{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,28]],"date-time":"2026-01-28T23:00:02Z","timestamp":1769641202793,"version":"3.49.0"},"publisher-location":"New York, NY, USA","reference-count":34,"publisher":"ACM","content-domain":{"domain":["dl.acm.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,11,15]]},"DOI":"10.1145\/3768292.3770421","type":"proceedings-article","created":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:24:26Z","timestamp":1763105066000},"page":"422-430","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["DeltaLag: Learning Dynamic Lead-Lag Patterns in Financial Markets"],"prefix":"10.1145","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-8160-9107","authenticated-orcid":false,"given":"Wanyun","family":"Zhou","sequence":"first","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8175-8451","authenticated-orcid":false,"given":"Saizhuo","family":"Wang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology, Hong Kong SAR, China and International Digital Economy Academy, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8464-2152","authenticated-orcid":false,"given":"Mihai","family":"Cucuringu","sequence":"additional","affiliation":[{"name":"Department of Mathematics, University of California, Los Angeles, Los Angeles, USA and Department of Statistics & Oxford Man Institute of Quantitative Finance, University of Oxford, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7950-7386","authenticated-orcid":false,"given":"Zihao","family":"Zhang","sequence":"additional","affiliation":[{"name":"University of Oxford, Oxford, United Kingdom"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-2879-081X","authenticated-orcid":false,"given":"Xiang","family":"Li","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-5046-2588","authenticated-orcid":false,"given":"Jian","family":"Guo","sequence":"additional","affiliation":[{"name":"International Digital Economy Academy, Shenzhen, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0013-336X","authenticated-orcid":false,"given":"Chao","family":"Zhang","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9745-4372","authenticated-orcid":false,"given":"Xiaowen","family":"Chu","sequence":"additional","affiliation":[{"name":"The Hong Kong University of Science and Technology (Guangzhou), Guangzhou, China"}]}],"member":"320","published-online":{"date-parts":[[2025,11,14]]},"reference":[{"key":"e_1_3_3_2_2_2","doi-asserted-by":"crossref","unstructured":"Jakob Albers Mihai Cucuringu Sam Howison and Alexander\u00a0Y Shestopaloff. 2021. Fragmentation price formation and cross-impact in bitcoin markets. Applied Mathematical Finance 28 5 (2021) 395\u2013448.","DOI":"10.1080\/1350486X.2022.2080083"},{"key":"e_1_3_3_2_3_2","doi-asserted-by":"crossref","unstructured":"Stefanos Bennett Mihai Cucuringu and Gesine Reinert. 2022. Lead\u2013lag detection and network clustering for multivariate time series with an application to the US equity market. Machine Learning 111 12 (2022) 4497\u20134538.","DOI":"10.1007\/s10994-022-06250-4"},{"key":"e_1_3_3_2_4_2","doi-asserted-by":"crossref","unstructured":"Giuseppe Buccheri Fulvio Corsi and Stefano Peluso. 2021. High-frequency lead-lag effects and cross-asset linkages: a multi-asset lagged adjustment model. Journal of Business & Economic Statistics 39 3 (2021) 605\u2013621.","DOI":"10.1080\/07350015.2019.1697699"},{"key":"e_1_3_3_2_5_2","doi-asserted-by":"crossref","unstructured":"\u00c1lvaro Cartea Mihai Cucuringu and Qi Jin. 2023. Detecting Lead-Lag Relationships in Stock Returns and Portfolio Strategies. (October 2023). Available at SSRN: https:\/\/ssrn.com\/abstract=4599565.","DOI":"10.2139\/ssrn.4599565"},{"key":"e_1_3_3_2_6_2","volume-title":"Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)","author":"Cucuringu M.","year":"2020","unstructured":"M. Cucuringu, H. Li, H. Sun, and L. Zanetti. 