mlsanityData to 14 Oct 2026

XForecast: The KDD 2026 Finance Day Challenge on News-Grounded Financial Forecasting

arXiv 2610.16131 ↗First posted 12 Oct 2026cs.LGcited by 0

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Abstract (arXiv)

Financial forecasting has traditionally relied on numerical time-series (TS) signals, while textual information such as news remains largely underutilized. We introduce XForecast, a benchmark and competition for news-grounded financial forecasting, where the task is to predict stock price movements four weeks ahead using both historical price data and paired news signals. XForecast combines stock price data with curated financial text generated using FinTexTS, a multimodal dataset generation method. It covers 100 large-cap stocks from 2019--2023, with training on 2019--2022 and evaluation on 2023. To better evaluate models that leverage textual signals, we adopt a weighted hit rate metric that places greater emphasis on instances where news information is expected to have stronger influence. The XForecast Challenge, hosted on Kaggle as part of KDD 2026 Finance Day, aims to benchmark methods for integrating financial TS and textual information.

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References · 13

  1. 2017Attention Is All You Needcited by 60,361
  2. 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
  3. 2022A Time Series is Worth 64 Words: Long-term Forecasting with Transformerscited by 2,039
  4. 2018An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modelingcited by 1,476
  5. 2022TimesNet: Temporal 2D-Variation Modeling for General Time Series Analysiscited by 1,058
  6. 2019Temporal Fusion Transformers for Interpretable Multi-horizon Time Series Forecastingcited by 761
  7. 2024Time-MMD: Multi-Domain Multimodal Dataset for Time Series Analysiscited by 74
  8. 2025Time-VLM: Exploring Multimodal Vision-Language Models for Augmented Time Series Forecastingcited by 63
  9. 2025Llama-Embed-Nemotron-8B: A Universal Text Embedding Model for Multilingual and Cross-Lingual Taskscited by 51
  10. 2026FinTexTS: Financial Text-Paired Time-Series Dataset via Semantic-Based and Multi-Level Pairingcited by 5
  11. 2024Double-Path Adaptive-correlation Spatial-Temporal Inverted Transformer for Stock Time Series Forecastingcited by 4
  12. 2026TiMi: Empower Time Series Transformers with Multimodal Mixture of Expertscited by 4
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  1. 2025Data-Efficient Realized Volatility Forecasting with Vision Transformerscited by 3

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