{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T19:10:49Z","timestamp":1772910649395,"version":"3.50.1"},"reference-count":68,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T00:00:00Z","timestamp":1753142400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61972358"],"award-info":[{"award-number":["61972358"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Complex Intell. Syst."],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s40747-025-02023-3","type":"journal-article","created":{"date-parts":[[2025,7,22]],"date-time":"2025-07-22T05:55:27Z","timestamp":1753163727000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Stock movement prediction with multimodal stable fusion via gated cross-attention mechanism"],"prefix":"10.1007","volume":"11","author":[{"given":"Chang","family":"Zong","sequence":"first","affiliation":[]},{"given":"Jian","family":"Wan","sequence":"additional","affiliation":[]},{"given":"Lucia","family":"Cascone","sequence":"additional","affiliation":[]},{"given":"Hang","family":"Zhou","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,7,22]]},"reference":[{"key":"2023_CR1","doi-asserted-by":"publisher","unstructured":"Feng F, He X, Wang X, Luo C, Liu Y, Chua T (2019) Temporal Relational Ranking for Stock Prediction. ACM Trans. Inf. Syst.. 37 (3), https:\/\/doi.org\/10.1145\/3309547","DOI":"10.1145\/3309547"},{"key":"2023_CR2","doi-asserted-by":"publisher","unstructured":"Nelson D, Pereira A, Oliveira R (2017) Stock market\u2019s price movement prediction with LSTM neural networks. 2017 International Joint Conference On Neural Networks (IJCNN). pp. 1419-1426 https:\/\/doi.org\/10.1109\/IJCNN.2017.7966019","DOI":"10.1109\/IJCNN.2017.7966019"},{"key":"2023_CR3","doi-asserted-by":"publisher","first-page":"163","DOI":"10.1016\/j.knosys.2018.10.034","volume":"164","author":"W Long","year":"2019","unstructured":"Long W, Lu Z, Cui L (2019) Deep learning-based feature engineering for stock price movement prediction. Knowl-Based Syst 164:163\u2013173. https:\/\/doi.org\/10.1016\/j.knosys.2018.10.034","journal-title":"Knowl-Based Syst"},{"key":"2023_CR4","doi-asserted-by":"publisher","first-page":"9603","DOI":"10.1016\/j.eswa.2015.07.052","volume":"42","author":"T Nguyen","year":"2015","unstructured":"Nguyen T, Shirai K, Velcin J (2015) Sentiment analysis on social media for stock movement prediction. Expert Syst Appl 42:9603\u20139611. https:\/\/doi.org\/10.1016\/j.eswa.2015.07.052","journal-title":"Expert Syst Appl"},{"key":"2023_CR5","unstructured":"Liu J, Lin H, Liu X, Xu B, Ren Y, Diao Y, Yang L (2019) Transformer-based capsule network for stock movement prediction. Proceedings of the First Workshop On Financial Technology And Natural Language Processing. pp. 66-73 https:\/\/aclanthology.org\/W19-5511\/"},{"key":"2023_CR6","doi-asserted-by":"publisher","DOI":"10.1145\/3451397","author":"J Gao","year":"2021","unstructured":"Gao J, Ying X, Xu C, Wang J, Zhang S, Li Z (2021) Graph-based stock recommendation by time-aware relational attention network. ACM Trans Knowl Discov Data. https:\/\/doi.org\/10.1145\/3451397","journal-title":"ACM Trans Knowl Discov Data"},{"key":"2023_CR7","doi-asserted-by":"publisher","unstructured":"Xu Y, Cohen S (2018) Stock Movement Prediction from Tweets and Historical Prices. Proceedings Of The 56th Annual Meeting Of The Association