LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline

• URL: http://arxiv.org/abs/2512.07867v1
• Date: Wed, 26 Nov 2025 19:29:22 GMT
• Title: LLM-Generated Counterfactual Stress Scenarios for Portfolio Risk Simulation via Hybrid Prompt-RAG Pipeline
• Abstract summary: We develop a transparent and fully auditable LLM-based pipeline for macro-financial stress testing.<n>The system generates machine-readable macroeconomic scenarios for the G7, which cover GDP growth, inflation, and policy rates.<n>Across models, countries, and retrieval settings, the LLMs produce coherent and country-specific stress narratives.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We develop a transparent and fully auditable LLM-based pipeline for macro-financial stress testing, combining structured prompting with optional retrieval of country fundamentals and news. The system generates machine-readable macroeconomic scenarios for the G7, which cover GDP growth, inflation, and policy rates, and are translated into portfolio losses through a factor-based mapping that enables Value-at-Risk and Expected Shortfall assessment relative to classical econometric baselines. Across models, countries, and retrieval settings, the LLMs produce coherent and country-specific stress narratives, yielding stable tail-risk amplification with limited sensitivity to retrieval choices. Comprehensive plausibility checks, scenario diagnostics, and ANOVA-based variance decomposition show that risk variation is driven primarily by portfolio composition and prompt design rather than by the retrieval mechanism. The pipeline incorporates snapshotting, deterministic modes, and hash-verified artifacts to ensure reproducibility and auditability. Overall, the results demonstrate that LLM-generated macro scenarios, when paired with transparent structure and rigorous validation, can provide a scalable and interpretable complement to traditional stress-testing frameworks.

