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New LLM research targets financial forecasting with multimodal and cross-lingual models · 6 sources tracked

By PulseAugur Editorial · [6 sources] ·

Researchers are developing advanced language models for financial forecasting and time-series analysis. OpenTSLM TeeMoE unifies forecasting, contextual prediction, and temporal reasoning by integrating multiple specialized experts. DualCast uses a dual-path framework to forecast financial time-series, incorporating news at prediction time. Another approach, NMIXX, adapts existing encoders for cross-lingual financial text analysis, improving financial correlation while slightly decreasing general domain correlation. Additionally, a new benchmark, MM-FinEval, has been created to evaluate multimodal LLMs on financial tasks using text, audio, and visual data from earnings calls. Finally, a study shows that post-training language models like Qwen3-4B can significantly improve their stock price forecasting capabilities. AI

IMPACT These advancements in LLMs for finance could lead to more sophisticated and accurate market predictions and analysis tools.

RANK_REASON The cluster contains multiple research papers detailing new models, benchmarks, and methods for financial forecasting and time-series analysis.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 6 sources. How we write summaries →

New LLM research targets financial forecasting with multimodal and cross-lingual models · 6 sources tracked

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The cluster contains multiple research papers detailing new models, benchmarks, and methods for financial forecasting and time-series analysis.
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6 independent sources
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3 days old
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COVERAGE [6]

  1. arXiv cs.LG TIER_1 English(EN) · Tony Chen, Timo Stoffregen, Maxwell Xu, Thomas Kaar, Martin Maritsch, Geremia Pompei, Nicolas Zumarraga, Robert Jakob, Paul Schmiedmayer, Patrick Langer, Juncheng Liu ·

    OpenTSLM TeeMoE: A Unified Time-Series Language Model for Forecasting, Contextual Prediction, and Reasoning

    arXiv:2609.40265v1 Announce Type: new Abstract: Real-world time-series applications increasingly require models that can handle time series forecasting, context-conditioned prediction, and language-based temporal reasoning. Yet current time-series foundation models remain fragmen…

  2. arXiv cs.AI TIER_1 English(EN) · Sujung Kim, Seung Hwan Cho, Sangjin Park, Young-Min Kim ·

    Reasoning Externalization for Faithful Large Language Model Narratives of Stock Return Predictions

    arXiv:2609.38869v1 Announce Type: new Abstract: In finance, interpreting machine learning predictions is essential, yet the numerical outputs of explainable AI can be difficult for non-experts to understand. While large language models (LLMs) can translate these outputs into natu…

  3. arXiv cs.AI TIER_1 English(EN) · Wentao Zhao, Hongqiang Wu, Shanghang Liu, Zhaochen Zan, Yu Zhang, Biqing Huang ·

    DualCast: A Dual-Path Language Model for Bimodal Financial Time-Series Forecasting

    arXiv:2609.38197v1 Announce Type: cross Abstract: Financial time-series forecasting must capture price dynamics across heterogeneous assets while incorporating news available at prediction time. We introduce DualCast, a dual-path framework that extends a frozen language model wit…

  4. arXiv cs.AI TIER_1 English(EN) · Dong Shu, Yanguang Liu, Huopu Zhang, Saisai Hu, Haiyan Zhao, Hekun Huang, Mengnan Du ·

    MM-FinEval: A Multi-Task Multimodal Benchmark for Real-World Financial Forecasting

    arXiv:2609.38523v1 Announce Type: cross Abstract: Financial forecasting from earnings conference calls requires models to reason over complex corporate disclosures, market expectations, and subtle communication signals. However, existing financial benchmarks are often limited to …

  5. arXiv cs.AI TIER_1 English(EN) · Hanwool Lee, Sara Yu, Yewon Hwang, Jonghyun Choi, Heejae Ahn, Sungbum Jung, Youngjae Yu ·

    NMIXX: Domain-Adapted Neural Embeddings for Cross-Lingual eXploration of Finance

    arXiv:2507.09601v3 Announce Type: replace-cross Abstract: Financial text embeddings must distinguish changes in event status, perspective, and obligations even when passages share similar wording. NMIXX adapts existing encoders through 18.8k source-linked triplets: paraphrases an…

  6. arXiv cs.CL TIER_1 English(EN) · Jiacheng Guo, Suozhi Huang, Shuzhen Li, Yunlong Gao, Zerui Cheng, Jason Ge, Shushu Liang, Zihao Li, Hao Lu, Ming Yin, Shilong Liu, Jiashuo Liu, Xu Kuang, Mengdi Wang ·

    Can Language Models Learn to Forecast Stock Prices

    arXiv:2609.36914v1 Announce Type: new Abstract: Post-training has been shown to significantly improve language models' performance on tasks with verifiable outcomes, including mathematical reasoning, software engineering, and computer use. However, whether the same approach can i…

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