For Computational Linguistics (Volume 1: Long Papers). pp. 1970-1979 https:\/\/doi.org\/10.18653\/v1\/P18-1183","DOI":"10.18653\/v1\/P18-1183"},{"key":"2023_CR8","doi-asserted-by":"publisher","first-page":"515","DOI":"10.1016\/j.inffus.2022.10.025","volume":"91","author":"Y Ma","year":"2023","unstructured":"Ma Y, Mao R, Lin Q, Wu P, Cambria E (2023) Multi-source aggregated classification for stock price movement prediction. Inf Fusion 91:515\u2013528. https:\/\/doi.org\/10.1016\/j.inffus.2022.10.025","journal-title":"Inf Fusion"},{"key":"2023_CR9","doi-asserted-by":"publisher","unstructured":"Sawhney R, Agarwal S, Wadhwa A, Shah R (2020) Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations. Proceedings Of The 2020 Conference On Empirical Methods In Natural Language Processing (EMNLP). pp. 8415-8426 (11), https:\/\/doi.org\/10.18653\/v1\/2020.emnlp-main.676","DOI":"10.18653\/v1\/2020.emnlp-main.676"},{"key":"2023_CR10","doi-asserted-by":"publisher","unstructured":"Li W, Bao R, Harimoto K, Chen D, Xu J, Su Q (2021) Modeling the stock relation with graph network for overnight stock movement prediction. Proceedings Of The Twenty-Ninth International Joint Conference On Artificial Intelligence. https:\/\/doi.org\/10.5555\/3491440.3492066","DOI":"10.5555\/3491440.3492066"},{"key":"2023_CR11","doi-asserted-by":"publisher","unstructured":"Elahi A, Taghvaei F (2024) Combining Financial Data and News Articles for Stock Price Movement Prediction Using Large Language Models. 2024 IEEE International Conference On Big Data (BigData). pp. 4875-4883 https:\/\/doi.org\/10.1109\/BigData62323.2024.10825449","DOI":"10.1109\/BigData62323.2024.10825449"},{"key":"2023_CR12","doi-asserted-by":"publisher","unstructured":"Bhat R, Jain B (2024) Stock Price Trend Prediction using Emotion Analysis of Financial Headlines with Distilled LLM Model. Proceedings Of The 17th International Conference On PErvasive Technologies Related To Assistive Environments. pp. 67-73 https:\/\/doi.org\/10.1145\/3652037.3652076","DOI":"10.1145\/3652037.3652076"},{"key":"2023_CR13","doi-asserted-by":"publisher","unstructured":"Xie Q, Han W, Lai Y, Peng M, Huang J (2023) The Wall Street Neophyte: A Zero-Shot Analysis of ChatGPT Over MultiModal Stock Movement Prediction Challenges. https:\/\/doi.org\/10.48550\/arXiv.2304.05351","DOI":"10.48550\/arXiv.2304.05351"},{"key":"2023_CR14","doi-asserted-by":"publisher","unstructured":"Kaeley H, Qiao Y, Bagherzadeh N (2023) Support for Stock Trend Prediction Using Transformers and Sentiment Analysis. https:\/\/doi.org\/10.48550\/arXiv.2305.14368","DOI":"10.48550\/arXiv.2305.14368"},{"key":"2023_CR15","doi-asserted-by":"publisher","unstructured":"Soun Y, Yoo J, Cho M, Jeon J, Kang U (2022) Accurate Stock Movement Prediction with Self-supervised Learning from Sparse Noisy Tweets. 2022 IEEE International Conference On Big Data (Big Data). pp. 1691-1700 https:\/\/doi.org\/10.1109\/BigData55660.2022.10020720","DOI":"10.1109\/BigData55660.2022.10020720"},{"key":"2023_CR16","doi-asserted-by":"publisher","unstructured":"Zhao Z, Rao R, Tu S, Shi J (2017) Time-Weighted LSTM Model with Redefined Labeling for Stock Trend Prediction. 