@inproceedings{ong-etal-2025-deriving,
    title = "Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation",
    author = "Ong, Keane  and
      Mao, Rui  and
      Varshney, Deeksha  and
      Liang, Paul Pu  and
      Cambria, Erik  and
      Mengaldo, Gianmarco",
    editor = "Christodoulopoulos, Christos  and
      Chakraborty, Tanmoy  and
      Rose, Carolyn  and
      Peng, Violet",
    booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2025",
    address = "Suzhou, China",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-emnlp/2025.emnlp-main.575/",
    pages = "11422--11445",
    ISBN = "979-8-89176-332-6",
    abstract = "Counterfactual reasoning typically involves considering alternatives to actual events. While often applied to understand past events, a distinct form{---}forward counterfactual reasoning{---}focuses on anticipating plausible future developments. This type of reasoning is invaluable in dynamic financial markets, where anticipating market developments can powerfully unveil potential risks and opportunities for stakeholders, guiding their decision-making. However, performing this at scale is challenging due to the cognitive demands involved, underscoring the need for automated solutions. Large Language Models (LLMs) offer promise, but remain unexplored for this application. To address this gap, we introduce a novel benchmark, Fin-Force{---}**FIN**ancial **FOR**ward **C**ounterfactual **E**valuation. By curating financial news headlines and providing structured evaluation, Fin-Force supports LLM based forward counterfactual generation. This paves the way for scalable and automated solutions for exploring and anticipating future market developments, thereby providing structured insights for decision-making. Through experiments on Fin-Force, we evaluate state-of-the-art LLMs and counterfactual generation methods, analyzing their limitations and proposing insights for future research."
}