Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents

• URL: http://arxiv.org/abs/2510.07920v1
• Date: Thu, 09 Oct 2025 08:13:35 GMT
• Title: Profit Mirage: Revisiting Information Leakage in LLM-based Financial Agents
• Abstract summary: LLM-based financial agents have attracted widespread excitement for their ability to trade like human experts.<n>Most systems exhibit a "profit mirage": dazzling back-tested returns evaporate once the model's knowledge window ends.<n>We introduce FactFin, a framework that applies counterfactual perturbations to compel LLM-based agents to learn causal drivers instead of memorized outcomes.
• Score: 40.636918662488505
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: LLM-based financial agents have attracted widespread excitement for their ability to trade like human experts. However, most systems exhibit a "profit mirage": dazzling back-tested returns evaporate once the model's knowledge window ends, because of the inherent information leakage in LLMs. In this paper, we systematically quantify this leakage issue across four dimensions and release FinLake-Bench, a leakage-robust evaluation benchmark. Furthermore, to mitigate this issue, we introduce FactFin, a framework that applies counterfactual perturbations to compel LLM-based agents to learn causal drivers instead of memorized outcomes. FactFin integrates four core components: Strategy Code Generator, Retrieval-Augmented Generation, Monte Carlo Tree Search, and Counterfactual Simulator. Extensive experiments show that our method surpasses all baselines in out-of-sample generalization, delivering superior risk-adjusted performance.

Related papers

• The Price of Agreement: Measuring LLM Sycophancy in Agentic Financial Applications [5.019664088524701]
  We focus on evaluating sycophancy that LLMs display in agentic financial tasks.<n>Our findings are three-fold: first, we find the models show only low to modest drops in performance in the face of user rebuttals or contradictions to the reference answer.<n>Second, we introduce a suite of tasks to test for sycophancy by user preference information that contradicts the reference answer and find that most models fail in the presence of such inputs.
  arXiv  Detail & Related papers  (2026-04-27T16:27:10Z)
• Integrating Domain Knowledge for Financial QA: A Multi-Retriever RAG Approach with LLMs [13.368251290146794]
  We implement a multi-retriever Retrieval Augmented Generators system to retrieve both external domain knowledge and internal question contexts.<n>We find that domain-specific training with the SecBERT encoder significantly contributes to our best neural symbolic model.
  arXiv  Detail & Related papers  (2025-12-29T20:24:15Z)
• Interpreting LLMs as Credit Risk Classifiers: Do Their Feature Explanations Align with Classical ML? [4.0057196015831495]
  Large Language Models (LLMs) are increasingly explored as flexible alternatives to classical machine learning models for classification tasks through zero-shot prompting.<n>This study conducts a systematic comparison between zero-shot LLM-based classifiers and LightGBM, a state-of-the-art gradient-boosting model, on a real-world loan default prediction task.<n>We evaluate their predictive performance, analyze feature attributions using SHAP, and assess the reliability of LLM-generated self-explanations.
  arXiv  Detail & Related papers  (2025-10-29T17:05:00Z)
• Standard Benchmarks Fail - Auditing LLM Agents in Finance Must Prioritize Risk [31.43947127076459]
  Standard benchmarks fixate on how well large language model (LLM) agents perform in finance, yet say little about whether they are safe to deploy.<n>We argue that accuracy metrics and return-based scores provide an illusion of reliability, overlooking vulnerabilities such as hallucinated facts, stale data, and adversarial prompt manipulation.
  arXiv  Detail & Related papers  (2025-02-21T12:56:15Z)
• Towards Fully Exploiting LLM Internal States to Enhance Knowledge Boundary Perception [58.62352010928591]
  Large language models (LLMs) exhibit impressive performance across diverse tasks but often struggle to accurately gauge their knowledge boundaries.<n>This paper explores leveraging LLMs' internal states to enhance their perception of knowledge boundaries from efficiency and risk perspectives.
  arXiv  Detail & Related papers  (2025-02-17T11:11:09Z)
• LLM-Lasso: A Robust Framework for Domain-Informed Feature Selection and Regularization [59.75242204923353]
  We introduce LLM-Lasso, a framework that leverages large language models (LLMs) to guide feature selection in Lasso regression.<n>LLMs generate penalty factors for each feature, which are converted into weights for the Lasso penalty using a simple, tunable model.<n>Features identified as more relevant by the LLM receive lower penalties, increasing their likelihood of being retained in the final model.
  arXiv  Detail & Related papers  (2025-02-15T02:55:22Z)
• Leveraging Online Olympiad-Level Math Problems for LLMs Training and Contamination-Resistant Evaluation [55.21013307734612]
  AoPS-Instruct is a dataset of more than 600,000 high-quality QA pairs.<n>LiveAoPSBench is an evolving evaluation set with timestamps, derived from the latest forum data.<n>Our work presents a scalable approach to creating and maintaining large-scale, high-quality datasets for advanced math reasoning.
  arXiv  Detail & Related papers  (2025-01-24T06:39:38Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• Assessing the Reliability of Large Language Model Knowledge [78.38870272050106]
  Large language models (LLMs) have been treated as knowledge bases due to their strong performance in knowledge probing tasks. How do we evaluate the capabilities of LLMs to consistently produce factually correct answers? We propose MOdel kNowledge relIabiliTy scORe (MONITOR), a novel metric designed to directly measure LLMs' factual reliability.
  arXiv  Detail & Related papers  (2023-10-15T12:40:30Z)
• Enhancing Financial Sentiment Analysis via Retrieval Augmented Large Language Models [11.154814189699735]
  Large Language Models (LLMs) pre-trained on extensive corpora have demonstrated superior performance across various NLP tasks. We introduce a retrieval-augmented LLMs framework for financial sentiment analysis. Our approach achieves 15% to 48% performance gain in accuracy and F1 score.
  arXiv  Detail & Related papers  (2023-10-06T05:40:23Z)
• Assessing Hidden Risks of LLMs: An Empirical Study on Robustness, Consistency, and Credibility [37.682136465784254]
  We conduct over a million queries to the mainstream large language models (LLMs) including ChatGPT, LLaMA, and OPT. We find that ChatGPT is still capable to yield the correct answer even when the input is polluted at an extreme level. We propose a novel index associated with a dataset that roughly decides the feasibility of using such data for LLM-involved evaluation.
  arXiv  Detail & Related papers  (2023-05-15T15:44:51Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.