2017 IEEE 29th International Conference On Tools With Artificial Intelligence (ICTAI). pp. 1210-1217 https:\/\/doi.org\/10.1109\/ICTAI.2017.00184","DOI":"10.1109\/ICTAI.2017.00184"},{"key":"2023_CR17","doi-asserted-by":"publisher","first-page":"60","DOI":"10.1016\/j.eswa.2017.12.026","volume":"97","author":"J Zhang","year":"2018","unstructured":"Zhang J, Cui S, Xu Y, Li Q, Li T (2018) A novel data-driven stock price trend prediction system. Expert Syst Appl 97:60\u201369. https:\/\/doi.org\/10.1016\/j.eswa.2017.12.026","journal-title":"Expert Syst Appl"},{"key":"2023_CR18","doi-asserted-by":"publisher","unstructured":"Zou J, Cao H, Liu L, Lin Y, Abbasnejad E, Shi J (2022) Astock: A New Dataset and Automated Stock Trading based on Stock-specific News Analyzing Model. Proceedings Of The Fourth Workshop On Financial Technology And Natural Language Processing (FinNLP). pp. 178-186 (12), https:\/\/doi.org\/10.18653\/v1\/2022.finnlp-1.24","DOI":"10.18653\/v1\/2022.finnlp-1.24"},{"key":"2023_CR19","doi-asserted-by":"publisher","unstructured":"Chen Q, Robert C (2022) Multivariate Realized Volatility Forecasting with Graph Neural Network. Proceedings Of The Third ACM International Conference On AI In Finance. pp. 156-164 (10), https:\/\/doi.org\/10.1145\/3533271.3561663","DOI":"10.1145\/3533271.3561663"},{"key":"2023_CR20","doi-asserted-by":"publisher","DOI":"10.3390\/s23052381","author":"M Paw\u0142owski","year":"2023","unstructured":"Paw\u0142owski M, Wr\u00f3blewska A, Sysko-Roma\u0144czuk S (2023) Effective techniques for multimodal data fusion: a comparative analysis. Sensors. https:\/\/doi.org\/10.3390\/s23052381","journal-title":"Sensors"},{"key":"2023_CR21","doi-asserted-by":"publisher","unstructured":"Fukui A, Park D, Yang D, Rohrbach A, Darrell T, Rohrbach M (2026) Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding. Proceedings Of The 2016 Conference On Empirical Methods In Natural Language Processing. pp. 457-468 (11), https:\/\/doi.org\/10.18653\/v1\/D16-1044","DOI":"10.18653\/v1\/D16-1044"},{"key":"2023_CR22","doi-asserted-by":"publisher","unstructured":"Wirojwatanakul P, Wangperawong A (2019) Multi-label product categorization using multi-modal fusion models. https:\/\/doi.org\/10.48550\/arXiv.1907.00420","DOI":"10.48550\/arXiv.1907.00420"},{"key":"2023_CR23","doi-asserted-by":"publisher","unstructured":"Singh A, Natarajan V, Shah M, Jiang Y, Chen X, Batra D, Parikh D, Rohrbach M (2019) Towards VQA Models That Can Read. 2019 IEEE\/CVF Conference On Computer Vision And Pattern Recognition (CVPR). pp. 8309-8318 https:\/\/doi.org\/10.1109\/CVPR.2019.00851","DOI":"10.1109\/CVPR.2019.00851"},{"key":"2023_CR24","doi-asserted-by":"publisher","unstructured":"Zhang Q, Fu J, Liu X, Huang X (2018) Adaptive co-attention network for named entity recognition in tweets. Proceedings Of The Thirty-Second AAAI Conference On Artificial Intelligence And Thirtieth Innovative Applications Of Artificial Intelligence Conference And Eighth AAAI Symposium On Educational Advances In Artificial Intelligence. https:\/\/doi.org\/10.1609\/aaai.v32i1.11962","DOI":"10.1609\/aaai.v32i1.11962"},{"key":"2023_CR25","doi-asserted-by":"publisher","DOI":"10.3390\/s22031045","author":"F Yan","year":"2022","unstructured":"Yan F, Silamu W, Li Y (2022) Deep modular bilinear attention network for visual question answering. Sensors. https:\/\/doi.org\/10.3390\/s22031045","journal-title":"Sensors"},{"key":"2023_CR26","doi-asserted-by":"publisher","unstructured":"Huynh T, Nguyen M, Nguyen T, Nguyen P, Weidlich M, Nguyen