{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T13:19:10Z","timestamp":1777987150285,"version":"3.51.4"},"reference-count":51,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/100012492","name":"Beijing Municipal Outstanding Young Talents","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100012492","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62122089"],"award-info":[{"award-number":["62122089"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.artint.2026.104539","type":"journal-article","created":{"date-parts":[[2026,4,10]],"date-time":"2026-04-10T16:57:30Z","timestamp":1775840250000},"page":"104539","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["ExPred: Explainable stock movement prediction via hybrid reflection and direct preference hierarchical optimization"],"prefix":"10.1016","volume":"355","author":[{"ORCID":"https:\/\/orcid.org\/0009-0006-8074-3243","authenticated-orcid":false,"given":"Shuqi","family":"Li","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Heyue","family":"Lin","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Xin","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rui","family":"Yan","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"78","reference":[{"issue":"2","key":"10.1016\/j.artint.2026.104539_bib0001","doi-asserted-by":"crossref","first-page":"383","DOI":"10.2307\/2325486","article-title":"Efficient capital markets","volume":"25","author":"Fama","year":"1970","journal-title":"J. Financ."},{"key":"10.1016\/j.artint.2026.104539_bib0002","series-title":"Using News Articles to Predict Stock Price Movements","volume":"Vol. 17","author":"Gidofalvi","year":"2001"},{"key":"10.1016\/j.artint.2026.104539_bib0003","series-title":"2013 Fourth International Conference on Intelligent Control and Information Processing (ICICIP)","first-page":"354","article-title":"A fundamental analysis-based method for stock market forecasting","author":"Chen","year":"2013"},{"key":"10.1016\/j.artint.2026.104539_bib0004","series-title":"2010 International Conference on Machine Learning and Cybernetics","first-page":"2605","article-title":"Forecasting the change of intraday stock price by using text mining news of stock","volume":"Vol. 5","author":"Cheng","year":"2010"},{"key":"10.1016\/j.artint.2026.104539_bib0005","first-page":"72","article-title":"Integrating genetic algorithms and text learning for financial prediction","author":"Thomas","year":"2000","journal-title":"Data Min. Evol. Algo."},{"key":"10.1016\/j.artint.2026.104539_bib0006","series-title":"Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","first-page":"1970","article-title":"Stock movement prediction from tweets and historical prices","author":"Xu","year":"2018"},{"key":"10.1016\/j.artint.2026.104539_bib0007","series-title":"Proceedings of the Eleventh ACM International Conference on Web Search and Data Mining","first-page":"261","article-title":"Listening to chaotic whispers: a deep learning framework for news-oriented stock trend prediction","author":"Hu","year":"2018"},{"key":"10.1016\/j.artint.2026.104539_bib0008","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.artint.2026.104539_bib0009","series-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Vol. 1: Long Papers)","first-page":"12164","article-title":"Causality-guided multi-memory interaction network for multivariate stock price movement prediction","author":"Luo","year":"2023"},{"key":"10.1016\/j.artint.2026.104539_bib0010","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"4418","article-title":"Coupling macro-sector-micro financial indicators for learning stock representations with less uncertainty","volume":"Vol. 35","author":"Wang","year":"2021"},{"key":"10.1016\/j.artint.2026.104539_bib0011","series-title":"IJCAI","first-page":"1461","article-title":"Human-Centric justification of machine learning predictions","volume":"Vol. 2017","author":"Biran","year":"2017"},{"key":"10.1016\/j.artint.2026.104539_bib0012","unstructured":"W.X. Zhao, K. Zhou, J. Li, T. Tang, X. Wang, Y. Hou, Y. Min, B. Zhang, J. Zhang, Z. Dong, et al., A survey of large language models,(2023) arXiv preprint arXiv: 2303.18223."},{"key":"10.1016\/j.artint.2026.104539_bib0013","unstructured":"H. Liu, C. Sferrazza, P. Abbeel, Chain of hindsight aligns language models with feedback,(2023) arXiv preprint arXiv: 2302.02676."},{"key":"10.1016\/j.artint.2026.104539_bib0014","first-page":"24824","article-title":"Chain-of-thought prompting elicits reasoning in large language models","volume":"35","author":"Wei","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.artint.2026.104539_bib0015","first-page":"47432","article-title":"Causalstock: deep end-to-end causal discovery for news-driven multi-stock movement prediction","volume":"37","author":"Li","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.artint.2026.104539_bib0016","series-title":"Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)","first-page":"12164","article-title":"Causality-guided multi-memory interaction network for multivariate stock price movement prediction","author":"Luo","year":"2023"},{"key":"10.1016\/j.artint.2026.104539_bib0017","doi-asserted-by":"crossref","unstructured":"H. Yang, X.