FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty.

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Bibliographic Details
Title: FusionLSTM-CNF: a confidence-calibrated multi-modal late fusion framework for robust stock movement prediction under uncertainty.
Authors: Wang TW; Institute for Advanced Studies (IAS), Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan, Malaysia. wangtianwen522@gmail.com., Shaikh ZA; Department of Computer Science and Information Technology, Benazir Bhutto Shaheed University Lyari, Karachi, 75660, Pakistan.; School of Engineering, École Polytechnique Fédérale de Lausanne, 1015, Lausanne, Switzerland., Yong SL; Department of Economics, Faculty of Business and Economics, Universiti Malaya, Kuala Lumpur, Wilayah Persekutuan, Malaysia., Elmannai H; Department of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, 11671, Riyadh, Saudi Arabia., Por LY; Center of Research for Cyber Security and Network (CSNET), Faculty of Computer Science and Information Technology, Universiti Malaya, Kuala Lumpur, Malaysia. porlip@um.edu.my.
Source: Scientific reports [Sci Rep] 2026 Apr 09; Vol. 16 (1). Date of Electronic Publication: 2026 Apr 09.
Publication Type: Journal Article
Journal Info: Publisher: Nature Publishing Group Country of Publication: England NLM ID: 101563288 Publication Model: Electronic Cited Medium: Internet ISSN: 2045-2322 (Electronic) Linking ISSN: 20452322 NLM ISO Abbreviation: Sci Rep Subsets: MEDLINE; PubMed not MEDLINE
Database: MEDLINE Ultimate
Description
ISSN:2045-2322
DOI:10.1038/s41598-026-43381-3