{"type":"ScholarlyArticle","title":"Trade When Opportunity Comes: Price Movement Forecasting via Locality-Aware Attention and Iterative Refinement Labeling","authors":[{"name":"Liang Zeng","slug":"liang-zeng","given":"Liang","family":"Zeng","affiliation":"Institute for Interdisciplinary Information Sciences (IIIS) , Tsinghua University , China"},{"name":"Lei Wang","slug":"lei-wang","given":"Lei","family":"Wang","affiliation":"Institute for Interdisciplinary Information Sciences (IIIS) , Tsinghua University , China"},{"name":"Hui Niu","slug":"hui-niu","given":"Hui","family":"Niu","affiliation":"Institute for Interdisciplinary Information Sciences (IIIS) , Tsinghua University , China"},{"name":"Ruchen Zhang","slug":"ruchen-zhang","given":"Ruchen","family":"Zhang","affiliation":"Huatai Securities Co. , Ltd , China"},{"name":"Ling Wang","slug":"ling-wang","given":"Ling","family":"Wang","affiliation":"Huatai Securities Co. , Ltd , China"},{"name":"Jian Li","slug":"jian-li","given":"Jian","family":"Li","affiliation":"Institute for Interdisciplinary Information Sciences (IIIS) , Tsinghua University , China"}],"abstract":"Price movement forecasting, aimed at predicting financial asset trends based on current market information, has achieved promising advancements through machine learning (ML) methods. Most existing ML methods, however, struggle with the extremely low signal-to-noise ratio and stochastic nature of financial data, often mistaking noises for real trading signals without careful selection of potentially profitable samples. To address this issue, we propose LARA, a novel price movement forecasting framework with two main components: Locality-Aware Attention (LA-Attention) and Iterative Refinement Labeling (RA-Labeling). (1) LA-Attention, enhanced by metric learning techniques, automatically extracts the potentially profitable samples through masked attention scheme and task-specific distance metrics. (2) RA-Labeling further iteratively refines the noisy labels of potentially profitable samples, and combines the learned predictors robust to the unseen and noisy samples. In a set of experiments on three real-world financial markets: stocks, cryptocurrencies, and ETFs, LARA significantly outperforms several machine learning based methods on the Qlib quantitative investment platform. Extensive ablation studies confirm LARA's superior ability in capturing more reliable trading opportunities.","doi":"10.24963/ijcai.2024/678","url":"https://ijcai.org/proceedings/2024/678","pdf":"https://ijcai.org/proceedings/2024/0678.pdf","markdown":"https://ijcai.org/proceedings/2024/678.md","bibtex":"https://ijcai.org/proceedings/2024/bibtex/678","volume":{"slug":"2024","title":"Proceedings of the Thirty-Third International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2024","url":"https://ijcai.org/proceedings/2024/","year":2024,"location":"Jeju, Korea","dates":"3-9 August 2024","editors":"Kate Larson","isbn":"978-1-956792-04-1","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2024"},"section":"Multidisciplinary Topics and Applications","track":"Main Track","number":678,"pages":{"first":6134,"last":6142},"keywords":["Multidisciplinary Topics and Applications: MTA: Finance","Machine Learning: ML: Applications"],"videos":[],"references":[{"position":1,"raw":"[Abner, 2010] David J Abner. 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