2020. Hermitian matrices for clustering directed graphs: insights and applications. In Proceedings of the 23rd International Conference on Artificial Intelligence and Statistics (AISTATS 2020)."},{"key":"e_1_3_3_2_7_2","doi-asserted-by":"crossref","unstructured":"Giovanni De\u00a0Luca and Federica Pizzolante. 2021. Detecting leaders country from road transport emission time-series. Environments 8 3 (2021) 18.","DOI":"10.3390\/environments8030018"},{"key":"e_1_3_3_2_8_2","doi-asserted-by":"publisher","unstructured":"Ningyuan Fan Zhi-Ping Fan Yongli Li and Meng Li. 2022. Does the lead-lag effect exist in stock markets? Applied Economics Letters 29 10 (June 2022) 895\u2013900. 10.1080\/13504851.2021.1897068Publisher: Routledge _eprint: https:\/\/doi.org\/10.1080\/13504851.2021.1897068.","DOI":"10.1080\/13504851.2021.1897068"},{"key":"e_1_3_3_2_9_2","doi-asserted-by":"crossref","unstructured":"Fuli Feng Xiangnan He Xiang Wang Cheng Luo Yiqun Liu and Tat-Seng Chua. 2019. Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS) 37 2 (2019) 1\u201330.","DOI":"10.1145\/3309547"},{"key":"e_1_3_3_2_10_2","doi-asserted-by":"crossref","unstructured":"Sepp Hochreiter and J\u00fcrgen Schmidhuber. 1997. Long short-term memory. Neural computation 9 8 (1997) 1735\u20131780. Publisher: MIT Press.","DOI":"10.1162\/neco.1997.9.8.1735"},{"key":"e_1_3_3_2_11_2","doi-asserted-by":"crossref","unstructured":"Yongli Li Chao Liu Tianchen Wang and Baiqing Sun. 2021. Dynamic patterns of daily lead-lag networks in stock markets. Quantitative Finance 21 12 (2021) 2055\u20132068.","DOI":"10.1080\/14697688.2021.1916067"},{"key":"e_1_3_3_2_12_2","doi-asserted-by":"publisher","DOI":"10.1109\/CVPR.2017.199"},{"key":"e_1_3_3_2_13_2","doi-asserted-by":"publisher","DOI":"10.1109\/CSCWD61410.2024.10580251"},{"key":"e_1_3_3_2_14_2","unstructured":"Deborah Miori and Mihai Cucuringu. 2022. Returns-Driven Macro Regimes and Characteristic Lead-Lag Behaviour between Asset Classes. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2209.00268 (2022)."},{"key":"e_1_3_3_2_15_2","doi-asserted-by":"crossref","unstructured":"Meike Nauta Doina Bucur and Christin Seifert. 2019. Causal discovery with attention-based convolutional neural networks. Machine Learning and Knowledge Extraction 1 1 (2019) 19.","DOI":"10.3390\/make1010019"},{"key":"e_1_3_3_2_16_2","doi-asserted-by":"publisher","unstructured":"Yuqi Nie Nam\u00a0H. Nguyen Phanwadee Sinthong and Jayant Kalagnanam. 2022. A Time Series is Worth 64 Words: Long-term Forecasting with Transformers. (2022). 10.48550\/ARXIV.2211.14730Publisher: arXiv Version Number: 2.","DOI":"10.48550\/ARXIV.2211.14730"},{"key":"e_1_3_3_2_17_2","unstructured":"Liliang Ren Yang Liu Yadong Lu Yelong Shen Chen Liang and Weizhu Chen. 2024. Samba: Simple Hybrid State Space Models for Efficient Unlimited Context Language Modeling. https:\/\/openreview.net\/forum?id=bIlnpVM4bc"},{"key":"e_1_3_3_2_18_2","doi-asserted-by":"crossref","unstructured":"Jakob Runge Peer Nowack Marlene Kretschmer Seth Flaxman and Dino Sejdinovic. 2019. Detecting and quantifying causal associations in large nonlinear time series datasets. Science advances 5 11 (2019) eaau4996.","DOI":"10.1126\/sciadv.aau4996"},{"key":"e_1_3_3_2_19_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-93417-438"},{"key":"e_1_3_3_2_20_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604237.3626879"},{"key":"e_1_3_3_2_21_2","doi-asserted-by":"publisher","unstructured":"Zhuangwei Shi. 2024. MambaStock: Selective state space model for stock prediction. 