{
  "abstract": "Stock movement prediction remains challenging because financial time series are nonstationary, noisy, and interdependent across heterogeneous information sources. Conventional multimodal forecasting methods often integrate price, text, and inter-firm relationships into a single representation, making it difficult to capture modality-pair-specific interactions and their varying relevance across market conditions. To address this issue, this study proposes Pairwise Cross-Modal Attention Fusion with Mixture-of-Experts (PaCMoE), which explicitly decomposes multimodal interactions into three modality pairs—Price–Text, Price–Graph, and Text–Graph. Each pair is modeled by an independent Cross-Modal Attention expert, and a Dense Mixture-of-Experts router adaptively integrates the resulting representations according to the input. This design enables separate modeling of pairwise interactions and input-dependent weighting of their contributions. The modality encoders combine PatchTST for prices, FinBERT with canonical correlation analysis for text, and a single-relation R-GCN for daily correlation graphs. PaCMoE is evaluated using chronological data splitting, validation-based model selection, and ten random seeds on the ACL18 (StockNet) benchmark. The proposed method achieves an average accuracy of 60.69% and an MCC of 0.2200, which are comparable to or higher than the multi-seed values reported for LSTM-based Adv-ALSTM and Transformer-based DTML. Additional evaluations include single-run ablation studies, three-class prediction incorporating neutral days, precision–recall analysis, and visualizations of router and attention weights to examine component contributions and model behavior. Training takes approximately 30 min per run on a single NVIDIA GeForce RTX 3090. These results support the effectiveness of explicitly modeling pairwise cross-modal interactions and adaptively integrating their contributions for stock movement prediction on the ACL18 benchmark.",
  "abstract_shown": true,
  "affiliations": [
    "School of Informatics, Kogakuin University, Tokyo 163-8677, Japan",
    "Graduate School of Engineering, Kogakuin University, Tokyo 163-8677, Japan"
  ],
  "article_type": null,
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Eiji Nagata"
    },
    {
      "affs": [
        1
      ],
      "name": "Riku Motomochi"
    },
    {
      "affs": [
        0,
        1
      ],
      "name": "Qiu Chen",
      "orcid": "0000-0003-3079-9207"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/dqhgp5rbd600v.bib",
  "cited_by": 0,
  "doi": "10.3390/math14213948",
  "fulltext_url": null,
  "id": "dqhgp5rbd600v",
  "issns": [
    "2227-7390"
  ],
  "landing_url": "https://www.mdpi.com/2227-7390/14/21/3948",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "article",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [],
  "openalex": null,
  "pdf_url": null,
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-29",
  "publisher": "MDPI AG",
  "ris": "https://research.draconitic.com/paper/dqhgp5rbd600v.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:18:29Z",
      "native_id": "10.3390/math14213948",
      "source": "crossref",
      "url": "https://www.mdpi.com/2227-7390/14/21/3948"
    }
  ],
  "subjects": [
    {
      "field": "mathematics",
      "origin": "issn:2227-7390",
      "subfield": "general"
    }
  ],
  "title": "PaCMoE: Pairwise Cross-Modal Attention Fusion with Mixture-of-Experts for Stock Movement Prediction",
  "title_text": "PaCMoE: Pairwise Cross-Modal Attention Fusion with Mixture-of-Experts for Stock Movement Prediction",
  "type": "peer-reviewed",
  "updated_at": "2026-10-31T00:20:46Z",
  "url": "https://research.draconitic.com/paper/dqhgp5rbd600v",
  "venue": "Mathematics",
  "versions": []
}