Q, Aberer K (2023) Efficient Integration of Multi-Order Dynamics and Internal Dynamics in Stock Movement Prediction. Proceedings Of The Sixteenth ACM International Conference On Web Search And Data Mining. pp. 850-858 https:\/\/doi.org\/10.1145\/3539597.3570427","DOI":"10.1145\/3539597.3570427"},{"key":"2023_CR27","doi-asserted-by":"publisher","unstructured":"Daiya D, Lin C (2021) Stock Movement Prediction and Portfolio Management via Multimodal Learning with Transformer. ICASSP 2021 - 2021 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP). pp. 3305-3309 https:\/\/doi.org\/10.1109\/ICASSP39728.2021.9414893","DOI":"10.1109\/ICASSP39728.2021.9414893"},{"key":"2023_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.knosys.2023.110262","volume":"262","author":"J Wang","year":"2023","unstructured":"Wang J, Hu Y, Jiang T, Tan J, Li Q (2023) Essential tensor learning for multimodal information-driven stock movement prediction. Knowl-Based Syst 262:110262. https:\/\/doi.org\/10.1016\/j.knosys.2023.110262","journal-title":"Knowl-Based Syst"},{"key":"2023_CR29","unstructured":"Fataliyev K, Liu W (2023) MCASP: Multi-Modal Cross Attention Network for Stock Market Prediction. Proceedings Of The 21st Annual Workshop Of The Australasian Language Technology Association. pp. 67-77 (11), https:\/\/aclanthology.org\/2023.alta-1.7\/"},{"key":"2023_CR30","doi-asserted-by":"publisher","first-page":"10334","DOI":"10.1609\/aaai.v34i06.6597","volume":"34","author":"T Zhi-Xuan","year":"2020","unstructured":"Zhi-Xuan T, Soh H, Ong D (2020) Factorized inference in deep Markov models for incomplete multimodal time series. Proc AAAI Conf Artif Intel 34:10334\u201310341. https:\/\/doi.org\/10.1609\/aaai.v34i06.6597","journal-title":"Proc AAAI Conf Artif Intel"},{"key":"2023_CR31","doi-asserted-by":"publisher","unstructured":"Chen J, Zhang A (2020) HGMF: Heterogeneous Graph-based Fusion for Multimodal Data with Incompleteness. Proceedings Of The 26th ACM SIGKDD International Conference On Knowledge Discovery & Data Mining. pp. 1295-1305 https:\/\/doi.org\/10.1145\/3394486.3403182","DOI":"10.1145\/3394486.3403182"},{"key":"2023_CR32","doi-asserted-by":"crossref","unstructured":"Ma M, Ren J, Zhao L, Testuggine D, Peng X Are Multimodal Transformers Robust to Missing Modality?. Proceedings Of The IEEE\/CVF Conference On Computer Vision And Pattern Recognition (CVPR). pp. 18177-18186 (2022,6), https:\/\/doi.org\/10.48550\/arXiv.2204.05454","DOI":"10.1109\/CVPR52688.2022.01764"},{"issue":"5","key":"2023_CR33","doi-asserted-by":"publisher","first-page":"6644","DOI":"10.1609\/aaai.v35i8.16822","volume":"35","author":"E Amrani","year":"2021","unstructured":"Amrani E, Ben-Ari R, Rotman D, Bronstein A (2021) Noise estimation using density estimation for self-supervised multimodal learning. Proc AAAI Conf Artif Intell 35(5):6644\u20136652. https:\/\/doi.org\/10.1609\/aaai.v35i8.16822","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"2023_CR34","doi-asserted-by":"publisher","first-page":"3018","DOI":"10.1109\/TMM.2023.3306489","volume":"26","author":"S Mai","year":"2024","unstructured":"Mai S, Sun Y, Xiong A, Zeng Y, Hu H (2024) Multimodal boosting: addressing noisy modalities and identifying modality contribution. IEEE Trans Multimed 26:3018\u20133033. https:\/\/doi.org\/10.1109\/TMM.2023.3306489","journal-title":"IEEE Trans