Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph
with Hierarchical Graph Neural Networks
- URL: http://arxiv.org/abs/2301.13492v1
- Date: Tue, 31 Jan 2023 09:17:13 GMT
- Title: Company-as-Tribe: Company Financial Risk Assessment on Tribe-Style Graph
with Hierarchical Graph Neural Networks
- Authors: Wendong Bi, Bingbing Xu, Xiaoqian Sun, Zidong Wang, Huawei Shen, Xueqi
Cheng
- Abstract summary: Company financial risk is ubiquitous and early risk assessment for listed companies can avoid considerable losses.
Traditional methods mainly focus on the financial statements of companies and lack the complex relationships among them.
We propose a novel Hierarchical Graph Neural Network (TH-GNN) for Tribe-style graphs via two levels, with the first level to encode the structure pattern of the tribes with contrastive learning, and the second level to diffuse information based on the inter-tribe relations.
- Score: 62.94317686301643
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Company financial risk is ubiquitous and early risk assessment for listed
companies can avoid considerable losses. Traditional methods mainly focus on
the financial statements of companies and lack the complex relationships among
them. However, the financial statements are often biased and lagged, making it
difficult to identify risks accurately and timely. To address the challenges,
we redefine the problem as \textbf{company financial risk assessment on
tribe-style graph} by taking each listed company and its shareholders as a
tribe and leveraging financial news to build inter-tribe connections. Such
tribe-style graphs present different patterns to distinguish risky companies
from normal ones. However, most nodes in the tribe-style graph lack attributes,
making it difficult to directly adopt existing graph learning methods (e.g.,
Graph Neural Networks(GNNs)). In this paper, we propose a novel Hierarchical
Graph Neural Network (TH-GNN) for Tribe-style graphs via two levels, with the
first level to encode the structure pattern of the tribes with contrastive
learning, and the second level to diffuse information based on the inter-tribe
relations, achieving effective and efficient risk assessment. Extensive
experiments on the real-world company dataset show that our method achieves
significant improvements on financial risk assessment over previous competing
methods. Also, the extensive ablation studies and visualization comprehensively
show the effectiveness of our method.
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