{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T10:19:00Z","timestamp":1753870740529,"version":"3.41.2"},"reference-count":46,"publisher":"Wiley","issue":"6","license":[{"start":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T00:00:00Z","timestamp":1656979200000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/onlinelibrary.wiley.com\/termsAndConditions#vor"}],"content-domain":{"domain":["wires.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["WIREs Data Min &amp; Knowl"],"published-print":{"date-parts":[[2022,11]]},"abstract":"<jats:title>Abstract<\/jats:title><jats:p>Corporate investment is an important part of corporate financial decision\u2010making and affects the future profit and value of the corporation. Predicting corporate investment provides great significance for capital market investors to understand the future operation and development of a corporation. Many researchers have studied independent prediction methods. However, individual firms imitate each other's investment in the actual decision\u2010making process. This phenomenon of investment convergence indicates investment correlation among individual firms, which is ignored in these existing methods. In this article, we first identify key variables in multivariate sequences by our designed two\u2010way fixed effects model for precise corporate network construction. Then, we propose a weighted temporal graph neural network called weighted temporal graph neural network (WTGNN) for graph learning and investment prediction over the corporate network. WTGNN improves the graph convolution capability by weighted sampling with attention and multivariate time series aggregation. We conducted extensive experiments using real\u2010world financial reporting data. The results show that WTGNN can achieve excellent graph learning performance and outperforms existing methods in the investment prediction task.<\/jats:p><jats:p>This article is categorized under:<jats:list list-type=\"simple\">\n<jats:list-item><jats:p>Technologies &gt; Prediction<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Technologies &gt; Machine Learning<\/jats:p><\/jats:list-item>\n<jats:list-item><jats:p>Application Areas &gt; Business and Industry<\/jats:p><\/jats:list-item>\n<\/jats:list><\/jats:p>","DOI":"10.1002\/widm.1472","type":"journal-article","created":{"date-parts":[[2022,7,5]],"date-time":"2022-07-05T10:45:48Z","timestamp":1657017948000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Corporate investment prediction using a weighted temporal graph neural network"],"prefix":"10.1002","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9441-9851","authenticated-orcid":false,"given":"Jianing","family":"Li","sequence":"first","affiliation":[{"name":"School of Business Administration Northeastern University  Shenyang China"}]},{"given":"Xin","family":"Yao","sequence":"additional","affiliation":[{"name":"School of Computer Science and Engineering Northeastern University  Shenyang China"}]}],"member":"311","published-online":{"date-parts":[[2022,7,5]]},"reference":[{"doi-asserted-by":"publisher","key":"e_1_2_12_2_1","DOI":"10.2307\/2118364"},{"doi-asserted-by":"publisher","key":"e_1_2_12_3_1","DOI":"10.1155\/2020\/8813738"},{"doi-asserted-by":"publisher","key":"e_1_2_12_4_1","DOI":"10.1016\/j.jacceco.2009.09.001"},{"doi-asserted-by":"publisher","key":"e_1_2_12_5_1","DOI":"10.1086\/261849"},{"doi-asserted-by":"publisher","key":"e_1_2_12_6_1","DOI":"10.1109\/MSP.2017.2693418"},{"unstructured":"Bruna J. Zaremba W. Szlam A.&Lecun Y.(2014).Spectral networks and locally connected networks on graphs. International Conference on Learning Representations.","key":"e_1_2_12_7_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_8_1","DOI":"10.1287\/mnsc.2020.3695"},{"key":"e_1_2_12_9_1","article-title":"Effective deep attributed network representation learning with topology adapted smoothing","author":"Chen J.","year":"2021","journal-title":"IEEE Transactions on Cybernetics"},{"doi-asserted-by":"publisher","key":"e_1_2_12_10_1","DOI":"10.1016\/j.cjar.2016.11.002"},{"doi-asserted-by":"crossref","unstructured":"Chiang W.