{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:54:19Z","timestamp":1781740459254,"version":"3.54.5"},"reference-count":37,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62506239"],"award-info":[{"award-number":["62506239"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62506048"],"award-info":[{"award-number":["62506048"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62366034"],"award-info":[{"award-number":["62366034"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,8]]},"DOI":"10.1016\/j.eswa.2026.132411","type":"journal-article","created":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T05:03:22Z","timestamp":1776143002000},"page":"132411","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["NEXUS: A multi-modal framework for capturing financial news interactions in market forecasting"],"prefix":"10.1016","volume":"323","author":[{"ORCID":"https:\/\/orcid.org\/0009-0002-7540-247X","authenticated-orcid":false,"given":"Guangyang","family":"Tian","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3736-4513","authenticated-orcid":false,"given":"Wuzhida","family":"Bao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuting","family":"Cao","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yin","family":"Yang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5048-0319","authenticated-orcid":false,"given":"Shiping","family":"Wen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132411_bib0001","doi-asserted-by":"crossref","first-page":"30235","DOI":"10.1109\/ACCESS.2025.3535782","article-title":"Time-series large language models: A systematic review of state-of-the-art","volume":"13","author":"Abdullahi","year":"2025","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132411_bib0002","unstructured":"Chaudhary, R. (2025). Advanced stock market prediction using long short-term memory networks: A comprehensive deep learning framework. arXiv: 2505.05325."},{"key":"10.1016\/j.eswa.2026.132411_bib0003","doi-asserted-by":"crossref","unstructured":"Chen, J., Xiao, S., Zhang, P., Luo, K., Lian, D., & Liu, Z. (2024). BGE M3-embedding: Multi-lingual, multi-functionality, multi-granularity text embeddings through self-knowledge distillation. arXiv: 2402.03216.","DOI":"10.18653\/v1\/2024.findings-acl.137"},{"issue":"9","key":"10.1016\/j.eswa.2026.132411_bib0004","doi-asserted-by":"crossref","first-page":"1301","DOI":"10.1080\/14697688.2024.2318220","article-title":"GPT\u2019s idea of stock factors","volume":"24","author":"Cheng","year":"2024","journal-title":"Quantitative Finance"},{"issue":"3","key":"10.1016\/j.eswa.2026.132411_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e25394","article-title":"Unsupervised novelty detection for time series using a deep learning approach","volume":"10","author":"Hossen","year":"2024","journal-title":"Heliyon"},{"key":"10.1016\/j.eswa.2026.132411_bib0006","series-title":"2021 3rd International conference on machine learning, big data and business intelligence (MLBDBI)","first-page":"60","article-title":"Stock price prediction based on temporal fusion transformer","author":"Hu","year":"2021"},{"key":"10.1016\/j.eswa.2026.132411_bib0007","series-title":"2024 4th International symposium on computer technology and information science (ISCTIS)","first-page":"796","article-title":"A finBERT framework for sentiment analysis of chinese financial news","author":"Huang","year":"2024"},{"key":"10.1016\/j.eswa.2026.132411_bib0008","unstructured":"Jabbar, A., & Jalil, S.Q. (2024). A comprehensive analysis of machine learning models for algorithmic trading of bitcoin. arXiv: 2407.18334."},{"key":"10.1016\/j.eswa.2026.132411_bib0009","unstructured":"Jeong, Y., Johari, R., Rothenh\u00e4usler, D., & Fox, E. (2025). Optimal empirical risk minimisation under temporal distribution shifts. arXiv: 2507.13287."