-Y. Liu, C.D. Wang, Fingpt: open-source financial large language models,(2023) arXiv preprint arXiv: 2306.06031.","DOI":"10.2139\/ssrn.4489826"},{"key":"10.1016\/j.artint.2026.104539_bib0018","unstructured":"S. Wu, O. Irsoy, S. Lu, V. Dabravolski, M. Dredze, S. Gehrmann, P. Kambadur, D. Rosenberg, G. Mann, Bloomberggpt: a large language model for finance,(2023) arXiv preprint arXiv: 2303.17564."},{"key":"10.1016\/j.artint.2026.104539_bib0019","unstructured":"H. Tong, J. Li, N. Wu, M. Gong, D. Zhang, Q. Zhang, Ploutos: towards interpretable stock movement prediction with financial large language model,(2024) arXiv preprint arXiv: 2403.00782."},{"key":"10.1016\/j.artint.2026.104539_bib0020","doi-asserted-by":"crossref","unstructured":"M. Wang, K. Izumi, H. Sakaji, LLMFactor: extracting profitable factors through prompts for explainable stock movement prediction, (2024) arXiv preprint arXiv: 2406.10811.","DOI":"10.18653\/v1\/2024.findings-acl.185"},{"key":"10.1016\/j.artint.2026.104539_bib0021","series-title":"Proceedings of the AAAI Symposium Series","first-page":"595","article-title":"FinMem: a performance-enhanced LLM trading agent with layered memory and character design","volume":"Vol. 3","author":"Yu","year":"2024"},{"key":"10.1016\/j.artint.2026.104539_bib0022","series-title":"Proceedings of the ACM on Web Conference 2024","first-page":"4304","article-title":"Learning to generate explainable stock predictions using self-reflective large language models","author":"Koa","year":"2024"},{"key":"10.1016\/j.artint.2026.104539_bib0023","unstructured":"Q. Xie, W. Han, X. Zhang, Y. Lai, M. Peng, A. Lopez-Lira, J. Huang, Pixiu: a large language model, instruction data and evaluation benchmark for finance,(2023) arXiv preprint arXiv: 2306.05443."},{"key":"10.1016\/j.artint.2026.104539_bib0024","author":"Zhang"},{"key":"10.1016\/j.artint.2026.104539_bib0025","series-title":"2017 International Joint Conference on Neural Networks (IJCNN)","first-page":"1419","article-title":"Stock market\u2019s price movement prediction with LSTM neural networks","author":"Nelson","year":"2017"},{"key":"10.1016\/j.artint.2026.104539_bib0026","doi-asserted-by":"crossref","unstructured":"Y. Qin, D. Song, H. Chen, W. Cheng, G. Jiang, G. Cottrell, A dual-stage attention-based recurrent neural network for time series prediction, (2017) arXiv preprint arXiv: 1704.02971.","DOI":"10.24963\/ijcai.2017\/366"},{"key":"10.1016\/j.artint.2026.104539_bib0027","series-title":"Technical Report","article-title":"Predicting Returns with Text Data","author":"Ke","year":"2019"},{"issue":"12","key":"10.1016\/j.artint.2026.104539_bib0028","doi-asserted-by":"crossref","first-page":"4759","DOI":"10.1093\/rfs\/hhad042","article-title":"Narrative asset pricing: interpretable systematic risk factors from news text","volume":"36","author":"Bybee","year":"2023","journal-title":"Rev. Financ. Stud."},{"key":"10.1016\/j.artint.2026.104539_bib0029","series-title":"2019 International Engineering Conference (IEC)","first-page":"200","article-title":"An overview of bag of words; importance, implementation, applications, and challenges","author":"Qader","year":"2019"},{"key":"10.1016\/j.artint.2026.104539_bib0030","series-title":"Proceedings of SDAIR-94, 3rd Annual Symposium on Document Analysis and Information Retrieval","first-page":"14","article-title":"N-gram-based text categorization","volume":"Vol. 161175","author":"Cavnar","year":"1994"},{"key":"10.1016\/j.artint.2026.104539_bib0031","series-title":"Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"1415","article-title":"Using structured events to predict stock price movement: an empirical investigation","author":"Ding","year":"2014"},{"key":"10.1016\/j.artint.2026.104539_bib0032","series-title":"Twenty-Fourth International Joint Conference on Artificial Intelligence","article-title":"Deep learning for event-driven stock prediction","author":"Ding","year":"2015"},{"key":"10.1016\/j.artint.2026.104539_bib0033","doi-asserted-by":"crossref","unstructured":"A. Lopez-Lira, Y. Tang, Can chatgpt forecast stock price movements? return predictability and large language models,(2023) arXiv preprint arXiv: 2304.07619.","DOI":"10.2139\/ssrn.4412788"},{"key":"10.1016\/j.artint.2026.104539_bib0034","unstructured":"Y. Chen, B.T. Kelly, D. Xiu, Expected returns and large language models, Available at SSRN 4416687 (2022)."},{"key":"10.1016\/j.artint.2026.104539_bib0035","unstructured":"L. Xiao, X. Chen, Enhancing llm with evolutionary fine tuning for news summary generation, (2023) arXiv preprint arXiv: 2307.02839."