Multimed"},{"key":"2023_CR35","doi-asserted-by":"publisher","unstructured":"Yoo J, Soun Y, Park Y, Kang U (2021) Accurate Multivariate Stock Movement Prediction via Data-Axis Transformer with Multi-Level Contexts. Proceedings Of The 27th ACM SIGKDD Conference On Knowledge Discovery & Data Mining. pp. 2037-2045 , https:\/\/doi.org\/10.1145\/3447548.3467297","DOI":"10.1145\/3447548.3467297"},{"key":"2023_CR36","doi-asserted-by":"publisher","unstructured":"Feng F, Chen H, He X, Ding J, Sun M, Chua T (2019) Enhancing Stock Movement Prediction with Adversarial Training. Proceedings Of The Twenty-Eighth International Joint Conference On Artificial Intelligence, IJCAI-19. pp. 5843-5849 (7), https:\/\/doi.org\/10.24963\/ijcai.2019\/810","DOI":"10.24963\/ijcai.2019\/810"},{"key":"2023_CR37","doi-asserted-by":"publisher","first-page":"469","DOI":"10.1109\/TKDE.2021.3079496","volume":"35","author":"Y Hsu","year":"2023","unstructured":"Hsu Y, Tsai Y, Li C (2023) FinGAT: financial graph attention networks for recommending top-KK profitable stocks. IEEE Trans Knowl Data Eng 35:469\u2013481. https:\/\/doi.org\/10.1109\/TKDE.2021.3079496","journal-title":"IEEE Trans Knowl Data Eng"},{"key":"2023_CR38","doi-asserted-by":"publisher","unstructured":"Sagala T, Saputri M, Mahendra R, Budi I (2020) Stock Price Movement Prediction Using Technical Analysis and Sentiment Analysis. Proceedings Of The 2020 2nd Asia Pacific Information Technology Conference. pp. 123-127 https:\/\/doi.org\/10.1145\/3379310.3381045","DOI":"10.1145\/3379310.3381045"},{"issue":"9","key":"2023_CR39","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.inffus.2017.02.003","volume":"37","author":"S Poria","year":"2017","unstructured":"Poria S, Cambria E, Bajpai R, Hussain A (2017) A review of affective computing. Inf Fusion 37(9):98\u2013125. https:\/\/doi.org\/10.1016\/j.inffus.2017.02.003","journal-title":"Inf Fusion"},{"key":"2023_CR40","doi-asserted-by":"publisher","unstructured":"D\u2019mello S, Kory J, (2015) A review and meta-analysis of multimodal affect detection systems. ACM Comput Surv. https:\/\/doi.org\/10.1145\/2682899","DOI":"10.1145\/2682899"},{"key":"2023_CR41","doi-asserted-by":"publisher","unstructured":"Fukui A, Park D, Yang D, Rohrbach A, Darrell T, Rohrbach M (2016)Multimodal Compact Bilinear Pooling for Visual Question Answering and Visual Grounding. Proceedings Of The 2016 Conference On Empirical Methods In Natural Language Processing. pp. 457-468 (11), https:\/\/doi.org\/10.18653\/v1\/D16-1044","DOI":"10.18653\/v1\/D16-1044"},{"key":"2023_CR42","doi-asserted-by":"publisher","unstructured":"Lu J, Xiong C, Parikh D, Socher R (2017) Knowing When to Look: Adaptive Attention via a Visual Sentinel for Image Captioning. 2017 IEEE Conference On Computer Vision And Pattern Recognition (CVPR). pp. 3242-3250 https:\/\/doi.org\/10.1109\/CVPR.2017.345","DOI":"10.1109\/CVPR.2017.345"},{"key":"2023_CR43","doi-asserted-by":"publisher","first-page":"5947","DOI":"10.1109\/TNNLS.2018.2817340","volume":"29","author":"Z Yu","year":"2018","unstructured":"Yu Z, Yu J, Xiang C, Fan J, Tao D (2018) Beyond bilinear: generalized multimodal factorized high-order pooling for visual question answering. IEEE Trans Neural Netw Learn Syst 