\u2010L. Liu X. Si S. Li Y. Bengio S.&Hsieh C.\u2010J.(2019).Cluster\u2010GCN: An efficient algorithm for training deep and large graph convolutional networks.Proceedings of the 25thACM SIGKDD international conference on knowledge discovery and data mining pp. 257\u2013266.","key":"e_1_2_12_11_1","DOI":"10.1145\/3292500.3330925"},{"doi-asserted-by":"publisher","key":"e_1_2_12_12_1","DOI":"10.1080\/00018730601170527"},{"doi-asserted-by":"publisher","key":"e_1_2_12_13_1","DOI":"10.1002\/widm.1285"},{"unstructured":"Donahue J. Jia Y. Vinyals O. Hoffman J. Zhang N. Tzeng E.&Darrell T.(2014).DeCAF: A deep convolutional activation feature for generic visual recognition.Proceedings of the 31th international conference on machine learning Vol. 32 pp. 647\u2013655.","key":"e_1_2_12_14_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_15_1","DOI":"10.1287\/mnsc.2016.2433"},{"doi-asserted-by":"crossref","unstructured":"Ge C.(2019).A LSTM and graph CNN combined network for community house price forecasting.20thIEEE international conference on mobile data management pp. 393\u2013394.","key":"e_1_2_12_16_1","DOI":"10.1109\/MDM.2019.00-15"},{"unstructured":"Gori M. Monfardini G.&Scarselli F.(2005).A new model for learning in graph domains.IEEE International Joint Conference on Neural Networks Vol. 2 pp. 729\u2013734.","key":"e_1_2_12_17_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_18_1","DOI":"10.1016\/j.neucom.2020.06.001"},{"unstructured":"Hamilton W. L. Ying Z.&Leskovec J.(2017).Inductive representation learning on large graphs.Advances in neural information processing systems 30: annual conference on neural information processing systems pp. 1024\u20131034.","key":"e_1_2_12_19_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_20_1","DOI":"10.1007\/s00500-020-04954-0"},{"doi-asserted-by":"publisher","key":"e_1_2_12_21_1","DOI":"10.1162\/neco.1997.9.8.1735"},{"doi-asserted-by":"crossref","unstructured":"Ibrahimi K.&Serbouti Y.(2017).Prediction of the content popularity in the 5G network: Auto\u2010regressive moving\u2010average and exponential smoothing approaches.International conference on wireless networks and mobile communications pp. 1\u20137.","key":"e_1_2_12_22_1","DOI":"10.1109\/WINCOM.2017.8238196"},{"unstructured":"Kingma D. P.&Jimmy B.(2015).Adam: A method for stochastic optimization.International conference on learning representations.","key":"e_1_2_12_23_1"},{"unstructured":"Kipf T. N.&Welling M.(2017).Semi\u2010supervised classification with graph convolutional networks.5th international conference on learning representations.","key":"e_1_2_12_24_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_25_1","DOI":"10.1111\/jofi.12094"},{"doi-asserted-by":"crossref","unstructured":"Li S. Zhou J. Xu T. Huang L. Wang F. Xiong H. Huang W. Dou D.&Xiong H.(2021).Structure\u2010aware interactive graph neural networks for the prediction of protein\u2010ligand binding affinity.The 27th ACM SIGKDD conference on knowledge discovery and data mining pp. 975\u2013985.","key":"e_1_2_12_26_1","DOI":"10.1145\/3447548.3467311"},{"doi-asserted-by":"publisher","key":"e_1_2_12_27_1","DOI":"10.1016\/j.knosys.2020.106618"},{"doi-asserted-by":"crossref","unstructured":"Obthong M. Tantisantiwong N. Jeamwatthanachai W.&Wills G. B.(2020).A survey on machine learning for stock price prediction: Algorithms and techniques.Proceedings of the 2nd international conference on finance economics management andIT business pp. 63\u201371.","key":"e_1_2_12_28_1","DOI":"10.5220\/0009340700630071"},{"doi-asserted-by":"publisher","key":"e_1_2_12_29_1","DOI":"10.1016\/j.najef.2017.03.001"},{"unstructured":"Paszke A. Gross S. Massa F. Lerer A. Bradbury J. Chanan G. Killeen T. Lin Z. Gimelshein N. Antiga L. Desmaison A. K\u00f6pf A. Yang E. DeVito Z. Raison M. Tejani A. Chilamkurthy S. Steiner B. Fang L. \u2026Chintala S.