Evaluating LLMs in Finance Requires Explicit Bias Consideration

Paper: arXiv 2602.14233

Authors: Yaxuan Kong, Hoyoung Lee, Yoontae Hwang, Alejandro Lopez-Lira, Bradford Levy, Dhagash Mehta, Qingsong Wen, Chanyeol Choi, Yongjae Lee, Stefan Zohren

Abstract

Large Language Models (LLMs) are increasingly integrated into financial workflows, but evaluation practice has not kept up. Finance-specific biases can inflate performance, contaminate backtests, and make reported results useless for any deployment claim. We identify five recurring biases in financial LLM applications. They include look-ahead bias, survivorship bias, narrative bias, objective bias, and cost bias. These biases break financial tasks in distinct ways and they often compound to create an illusion of validity. We reviewed 164 papers from 2023 to 2025 and found that no single bias is discussed in more than 28 percent of studies. This position paper argues that bias in financial LLM systems requires explicit attention and that structural validity should be enforced before any result is used to support a deployment claim. We propose a Structural Validity Framework and an evaluation checklist with minimal requirements for bias diagnosis and future system design. The material is available at https://github.com/Eleanorkong/Awesome-Financial-LLM-Bias-Mitigation.

Complexity vs Empirical Score

• Math Complexity: 2.0/10
• Empirical Rigor: 6.5/10
• Quadrant: Street Traders — practical and empirical, lighter on theory

Why this score: This position paper systematically identifies five core evaluation biases in financial LLM research based on a extensive literature review of 164 papers and a practitioner survey. While light on deep mathematical formalisms, it provides high empirical rigor via meta-analysis, robust qualitative frameworks, and actionable reproducibility artifacts (GitHub repository and evaluation checklists).

Research Flowchart

  flowchart TD
    A["Research Goal:<br>Evaluate Finance-Specific Biases in LLMs"] --> B["Data Input:<br>164 Financial LLM Papers (2023–2025)"]
    B --> C["Literature Review & Analysis"]
    C --> D["Identify 5 Core Biases:<br>Look-ahead, Survivorship, Narrative, Objective, Cost"]
    D --> E["Key Finding:<br>No single bias addressed in >28% of papers"]
    E --> F["Outcomes:<br>Structural Validity Framework & Evaluation Checklist"]

Cited References (76)

1. Lookahead bias in pretrained language models (2024)
2. Time Travel is Cheating: Going Live with DeepFund for Real-Time Fund Investment Benchmarking (2025) — link
3. Time travel in llms: Tracing data contamination in large language models (2023) — link
4. The Memorization Problem: Can We Trust LLMs’ Economic Forecasts? — → 2504.14765
5. A Test of Lookahead Bias in LLM Forecasts — → 2512.23847
6. Can LLM-based Financial Investing Strategies Outperform the Market in Long Run? — → 2505.07078
7. Artificial Hivemind: The Open-Ended Homogeneity of Language Models (and Beyond) (2025) — link
8. Consistency checks for language model forecasters (2024) — link

9 of 76 cited works are in the corpus; resolved via arXiv source bundles, Semantic Scholar and OpenAlex. Corpus-wide: 2518 linked papers.