29:5947\u20135959. https:\/\/doi.org\/10.1109\/TNNLS.2018.2817340","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"2023_CR44","doi-asserted-by":"publisher","first-page":"8658","DOI":"10.1609\/aaai.v33i01.33018658","volume":"33","author":"X Li","year":"2019","unstructured":"Li X, Song J, Gao L, Liu X, Huang W, He X, Gan C (2019) Beyond RNNs: positional self-attention with co-attention for video question answering. Proceed AAAI Conf Artif Intell 33:8658\u20138665. https:\/\/doi.org\/10.1609\/aaai.v33i01.33018658","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"2023_CR45","doi-asserted-by":"publisher","unstructured":"Zheng S, Wang W, Qu J, Yin H, Chen W, Zhao L (2023) MMKGR: Multi-hop Multi-modal Knowledge Graph Reasoning. 2023 IEEE 39th International Conference On Data Engineering (ICDE). pp. 96-109 https:\/\/doi.org\/10.1109\/ICDE55515.2023.00015","DOI":"10.1109\/ICDE55515.2023.00015"},{"key":"2023_CR46","doi-asserted-by":"publisher","unstructured":"Rajan V, Brutti A, Cavallaro A (2022) Is Cross-Attention Preferable to Self-Attention for Multi-Modal Emotion Recognition?. ICASSP 2022 - 2022 IEEE International Conference On Acoustics, Speech And Signal Processing (ICASSP). pp. 4693-4697 https:\/\/doi.org\/10.1109\/ICASSP43922.2022.9746924","DOI":"10.1109\/ICASSP43922.2022.9746924"},{"key":"2023_CR47","doi-asserted-by":"crossref","unstructured":"Li P, Gu J, Kuen J, Morariu V, Zhao H, Jain R, Manjunatha V, Liu H (2021) SelfDoc: Self-Supervised Document Representation Learning. 2021 IEEE\/CVF Conference On Computer Vision And Pattern Recognition (CVPR). pp. 5648-5656 |DOIurl10.1109\/CVPR46437.2021.00560","DOI":"10.1109\/CVPR46437.2021.00560"},{"key":"2023_CR48","doi-asserted-by":"publisher","unstructured":"Jaegle A, Borgeaud S, Alayrac J, Doersch C, Ionescu C, Ding D, Koppula S, Zoran D, Brock A, Shelhamer E, Henaff O, Botvinick M, Zisserman A, Vinyals O, Carreira J (2022) Perceiver IO: A General Architecture for Structured Inputs & Outputs. International Conference On Learning Representations. https:\/\/doi.org\/10.48550\/arXiv.2107.14795","DOI":"10.48550\/arXiv.2107.14795"},{"key":"2023_CR49","doi-asserted-by":"publisher","first-page":"743","DOI":"10.1038\/s41551-023-01045-x","volume":"7","author":"H Zhou","year":"2023","unstructured":"Zhou H, Yu Y, Wang C, Zhang S, Gao Y, Pan J, Shao J, Lu G, Zhang K, Li W (2023) A transformer-based representation-learning model with unified processing of multimodal input for clinical diagnostics. Nature Biomed Eng 7:743\u2013755. https:\/\/doi.org\/10.1038\/s41551-023-01045-x","journal-title":"Nature Biomed Eng"},{"key":"2023_CR50","doi-asserted-by":"publisher","unstructured":"Tsai J, Chu W (2022) Multimodal Fusion with Cross-Modal Attention for Action Recognition in Still Images. Proceedings Of The 4th ACM International Conference On Multimedia In Asia. https:\/\/doi.org\/10.1145\/3551626.3564960","DOI":"10.1145\/3551626.3564960"},{"key":"2023_CR51","doi-asserted-by":"publisher","unstructured":"Jozefowicz R, Vinyals O, Schuster M, Shazeer N, Wu Y (2016) Exploring the Limits of Language Modeling. https:\/\/doi.org\/10.48550\/arXiv.1602.02410","DOI":"10.48550\/arXiv.1602.02410"},{"key":"2023_CR52","doi-asserted-by":"publisher","unstructured":"Dauphin Y, Fan A, Auli M, Grangier D (2017) Language modeling with gated convolutional networks. Proceedings Of The 34th International Conference On Machine Learning - Volume 70. pp. 933-941 https:\/\/doi.org\/10.5555\/3305381.3305478","DOI":"10.5555\/3305381.3305478"},{"key":"2023_CR53","doi-asserted-by":"publisher","unstructured":"Hua W, Dai Z, Liu H, Le Q (2022) Transformer Quality in Linear Time. Proceedings Of The 39th International Conference On Machine Learning. 