{"id":32228,"date":"2026-01-21T17:01:31","date_gmt":"2026-01-21T17:01:31","guid":{"rendered":"https:\/\/lamarr-institute.org\/publication\/towards-unified-multimodal-financial-forecasting-integrating-sentiment-embeddings-and-market-indicators-via-cross-modal-attention\/"},"modified":"2026-06-08T13:18:37","modified_gmt":"2026-06-08T13:18:37","slug":"towards-unified-multimodal-financial-forecasting-integrating-sentiment-embeddings-and-market-indicators-via-cross-modal-attention","status":"publish","type":"publication","link":"https:\/\/lamarr-institute.org\/de\/publication\/towards-unified-multimodal-financial-forecasting-integrating-sentiment-embeddings-and-market-indicators-via-cross-modal-attention\/","title":{"rendered":"Towards Unified Multimodal Financial Forecasting: Integrating Sentiment Embeddings and Market Indicators via Cross-Modal Attention"},"content":{"rendered":"<p>We propose {STONK} (Stock Optimization using News Knowledge), a multimodal framework integrating numerical market indicators with sentiment-enriched news embeddings to improve daily stock-movement prediction. By combining numerical \\&#038; textual embeddings via feature concatenation and cross-modal attention, our unified pipeline addresses limitations of isolated analyses. Backtesting shows {STONK} outperforms numeric-only baselines. A comprehensive evaluation of fusion strategies and model configurations offers evidence-based guidance for scalable multimodal financial forecasting. Source code is available on {GitHub}11https:\/\/github.com\/sarthak-12\/thesis-dsaa\/.<\/p>\n","protected":false},"excerpt":{"rendered":"<p>We propose {STONK} (Stock Optimization using News Knowledge), a multimodal framework integrating numerical market indicators with sentiment-enriched news embeddings to improve daily stock-movement prediction. By combining numerical \\&#038; textual embeddings via feature concatenation and cross-modal attention, our unified pipeline addresses limitations of isolated analyses. Backtesting shows {STONK} outperforms numeric-only baselines. A comprehensive evaluation of fusion strategies and model configurations offers evidence-based guidance for scalable multimodal financial forecasting. Source code is [&hellip;]<\/p>\n","protected":false},"author":12,"featured_media":0,"template":"","meta":{"_acf_changed":false,"footnotes":""},"publication-type":[32],"class_list":["post-32228","publication","type-publication","status-publish","hentry","publication-type-inproceedings"],"acf":[],"publishpress_future_workflow_manual_trigger":{"enabledWorkflows":[]},"_links":{"self":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32228","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication"}],"about":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/types\/publication"}],"author":[{"embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/users\/12"}],"version-history":[{"count":0,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication\/32228\/revisions"}],"wp:attachment":[{"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/media?parent=32228"}],"wp:term":[{"taxonomy":"publication-type","embeddable":true,"href":"https:\/\/lamarr-institute.org\/de\/wp-json\/wp\/v2\/publication-type?post=32228"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}