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

Created by
  • Haebom

Author

Keane Ong, Rui Mao, Deeksha Varshney, Paul Pu Liang, Erik Cambria, Gianmarco Mengaldo

Outline

This paper focuses on "forward counterfactual inference," a technique used to predict future market developments, and addresses the need for automated solutions to address the challenges of large-scale application. To this end, we introduce FIN-FORCE (FINancial FORWARD Counterfactual Evaluation), a novel benchmark that supports LLM-based forward counterfactual generation from financial news headlines. Through experiments utilizing FIN-FORCE, we evaluate the performance of state-of-the-art LLM and counterfactual generation methods, analyze their limitations, and suggest future research directions.

Takeaways, Limitations

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Takeaways:
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It highlights the importance of “forward counterfactual reasoning” in predicting risks and opportunities in dynamic financial markets.
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To address the challenges of large-scale implementation, we present the potential of LLM-based automated solutions.
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We provide an evaluation framework for LLM-based forward counterfactual generation using the FIN-FORCE benchmark.
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Through experiments, we evaluate the performance of LLM and counterfactual generation methods and suggest directions for improvement.
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Limitations:
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While it identifies Limitations of LLM-based solutions and provides insights for future research, it may not provide details on specific problem-solving methods or performance improvements.
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The presented benchmarks and experimental results may be limited to specific LLM models and data, and further research is needed to determine their generalizability.
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Given the complexity of financial market forecasting, benchmarks may not perfectly reflect actual market conditions.
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