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Technol."],"published-print":{"date-parts":[[2024,4,30]]},"abstract":"<jats:p>Many deep learning works on financial time-series forecasting focus on predicting future prices\/returns of individual assets with numerical price-related information for trading, and hence propose models designed for univariate, single-task, and\/or unimodal settings. Forecasting for investment and risk management involves multiple tasks in multivariate settings: forecasts of expected returns and risks of assets in portfolios, and correlations between these assets. As different sources\/types of time-series influence future returns, risks, and correlations of assets in different ways, it is also important to capture time-series from different modalities. Hence, this article addresses financial time-series forecasting for investment and risk management in a multivariate, multitask, and multimodal setting. Financial time-series forecasting, however, is challenging due to the low signal-to-noise ratios typical in financial time-series, and as intra-series and inter-series relationships of assets evolve across time. To address these challenges, our proposed Temporal Implicit Multimodal Network (TIME) model learns implicit inter-series relationship networks between assets from multimodal financial time-series at multiple time-steps adaptively. TIME then uses dynamic network and temporal encoding modules to jointly capture such evolving relationships, multimodal financial time-series, and temporal representations. Our experiments show that TIME outperforms other state-of-the-art models on multiple forecasting tasks and investment and risk management applications.<\/jats:p>","DOI":"10.1145\/3643855","type":"journal-article","created":{"date-parts":[[2024,2,1]],"date-time":"2024-02-01T11:56:39Z","timestamp":1706788599000},"page":"1-25","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":10,"title":["Temporal Implicit Multimodal Networks for Investment and Risk Management"],"prefix":"10.1145","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5922-8956","authenticated-orcid":false,"given":"Gary","family":"Ang","sequence":"first","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0065-8665","authenticated-orcid":false,"given":"Ee-Peng","family":"Lim","sequence":"additional","affiliation":[{"name":"Singapore Management University, Singapore, Singapore"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,3,28]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"publisher","DOI":"10.1109\/MIS.2022.3165994"},{"key":"e_1_3_2_3_2","first-page":"172","volume-title":"Conference on Learning Theory","author":"Anava Oren","year":"2013","unstructured":"Oren Anava, Elad Hazan, Shie Mannor, and Ohad Shamir. 2013. 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