162 pp. 9099-9117 (7,17), https:\/\/doi.org\/10.48550\/arXiv.2202.10447","DOI":"10.48550\/arXiv.2202.10447"},{"key":"2023_CR54","doi-asserted-by":"publisher","unstructured":"Ye J, Zhao J, Ye K, Xu C (2021) Multi-Graph Convolutional Network for Relationship-Driven Stock Movement Prediction. 2020 25th International Conference On Pattern Recognition (ICPR). pp. 6702-6709 https:\/\/doi.org\/10.1109\/ICPR48806.2021.9412695","DOI":"10.1109\/ICPR48806.2021.9412695"},{"key":"2023_CR55","doi-asserted-by":"publisher","unstructured":"Touvron H, Lavril T, Izacard G, Martinet X, Lachaux M, Lacroix T, Rozi\u00e8re B, Goyal N, Hambro E, Azhar F, Rodriguez A, Joulin A, Grave E, Lample G (2023) LLaMA: Open and Efficient Foundation Language Models. https:\/\/doi.org\/10.48550\/arXiv.2302.13971","DOI":"10.48550\/arXiv.2302.13971"},{"key":"2023_CR56","doi-asserted-by":"publisher","unstructured":"Veli\u010dkovi\u0107 P, Cucurull G, Casanova A, Romero A, Li\u00f2 P, Bengio Y (2018) Graph Attention Networks. International Conference On Learning Representations. https:\/\/doi.org\/10.48550\/arXiv.1710.10903","DOI":"10.48550\/arXiv.1710.10903"},{"key":"2023_CR57","doi-asserted-by":"publisher","unstructured":"Sak H, Senior A, Beaufays F (2014) Long Short-Term Memory Based Recurrent Neural Network Architectures for Large Vocabulary Speech Recognition. https:\/\/doi.org\/10.48550\/arXiv.1402.1128","DOI":"10.48550\/arXiv.1402.1128"},{"key":"2023_CR58","doi-asserted-by":"publisher","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez A, Kaiser \u0141, Polosukhin I (2017) Attention is all you need. Proceedings Of The 31st International Conference On Neural Information Processing Systems. pp. 6000-6010 https:\/\/doi.org\/10.5555\/3295222.3295349","DOI":"10.5555\/3295222.3295349"},{"key":"2023_CR59","doi-asserted-by":"publisher","unstructured":"Wu H, Zhang W, Shen W, Wang J (2018) Hybrid Deep Sequential Modeling for Social Text-Driven Stock Prediction. Proceedings Of The 27th ACM International Conference On Information And Knowledge Management. pp. 1627-1630 https:\/\/doi.org\/10.1145\/3269206.3269290","DOI":"10.1145\/3269206.3269290"},{"key":"2023_CR60","doi-asserted-by":"publisher","unstructured":"Matthews B (1975) Comparison of the predicted and observed secondary structure of T4 phage lysozyme. Biochimica Et Biophysica Acta (BBA) - Protein Structure. 405, 442-451 https:\/\/doi.org\/10.1016\/0005-2795(75)90109-9","DOI":"10.1016\/0005-2795(75)90109-9"},{"key":"2023_CR61","doi-asserted-by":"publisher","unstructured":"Qin Y, Song D, Cheng H, Cheng W, Jiang G, Cottrell G (2017) A dual-stage attention-based recurrent neural network for time series prediction. Proceedings Of The 26th International Joint Conference On Artificial Intelligence. pp. 2627-2633 https:\/\/doi.org\/10.5555\/3172077.3172254","DOI":"10.5555\/3172077.3172254"},{"key":"2023_CR62","doi-asserted-by":"publisher","unstructured":"Mikolov T, Chen K, Corrado G, Dean J (2013) Efficient Estimation of Word Representations in Vector Space. https:\/\/doi.org\/10.48550\/arXiv.1301.3781","DOI":"10.48550\/arXiv.1301.3781"},{"key":"2023_CR63","doi-asserted-by":"publisher","unstructured":"Devlin