10.48550\/arXiv.2402.18959arXiv:https:\/\/arXiv.org\/abs\/2402.18959 [cs].","DOI":"10.48550\/arXiv.2402.18959"},{"key":"e_1_3_3_2_22_2","doi-asserted-by":"crossref","unstructured":"Konstantinos Tolikas. 2018. The lead-lag relation between the stock and the bond markets. The European Journal of Finance 24 10 (2018) 849\u2013866.","DOI":"10.1080\/1351847X.2017.1340320"},{"key":"e_1_3_3_2_23_2","unstructured":"Ilya Tolstikhin Neil Houlsby Alexander Kolesnikov Lucas Beyer Xiaohua Zhai Thomas Unterthiner Jessica Yung Andreas Steiner Daniel Keysers Jakob Uszkoreit Mario Lucic and Alexey Dosovitskiy. 2021. MLP-Mixer: An all-MLP Architecture for Vision. arXiv:https:\/\/arXiv.org\/abs\/2105.01601 [cs] (June 2021). http:\/\/arxiv.org\/abs\/2105.01601 arXiv:https:\/\/arXiv.org\/abs\/2105.01601."},{"key":"e_1_3_3_2_24_2","doi-asserted-by":"crossref","unstructured":"Saizhuo Wang Hao Kong Jiadong Guo Fengrui Hua Yiyan Qi Wanyun Zhou Jiahao Zheng Xinyu Wang Lionel\u00a0M Ni and Jian Guo. 2025. QuantBench: Benchmarking AI Methods for Quantitative Investment. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2504.18600 (2025).","DOI":"10.1631\/FITEE.2500280"},{"key":"e_1_3_3_2_25_2","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-12026-8_28"},{"key":"e_1_3_3_2_26_2","doi-asserted-by":"publisher","unstructured":"Haixu Wu Tengge Hu Yong Liu Hang Zhou Jianmin Wang and Mingsheng Long. 2023. TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysis. 10.48550\/arXiv.2210.02186arXiv:https:\/\/arXiv.org\/abs\/2210.02186 [cs].","DOI":"10.48550\/arXiv.2210.02186"},{"key":"e_1_3_3_2_27_2","doi-asserted-by":"publisher","DOI":"10.1145\/3511808.3557089"},{"key":"e_1_3_3_2_28_2","doi-asserted-by":"publisher","DOI":"10.1145\/3447548.3467297"},{"key":"e_1_3_3_2_29_2","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098117"},{"key":"e_1_3_3_2_30_2","doi-asserted-by":"publisher","DOI":"10.1145\/3604237.3626904"},{"key":"e_1_3_3_2_31_2","doi-asserted-by":"crossref","unstructured":"Yichi Zhang Mihai Cucuringu Alexander\u00a0Y Shestopaloff and Stefan Zohren. 2023. Robust Detection of Lead-Lag Relationships in Lagged Multi-Factor Models. arXiv preprint arXiv:https:\/\/arXiv.org\/abs\/2305.06704 (2023).","DOI":"10.2139\/ssrn.4445975"},{"key":"e_1_3_3_2_32_2","unstructured":"Yunhao Zhang and Junchi Yan. 2023. Crossformer: Transformer Utilizing Cross-Dimension Dependency for Multivariate Time Series Forecasting. https:\/\/openreview.net\/forum?id=vSVLM2j9eie"},{"key":"e_1_3_3_2_33_2","doi-asserted-by":"publisher","DOI":"10.24963\/ijcai.2023\/555"},{"key":"e_1_3_3_2_34_2","doi-asserted-by":"publisher","unstructured":"Jianyuan Zhong Zhijian Xu Saizhuo Wang Xiangyu Wen Jian Guo and Qiang Xu. 2024. DSPO: An End-to-End Framework for Direct Sorted Portfolio Construction. 10.48550\/arXiv.2405.15833arXiv:https:\/\/arXiv.org\/abs\/2405.15833 [q-fin].","DOI":"10.48550\/arXiv.2405.15833"},{"key":"e_1_3_3_2_35_2","doi-asserted-by":"publisher","DOI":"10.1609\/aaai.v36i4.20361"}],"event":{"name":"ICAIF '25: 6th ACM International Conference on AI in Finance","location":"Singapore Singapore","acronym":"ICAIF '25"},"container-title":["Proceedings of the 6th ACM International Conference on AI in Finance"],"original-title":[],"deposited":{"date-parts":[[2025,11,14]],"date-time":"2025-11-14T07:32:51Z","timestamp":1763105571000},"score":1,"resource":{"primary":{"URL":"https:\/\/dl.acm.org\/doi\/10.1145\/3768292.3770421"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,11,14]]},"references-count":34,"alternative-id":["10.1145\/3768292.3770421","10.1145\/3768292"],"URL":"https:\/\/doi.org\/10.1145\/3768292.3770421","relation":{},"subject":[],"published":{"date-parts":[[2025,11,14]]},"assertion":[{"value":"2025-11-14","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}