. Empirical analysis confirms that the proposed method outperforms the state-of-the-art DeepWalk model by 10%. Furthermore, this study also reveals the importance of networks in mutual fund performance. The inclusion of network connections in a feedforward machine learning model significantly increases the performance of the model by 118%. Finally, portfolio analysis and regression estimation on next month's excess return show that the proposed approach has a significant economic contribution over current benchmark approaches.
AB - Mutual funds are interconnected to each other through multiple types of links, including but not limited to co-investment, advisors, firms, and managers. These connections enable information flow among network entities, influence investment decisions, and ultimately impact mutual fund managers' performance. In this paper, we propose a dynamic graph neural network approach to model these heterogeneous relationships and their contributions to mutual fund performance. Using the graph attention mechanism, our model learns latent embedding for mutual funds and their invested assets dynamically in each month and then uses the embedding to estimate future returns. Empirical analysis confirms that the proposed method outperforms the state-of-the-art DeepWalk model by 10%. Furthermore, this study also reveals the importance of networks in mutual fund performance. The inclusion of network connections in a feedforward machine learning model significantly increases the performance of the model by 118%. Finally, portfolio analysis and regression estimation on next month's excess return show that the proposed approach has a significant economic contribution over current benchmark approaches.
KW - Fintech
KW - Graph Neural Networks
KW - Mutual Fund Prediction
KW - heterogeneous Graph
UR - http://www.scopus.com/inward/record.url?scp=85179850923&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85179850923&partnerID=8YFLogxK
U2 - 10.1145/3604237.3626910
DO - 10.1145/3604237.3626910
M3 - Conference contribution
AN - SCOPUS:85179850923
T3 - ICAIF 2023 - 4th ACM International Conference on AI in Finance
SP - 235
EP - 243
BT - ICAIF 2023 - 4th ACM International Conference on AI in Finance
PB - Association for Computing Machinery, Inc
Y2 - 27 November 2023 through 29 November 2023
ER -