},{"key":"10.1016\/j.eswa.2026.132411_bib0010","doi-asserted-by":"crossref","first-page":"424","DOI":"10.1016\/j.pacfin.2016.05.011","article-title":"Melancholia and Japanese stock returns\u20132003 to 2012","volume":"40","author":"Khuu","year":"2016","journal-title":"Pacific-Basin Finance Journal"},{"key":"10.1016\/j.eswa.2026.132411_bib0011","doi-asserted-by":"crossref","first-page":"114180","DOI":"10.52202\/079017-3627","article-title":"Are self-attentions effective for time series forecasting?","volume":"37","author":"Kim","year":"2024","journal-title":"Advances in Neural Information Processing Systems"},{"issue":"12","key":"10.1016\/j.eswa.2026.132411_bib0012","doi-asserted-by":"crossref","first-page":"1920","DOI":"10.3390\/math12121920","article-title":"Back to basics: The power of the multilayer perceptron in financial time series forecasting","volume":"12","author":"Lazcano","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.eswa.2026.132411_bib0013","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"5187","article-title":"PEN: Prediction-explanation network to forecast stock price movement with better explainability","volume":"Vol. 37","author":"Li","year":"2023"},{"key":"10.1016\/j.eswa.2026.132411_bib0014","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"162","article-title":"Master: Market-guided stock transformer for stock price forecasting","volume":"Vol. 38","author":"Li","year":"2024"},{"key":"10.1016\/j.eswa.2026.132411_bib0015","series-title":"Proceedings of the 2024 joint international conference on computational linguistics, language resources and evaluation","first-page":"773","article-title":"AlphaFin: Benchmarking financial analysis with retrieval-augmented stock-chain framework","author":"Li","year":"2024"},{"key":"10.1016\/j.eswa.2026.132411_bib0016","unstructured":"Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv: 1907.11692."},{"key":"10.1016\/j.eswa.2026.132411_bib0017","series-title":"Proceedings of the twenty-ninth international conference on international joint conferences on artificial intelligence","first-page":"4513","article-title":"FinBERT: A pre-trained financial language representation model for financial text mining","author":"Liu","year":"2021"},{"issue":"12","key":"10.1016\/j.eswa.2026.132411_bib0018","doi-asserted-by":"crossref","first-page":"876","DOI":"10.14569\/IJACSA.2021.01212106","article-title":"Predicting stock closing prices in emerging markets with transformer neural networks: The saudi stock exchange case","volume":"12","author":"Malibari","year":"2021","journal-title":"International Journal of Advanced Computer Science and Applications"},{"key":"10.1016\/j.eswa.2026.132411_bib0019","doi-asserted-by":"crossref","first-page":"1991","DOI":"10.1109\/ACCESS.2018.2886457","article-title":"DeepAnT: A deep learning approach for unsupervised anomaly detection in time series","volume":"7","author":"Munir","year":"2019","journal-title":"IEEE Access"},{"key":"10.1016\/j.eswa.2026.132411_bib0020","unstructured":"Oad, V., Pathak, P., Innan, N., Shafique, M. et al. (2025). KASPER: Kolmogorov arnold networks for stock prediction and explainable regimes. arXiv: 2507.18983."},{"issue":"12","key":"10.1016\/j.eswa.2026.132411_bib0021","doi-asserted-by":"crossref","first-page":"207","DOI":"10.14569\/IJACSA.2024.0151223","article-title":"A deep learning-based LSTM for stock price prediction using twitter sentiment analysis","volume":"15","author":"Ouf","year":"2024","journal-title":"International Journal of Advanced Computer Science and Applications"},{"issue":"5","key":"10.1016\/j.eswa.2026.132411_bib0022","doi-asserted-by":"crossref","first-page":"725","DOI":"10.1515\/snde-2022-0025","article-title":"Should you use GARCH models for forecasting volatility? A comparison to GRU neural networks","volume":"28","author":"Pallotta","year":"2024","journal-title":"Studies in Nonlinear Dynamics & Econometrics"},{"key":"10.1016\/j.eswa.2026.132411_bib0023","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2020.106181","article-title":"Financial time series forecasting with deep learning: A systematic literature review: 2005\u20132019","volume":"90","author":"Sezer","year":"2020","journal-title":"Applied soft computing"},{"issue":"1","key":"10.1016\/j.eswa.2026.132411_bib0024","doi-asserted-by":"crossref","first-page":"17","DOI":"10.23919\/AISE.2025.000002","article-title":"Neuromorphic computing in the era of large models","volume":"1","author":"Shan","year":"2025","journal-title":"Artificial Intelligence Science and Engineering"},{"issue":"11","key":"10.1016\/j.eswa.2026.132411_bib0025","first-page":"55","article-title":"Stock price prediction using LSTM, ARIMA and UCM","volume":"9","author":"Sharma","year":"2022","journal-title":"Center for Development Economic Studies"},{"key":"10.1016\/j.eswa.2026.132411_bib0026","unstructured":"Shi, Z., Hu, Y., Mo, G., & Wu, J. (2022). Attention-based CNN-LSTM and XGBoost hybrid model for stock prediction. arXiv: 2204.02623."