},{"key":"10.1016\/j.artint.2026.104539_bib0036","unstructured":"X. Pu, M. Gao, X. Wan, Summarization is (almost) dead,(2023) arXiv preprint arXiv: 2309.09558."},{"key":"10.1016\/j.artint.2026.104539_bib0037","unstructured":"Q. Dong, L. Li, D. Dai, C. Zheng, Z. Wu, B. Chang, X. Sun, J. Xu, Z. Sui, A survey on in-context learning,(2022) arXiv preprint arXiv: 2301.00234."},{"key":"10.1016\/j.artint.2026.104539_bib0038","series-title":"International Conference on Machine Learning","first-page":"12697","article-title":"Calibrate before use: improving few-shot performance of language models","author":"Zhao","year":"2021"},{"key":"10.1016\/j.artint.2026.104539_bib0039","first-page":"53728","article-title":"Direct preference optimization: your language model is secretly a reward model","volume":"36","author":"Rafailov","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.artint.2026.104539_bib0040","unstructured":"Y. Wu, Z. Sun, H. Yuan, K. Ji, Y. Yang, Q. Gu, Self-play preference optimization for language model alignment, (2024) arXiv preprint arXiv: 2405.00675."},{"key":"10.1016\/j.artint.2026.104539_bib0041","doi-asserted-by":"crossref","unstructured":"K. D\u2019Oosterlinck, W. Xu, C. Develder, T. Demeester, A. Singh, C. Potts, D. Kiela, S. Mehri, Anchored preference optimization and contrastive revisions: addressing underspecification in alignment,(2024) arXiv preprint arXiv: 2408.06266.","DOI":"10.1162\/tacl_a_00748"},{"key":"10.1016\/j.artint.2026.104539_bib0042","doi-asserted-by":"crossref","unstructured":"H. Chen, G. He, H. Su, J. Zhu, Noise contrastive alignment of language models with explicit rewards,(2024) arXiv preprint arXiv: 2402.05369.","DOI":"10.52202\/079017-3741"},{"key":"10.1016\/j.artint.2026.104539_bib0043","unstructured":"S.R. Chowdhury, A. Kini, N. Natarajan, Provably robust dpo: aligning language models with noisy feedback,(2024) arXiv preprint arXiv: 2403.00409."},{"key":"10.1016\/j.artint.2026.104539_bib0044","first-page":"27730","article-title":"Training language models to follow instructions with human feedback","volume":"35","author":"Ouyang","year":"2022","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.artint.2026.104539_bib0045","series-title":"Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)","first-page":"8415","article-title":"Deep attentive learning for stock movement prediction from social media text and company correlations","author":"Sawhney","year":"2020"},{"key":"10.1016\/j.artint.2026.104539_bib0046","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11604","article-title":"Numhtml: numeric-oriented hierarchical transformer model for multi-task financial forecasting","volume":"vol. 36","author":"Yang","year":"2022"},{"key":"10.1016\/j.artint.2026.104539_bib0047","unstructured":"W.-L. Chiang, Z. Li, Z. Lin, Y. Sheng, Z. Wu, H. Zhang, L. Zheng, S. Zhuang, Y. Zhuang, J.E. Gonzalez, et al., Vicuna: an open-source chatbot impressing gpt-4 with 90 %* chatgpt quality, See https:\/\/vicuna. lmsys. org (accessed 14 April 2023) 2 (3) (2023) 6."},{"key":"10.1016\/j.artint.2026.104539_bib0048","unstructured":"H. Touvron, T. Lavril, G. Izacard, X. Martinet, M.-A. Lachaux, T. Lacroix, B. Rozi\u00e8re, N. Goyal, E. Hambro, F. Azhar, et al., Llama: open and efficient foundation language models,(2023) arXiv preprint arXiv: 2302.13971."},{"key":"10.1016\/j.artint.2026.104539_bib0049","unstructured":"OpenAI, GPT-4o mini: advancing cost-efficient intelligence, 2024, Accessed: 2025-01-22."},{"key":"10.1016\/j.artint.2026.104539_bib0050","unstructured":"Y. Zhao, R. Joshi, T. Liu, M. Khalman, M. Saleh, P.J. Liu, Slic-hf: sequence likelihood calibration with human feedback, (2023) arXiv preprint arXiv: 2305.10425."},{"key":"10.1016\/j.artint.2026.104539_bib0051","series-title":"International Conference on Artificial Intelligence and Statistics","first-page":"4447","article-title":"A general theoretical paradigm to understand learning from human preferences","author":"Azar","year":"2024"}],"container-title":["Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0004370226000652?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0004370226000652?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,5,5]],"date-time":"2026-05-05T12:25:03Z","timestamp":1777983903000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0004370226000652"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":51,"alternative-id":["S0004370226000652"],"URL":"https:\/\/doi.org\/10.1016\/j.artint.2026.104539","relation":{},"ISSN":["0004-3702"],"issn-type":[{"value":"0004-3702","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"ExPred: Explainable stock movement prediction via hybrid reflection and direct preference hierarchical optimization","name":"articletitle","label":"Article Title"},{"value":"Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.artint.2026.104539","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"104539"}}