{"type":"ScholarlyArticle","title":"Interpret ESG Rating’s Impact on the Industrial Chain Using Graph Neural Networks","authors":[{"name":"Bin Liu","slug":"bin-liu","given":"Bin","family":"Liu","affiliation":"School of Statistics , Southwestern University of Finance and Economics , Chengdu , China"},{"name":"Jiujun He","slug":"jiujun-he","given":"Jiujun","family":"He","affiliation":"School of Statistics , Southwestern University of Finance and Economics , Chengdu , China"},{"name":"Ziyuan Li","slug":"ziyuan-li","given":"Ziyuan","family":"Li","affiliation":"School of Statistics , Southwestern University of Finance and Economics , Chengdu , China"},{"name":"Xiaoyang Huang","slug":"xiaoyang-huang","given":"Xiaoyang","family":"Huang","affiliation":"School of Statistics , Southwestern University of Finance and Economics , Chengdu , China"},{"name":"Xiang Zhang","slug":"xiang-zhang","given":"Xiang","family":"Zhang","affiliation":"School of Finance , Southwestern University of Finance and Economics , Chengdu , China"},{"name":"Guosheng Yin","slug":"guosheng-yin","given":"Guosheng","family":"Yin","affiliation":"Department of Mathematics , Faculty of Natural Sciences , Imperial College London , London , UK"}],"abstract":"We conduct a quantitative analysis of the development of the industry chain from the environmental, social, and governance (ESG) perspective, which is an overall measure of sustainability.  Factors that may impact the performance of the industrial chain have been studied in the literature, such as government regulation, monetary policy, etc. Our interest lies in how the sustainability change (i.e., ESG shock) affects the performance of the industrial chain. To achieve this goal, we model the industrial chain with a graph neural network (GNN) and conduct node regression on two financial performance metrics, namely, the aggregated profitability ratios and operating margin. To quantify the effects of ESG, we propose to compute the interaction between ESG shocks and industrial chain features with a cross-attention module, and then filter the original node features in the graph regression. Experiments on two real datasets demonstrate that (i) there are significant effects of ESG shocks on the industrial chain, and (ii) model parameters including regression coefficients and the attention map can explain how ESG shocks affect the  performance of the industrial chain.","doi":"10.24963/ijcai.2023/674","url":"https://ijcai.org/proceedings/2023/674","pdf":"https://ijcai.org/proceedings/2023/0674.pdf","markdown":"https://ijcai.org/proceedings/2023/674.md","bibtex":"https://ijcai.org/proceedings/2023/bibtex/674","volume":{"slug":"2023","title":"Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2023","url":"https://ijcai.org/proceedings/2023/","year":2023,"location":"Macao, SAR","dates":"19-25 August 2023","editors":"Edith Elkind","isbn":"978-1-956792-03-4","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2023"},"section":"AI for Good","track":"AI for Good","number":674,"pages":{"first":6076,"last":6084},"keywords":["AI for Good: Machine Learning","AI for Good: Multidisciplinary Topics and Applications"],"videos":[],"references":[{"position":1,"raw":"[Amiraslani et al., 2017] Hami Amiraslani, Karl V Lins, Henri Servaes, and Ane Tamayo. 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