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However, accurately detecting these evolving temporal dependencies remains a complex challenge. Traditional approaches predominantly rely on statistical methods based on price evidence, while machine learning and deep learning techniques remain largely unexplored in this context. The lead-lag relationships and effects can be naturally represented using a dynamic graph structure, although this direction is still uninvestigated in the literature. Indeed, existing studies rarely leverage graph-based representations, and when they do, they typically consider static rather than dynamic structures, limiting their ability to capture temporal evolution. To overcome these limitations, this study proposes a novel framework that: (i) formulates lead-lag relationships and effects detection as a temporal link prediction task on dynamic graphs; (ii) introduces a novel real-world benchmark task for the evaluation and comparison of Temporal Graph Neural Networks (TGNNs); (iii) adapts, extends, and defines nine deep learning models ranging from simple LSTMs to State-of-the-Art TGNNs; (iv) explicitly evaluates two scenarios: lead-lag relationships that are both positive and negative, as well as those that are only positive; (v) performs an ablation study to assess the impact of the key components of the considered approaches. The experiments were conducted on a custom-gathered dataset of financial assets enriched with temporal, structural, and sentiment features. The findings demonstrate that temporal graph learning effectively models complex lead-lag relationships, opening new avenues for data-driven financial market analysis.<\/jats:p>","DOI":"10.1007\/s10994-026-07113-y","type":"journal-article","created":{"date-parts":[[2026,7,8]],"date-time":"2026-07-08T11:15:59Z","timestamp":1783509359000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Temporal Graph Learning Framework for Lead-Lag Detection in Financial Markets"],"prefix":"10.1007","volume":"115","author":[{"given":"Ivan","family":"Krstev","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Davide","family":"Rigoni","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Igor","family":"Mishkovski","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luca","family":"Pasa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2026,7,8]]},"reference":[{"key":"7113_CR1","doi-asserted-by":"publisher","DOI":"10.1016\/j.physa.2019.122986","volume":"539","author":"L Basnarkov","year":"2020","unstructured":"Basnarkov, L., Stojkoski, V., Utkovski, Z., & Kocarev, L. 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