用大模型生成未来市场反事实场景，帮投资者提前发现风险与机会
Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation
- 构建金融新闻数据集，支持大模型生成未来可能发生的市场情景
- 在FIN-FORCE上测试主流模型，发现其生成的反事实内容可信度不足
- 适合金融分析、AI决策、量化研究等领域的研究人员和从业者
反事实推理通常用于理解过去事件，而前向反事实推理则聚焦于预测未来可能发生的情景。这种推理在动态金融市场中极为重要，能帮助利益相关方识别潜在风险与机遇，指导决策。然而，由于认知负荷高，大规模实施困难，亟需自动化解决方案。大语言模型（LLM）虽具潜力，但在此领域尚未得到充分探索。为此，我们提出一个新基准：FIN-FORCE（Financial FORward Counterfactual Evaluation），通过整理金融新闻标题并提供结构化评估标准，支持基于LLM的前向反事实生成。该基准使大规模、自动化的未来市场情景推演成为可能，为决策提供系统性洞察。我们在FIN-FORCE上对当前先进模型和生成方法进行了评估，揭示其局限性，并为未来研究提供方向。相关数据集、补充材料及代码已公开于 https://github.com/keanepotato/fin_force。
原文摘要 · Abstract (English)
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. LLMs offer promise, but remain unexplored for this application. To address this gap, we introduce a novel benchmark, FIN-FORCE-FINancial FORward Counterfactual Evaluation. 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. We release the benchmark, supplementary data and all experimental codes at the following link: https://github.com/keanepotato/fin_force
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