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

TL;DR

This paper introduces a new benchmark for evaluating large language models in generating forward counterfactuals in financial markets, aiming to automate and improve future market insight predictions.

Contribution

It presents FIN-FORCE, a novel benchmark dataset for forward counterfactual generation in finance, and evaluates current LLMs, highlighting their limitations and guiding future research.

Findings

State-of-the-art LLMs show limitations in forward counterfactual generation.

The benchmark enables systematic evaluation of models' ability to predict future market scenarios.

Insights are provided for improving LLMs in financial forward reasoning.

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