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arXiv · 2311.13864

Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework

Chunjing Gan·Binbin Hu·Bo Huang·Tianyu Zhao·Yingru Lin·Wenliang Zhong·Zhiqiang Zhang·Jun Zhou·Chuan Shi
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Abstract

In this paper, we highlight that both conformity and risk preference matter in making fund investment decisions beyond personal interest and seek to jointly characterize these aspects in a disentangled manner. Consequently, we develop a novel M ulti-granularity Graph Disentangled Learning framework named MGDL to effectively perform intelligent matching of fund investment products. Benefiting from the well-established fund graph and the attention module, multi-granularity user representations are derived from historical behaviors to separately express personal interest, conformity and risk preference in a fine-grained way. To attain stronger disentangled representations with specific semantics, MGDL explicitly involve two self-supervised signals, i.e., fund type based contrasts and fund popularity. Extensive experiments in offline and online environments verify the effectiveness of MGDL.

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Chunjing Gan, Binbin Hu, Bo Huang, Tianyu Zhao, Yingru Lin, Wenliang Zhong, Zhiqiang Zhang, Jun Zhou, Chuan Shi. 2023-11-23. Which Matters Most in Making Fund Investment Decisions? A Multi-granularity Graph Disentangled Learning Framework. https://arxiv.org/abs/2311.13864

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