(2019).PyTorch: an imperative style high\u2010performance deep learning library.Advances in neural information processing systems 32: Annual conference on neural information processing systems pp. 8024\u20138035.","key":"e_1_2_12_30_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_31_1","DOI":"10.1007\/s11142-006-9012-1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_32_1","DOI":"10.1109\/TNN.2008.2005605"},{"doi-asserted-by":"crossref","unstructured":"Shen F.&Luo N.(2017).Investment time series prediction using a hybrid model based on RBMs and pattern clustering.16th IEEE\/ACIS international conference on computer and information science pp. 347\u2013352.","key":"e_1_2_12_33_1","DOI":"10.1109\/ICIS.2017.7960017"},{"doi-asserted-by":"publisher","key":"e_1_2_12_34_1","DOI":"10.1093\/rfs\/hht019"},{"doi-asserted-by":"publisher","key":"e_1_2_12_35_1","DOI":"10.1002\/widm.1404"},{"doi-asserted-by":"publisher","key":"e_1_2_12_36_1","DOI":"10.1016\/j.ins.2021.08.100"},{"doi-asserted-by":"publisher","key":"e_1_2_12_37_1","DOI":"10.2307\/1991374"},{"doi-asserted-by":"crossref","unstructured":"Unadkat V. Sayani P. Kanani P.&Doshi P.(2018).Deep learning for financial prediction.International Conference on Circuits and Systems in Digital Enterprise Technology.","key":"e_1_2_12_38_1","DOI":"10.1109\/ICCSDET.2018.8821178"},{"doi-asserted-by":"publisher","key":"e_1_2_12_39_1","DOI":"10.1109\/TCYB.2017.2734043"},{"doi-asserted-by":"crossref","unstructured":"White H.(1988).Economic prediction using neural networks: The case of IBM daily stock returns.Proceedings of international conference on neural networks pp. 451\u2013458.","key":"e_1_2_12_40_1","DOI":"10.1109\/ICNN.1988.23959"},{"doi-asserted-by":"publisher","key":"e_1_2_12_41_1","DOI":"10.1109\/TNNLS.2020.2978386"},{"doi-asserted-by":"publisher","key":"e_1_2_12_42_1","DOI":"10.1142\/S021962201841002X"},{"unstructured":"Xue G. Zhong M. Li J. Chen J. Zhai C.&Kong R.(2021).Dynamic network embedding survey.CoRR abs\/2103.15447.","key":"e_1_2_12_43_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_44_1","DOI":"10.1109\/TKDE.2021.3101356"},{"doi-asserted-by":"crossref","unstructured":"Yu B. Yin H.&Zhu Z.(2018).Spatio\u2010temporal graph convolutional networks: A deep learning framework for traffic forecasting.Proceedings of the twenty\u2010seventh international joint conference on artificial intelligence pp. 3634\u20133640.","key":"e_1_2_12_45_1","DOI":"10.24963\/ijcai.2018\/505"},{"unstructured":"Zaremba W. Sutskever I.&Vinyals O.(2014).Recurrent neural network regularization.CoRR abs\/1409.2329.","key":"e_1_2_12_46_1"},{"doi-asserted-by":"publisher","key":"e_1_2_12_47_1","DOI":"10.1007\/s10490-010-9242-4"}],"container-title":["WIREs Data Mining and Knowledge Discovery"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/widm.1472","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/widm.1472","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/wires.onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/widm.1472","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,22]],"date-time":"2023-08-22T09:47:37Z","timestamp":1692697657000},"score":1,"resource":{"primary":{"URL":"https:\/\/wires.onlinelibrary.wiley.com\/doi\/10.1002\/widm.1472"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,7,5]]},"references-count":46,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2022,11]]}},"alternative-id":["10.1002\/widm.1472"],"URL":"https:\/\/doi.org\/10.1002\/widm.1472","archive":["Portico"],"relation":{},"ISSN":["1942-4787","1942-4795"],"issn-type":[{"type":"print","value":"1942-4787"},{"type":"electronic","value":"1942-4795"}],"subject":[],"published":{"date-parts":[[2022,7,5]]},"assertion":[{"value":"2021-11-21","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-12-17","order":1,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2022-07-05","order":2,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e1472"}}