Deriving Strategic Market Insights with Large Language Models: A Benchmark for Forward Counterfactual Generation

TL;DR

This work introduces Fin-Force, a benchmark for forward counterfactual generation in finance, enabling scalable exploration of plausible future market developments from current news headlines. It defines two counterfactual types (opportunity and risk), curates 1368 headlines with a rigorous annotation protocol, and designs forward-looking metrics—Forward-Compatibility and Directionality—while evaluating zero-/few-shot prompting, SOTA counterfactual methods, and a self-training paradigm (SRLM). Findings show sampling-based counterfactuals and self-training yield the strongest performance, with smaller LLMs becoming competitive contenders, and highlight limitations of existing prompting-based approaches. The authors provide a public benchmark, supplementary data, and code to spur future research and practical, automated scenario analysis in finance, with potential extensions to multilingual data and explainability-driven applications.

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. 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

Figures (5)

• Figure 1: Overview of Fin-Force task. Given a financial news headline depicting a market event, an LLM is tasked with generating two forward counterfactuals - an opportunity counterfactual and a risk counterfactual. While the opportunity counterfactual explores how the event can positively shift, the risk counterfactual highlights potential adverse scenarios.
• Figure 2: Directionality (Dir.) scores between risk and opportunity counterfactuals for different baseline LLM prompting methods under zero and few-shot settings.
• Figure 3: Absolute performance changes with few-shot relative to zero-shot prompting for different LLMs. ✓ indicates improvement; ✗ indicates degradation.
• Figure 4: Analysis of prominent error cases. H represents a headline in Fin-Force; denotes the errorneous LLM response; CF stands for counterfactual.
• Figure 5: Performance across different iterations of training in the SRLM self-training paradigm. Iter. stands for training iteration.