J, Chang M, Lee K, Toutanova K (2019) BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. Proceedings Of The 2019 Conference Of The North American Chapter Of The Association For Computational Linguistics: Human Language Technologies, Volume 1 (Long And Short Papers). pp. 4171-4186 (6), https:\/\/doi.org\/10.18653\/v1\/N19-1423","DOI":"10.18653\/v1\/N19-1423"},{"key":"2023_CR64","doi-asserted-by":"publisher","unstructured":"Perozzi B, Al-Rfou R, Skiena S (2014) DeepWalk: online learning of social representations. Proceedings Of The 20th ACM SIGKDD International Conference On Knowledge Discovery And Data Mining. pp. 701-710 https:\/\/doi.org\/10.1145\/2623330.2623732","DOI":"10.1145\/2623330.2623732"},{"key":"2023_CR65","doi-asserted-by":"publisher","unstructured":"Kipf T, Welling M (2017) Semi-Supervised Classification with Graph Convolutional Networks. International Conference On Learning Representations. https:\/\/doi.org\/10.48550\/arXiv.1609.02907","DOI":"10.48550\/arXiv.1609.02907"},{"key":"2023_CR66","doi-asserted-by":"crossref","unstructured":"Wang M, Izumi K, Sakaji H (2014) LLMFactor: Extracting Profitable Factors through Prompts for Explainable Stock Movement Prediction. Findings Of The Association For Computational Linguistics: ACL 2024. pp. 3120-3131 (8), https:\/\/aclanthology.org\/2024.findings-acl.185\/","DOI":"10.18653\/v1\/2024.findings-acl.185"},{"key":"2023_CR67","doi-asserted-by":"publisher","unstructured":"Elahi A, Taghvaei F (2024) Combining financial data and news articles for stock price movement prediction using large language models. 2024 IEEE International Conference On Big Data (BigData). pp. 4875-4883 https:\/\/doi.org\/10.1109\/BigData62323.2024.10825449","DOI":"10.1109\/BigData62323.2024.10825449"},{"key":"2023_CR68","doi-asserted-by":"publisher","unstructured":"Warner B, Chaffin A, Clavi\u00e9 B, Weller O, Hallstr\u00f6m O, Taghadouini S, Gallagher A, Biswas R, Ladhak F, Aarsen T, Cooper N, Adams G, Howard J, Poli I (2024) Smarter, Better, Faster, Longer: A Modern Bidirectional Encoder for Fast, Memory Efficient, and Long Context Finetuning and Inference. https:\/\/doi.org\/10.48550\/arXiv.2412.13663","DOI":"10.48550\/arXiv.2412.13663"}],"container-title":["Complex &amp; Intelligent Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02023-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s40747-025-02023-3\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s40747-025-02023-3.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,31]],"date-time":"2025-08-31T05:27:00Z","timestamp":1756618020000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s40747-025-02023-3"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,22]]},"references-count":68,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["2023"],"URL":"https:\/\/doi.org\/10.1007\/s40747-025-02023-3","relation":{},"ISSN":["2199-4536","2198-6053"],"issn-type":[{"value":"2199-4536","type":"print"},{"value":"2198-6053","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,22]]},"assertion":[{"value":"20 March 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 July 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 July 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no Conflict of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"396"}}