},{"key":"10.1016\/j.eswa.2026.132411_bib0027","doi-asserted-by":"crossref","first-page":"103694","DOI":"10.1109\/ACCESS.2022.3210182","article-title":"Sentiment analysis with ensemble hybrid deep learning model","volume":"10","author":"Tan","year":"2022","journal-title":"IEEE Access"},{"issue":"4","key":"10.1016\/j.eswa.2026.132411_bib0028","doi-asserted-by":"crossref","first-page":"255","DOI":"10.23919\/AISE.2025.000018","article-title":"Time-series stock price forecasting based on neural networks: A comprehensive survey","volume":"1","author":"Tian","year":"2025","journal-title":"Artificial Intelligence Science and Engineering"},{"key":"10.1016\/j.eswa.2026.132411_bib0029","unstructured":"Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., Bhosale, S. et al. (2023). Llama 2: Open foundation and fine-tuned chat models. arXiv: 2307.09288."},{"issue":"4","key":"10.1016\/j.eswa.2026.132411_bib0030","doi-asserted-by":"crossref","first-page":"295","DOI":"10.23919\/AISE.2025.000020","article-title":"Exploring the interpretability of forecasting models for energy balancing market","volume":"1","author":"V\u00e5le","year":"2025","journal-title":"Artificial Intelligence Science and Engineering"},{"key":"10.1016\/j.eswa.2026.132411_bib0031","series-title":"Advances in neural information processing systems","first-page":"5998","article-title":"Attention is all you need","author":"Vaswani","year":"2017"},{"issue":"2","key":"10.1016\/j.eswa.2026.132411_bib0032","doi-asserted-by":"crossref","first-page":"175","DOI":"10.1080\/14697688.2023.2299466","article-title":"Fin-GAN: Forecasting and classifying financial time series via generative adversarial networks","volume":"24","author":"Vuleti\u0107","year":"2024","journal-title":"Quantitative Finance"},{"key":"10.1016\/j.eswa.2026.132411_bib0033","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.118128","article-title":"Stock market index prediction using deep transformer model","volume":"208","author":"Wang","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132411_bib0034","series-title":"Proceedings of the 33rd ACM international conference on information and knowledge management","first-page":"2379","article-title":"MANA-Net: Mitigating aggregated sentiment homogenization with news weighting for enhanced market prediction","author":"Wang","year":"2024"},{"issue":"1","key":"10.1016\/j.eswa.2026.132411_bib0035","article-title":"Deep learning models for price forecasting of financial time series: A review of recent advancements: 2020\u20132022","volume":"14","author":"Zhang","year":"2024","journal-title":"Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery"},{"key":"10.1016\/j.eswa.2026.132411_bib0036","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.117239","article-title":"Transformer-based attention network for stock movement prediction","volume":"202","author":"Zhang","year":"2022","journal-title":"Expert Systems with Applications"},{"issue":"2","key":"10.1016\/j.eswa.2026.132411_bib0037","doi-asserted-by":"crossref","first-page":"145","DOI":"10.38177\/AJBSR.2024.6211","article-title":"Predicting stock price by using attention-based hybrid LSTM model","volume":"6","author":"Zhou","year":"2024","journal-title":"Asian Journal of Basic Science & Research"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426013242?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426013242?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,6,17]],"date-time":"2026-06-17T23:29:08Z","timestamp":1781738948000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426013242"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,8]]},"references-count":37,"alternative-id":["S0957417426013242"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132411","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,8]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"NEXUS: A multi-modal framework for capturing financial news interactions in market forecasting","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132411","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"132411"}}