Related papers

• DeepBias: Adaptive In-depth Probing of Social Biases in LVLMs [71.85279836937578]
  We introduce DeepBias, an adaptive framework for the in-depth probing of social biases in LVLMs with carefully designed agents.<n>We build a benchmark named DeepBiasBench using our framework. By employing an ensemble of five diverse state-of-the-art LVLMs as anchors, the benchmark captures vulnerabilities shared across architectures.
  arXiv  Detail & Related papers  (2026-07-13T08:19:09Z)
• SHARC: SHAP-Based Interpretability in Machine Learning Risk Models for Regulatory Capital under ICAAP and CCAR [0.0]
  This paper describes an explainability framework for the Hybrid GPR-HS architecture and its stress-testing extension.<n>It decomposes Stressed Value-at-Risk into baseline, mean-driven, and volatility-driven components, enabling transparent linkage between scenario design and capital outcomes.<n>Results establish SHARC as a regulator-aligned explainability layer that makes the Hybrid GPR-HS framework fully auditable and consistent with FRTB, ICAAP Pillar 2, and CCAR transparency requirements.
  arXiv  Detail & Related papers  (2026-07-06T16:45:11Z)
• Joint Model and Data Sparsification via the Marginal Likelihood [53.29070892356214]
  We propose jointly learning individual feature and sample relevancies, enabling simultaneous model and data sparsification.<n>This symmetric pruning of model and data offers a natural extension that preserves conjugacy.<n> Empirical results across diverse regression tasks affirm that a joint ARD approach consistently yields both sparse and robust prediction models.
  arXiv  Detail & Related papers  (2026-05-28T13:26:53Z)
• STRIKE: Additive Feature-Group-Aware Stacking Framework for Credit Default Prediction [0.38233569758620045]
  STRIKE is a feature-group-aware stacking framework for structured credit risk data.<n>It partitions the feature space into semantically coherent groups and trains independent learners within each group.<n>It consistently outperforms strong tree-based baselines and conventional stacking approaches in terms of AUC-ROC.
  arXiv  Detail & Related papers  (2026-04-19T21:21:05Z)
• Safer Reasoning Traces: Measuring and Mitigating Chain-of-Thought Leakage in LLMs [5.834576254792341]
  Chain-of-Thought (CoT) prompting can increase privacy risk by resurfacing personally identifiable information (PII) from the prompt into reasoning traces and outputs.<n>We study such direct, inference-time PII leakage using a model-agnostic framework.
  arXiv  Detail & Related papers  (2026-03-05T19:20:44Z)
• ERP-RiskBench: Leakage-Safe Ensemble Learning for Financial Risk [0.0]
  This paper presents a rebuilt experimental framework for ERP financial risk detection using ensemble machine learning.<n>The risk definition is hybrid, covering both procurement compliance anomalies and transactional fraud.<n>A composite benchmark called ERP-RiskBench is assembled from public procurement event logs, labeled fraud data, and a new synthetic ERP dataset.
  arXiv  Detail & Related papers  (2026-03-03T04:41:26Z)
• Rethinking the Trust Region in LLM Reinforcement Learning [72.25890308541334]
  Proximal Policy Optimization (PPO) serves as the de facto standard algorithm for Large Language Models (LLMs)<n>We propose Divergence Proximal Policy Optimization (DPPO), which substitutes clipping with a more principled constraint.<n>DPPO achieves superior training and efficiency compared to existing methods, offering a more robust foundation for RL-based fine-tuning.
  arXiv  Detail & Related papers  (2026-02-04T18:59:04Z)
• Task-Awareness Improves LLM Generations and Uncertainty [48.857040212979484]
  Bayes-optimal responses consistently outperform standard decoding methods like beam search.<n>Our decision-theoretic framework is applicable to any problem that admits a latent response structure.
  arXiv  Detail & Related papers  (2026-01-29T10:16:23Z)
• Robust Machine Learning for Regulatory Sequence Modeling under Biological and Technical Distribution Shifts [0.3948325938742681]
  We introduce a robustness framework to quantify performance degradation, calibration failures, and uncertainty based reliability.<n>In simulation, motif driven regulatory outputs are generated with cell type specific programs, perturbations, GC bias, depth variation, batch effects, and heteroscedastic noise.<n>Models remain accurate but show higher error, severe variance miscalibration, and coverage collapse under motif effect rewiring and noise dominated regimes.
  arXiv  Detail & Related papers  (2026-01-21T13:15:27Z)
• Towards Comprehensive Stage-wise Benchmarking of Large Language Models in Fact-Checking [64.97768177044355]
  Large Language Models (LLMs) are increasingly deployed in real-world fact-checking systems.<n>We present FactArena, a fully automated arena-style evaluation framework.<n>Our analyses reveal significant discrepancies between static claim-verification accuracy and end-to-end fact-checking competence.
  arXiv  Detail & Related papers  (2026-01-06T02:51:56Z)
• 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)
• Tractable Asymmetric Verification for Large Language Models via Deterministic Replicability [0.6117371161379209]
  The landscape of Large Language Models (LLMs) shifts rapidly towards dynamic, multi-agent systems.<n>This paper proposes a verification framework that achieves tractable asymmetric effort.<n>We show that targeted verification can be over 12 times faster than full regeneration.
  arXiv  Detail & Related papers  (2025-09-14T03:30:06Z)
• Conformal P-Value in Multiple-Choice Question Answering Tasks with Provable Risk Control [0.0]
  This study introduces a significance testing-enhanced conformal prediction (CP) framework to improve trustworthiness of large language models (LLMs) in multiple-choice question answering (MCQA)<n>CP provides statistically rigorous marginal coverage guarantees for prediction sets, and significance testing offers established statistical rigor.<n>This work establishes a statistical framework for trustworthy LLM deployment in high-stakes QA applications.
  arXiv  Detail & Related papers  (2025-08-07T16:46:47Z)
• Data-Driven Calibration of Prediction Sets in Large Vision-Language Models Based on Inductive Conformal Prediction [0.0]
  We propose a model-agnostic uncertainty quantification method that integrates dynamic threshold calibration and cross-modal consistency verification.<n>We show that the framework achieves stable performance across varying calibration-to-test split ratios, underscoring its robustness for real-world deployment in healthcare, autonomous systems, and other safety-sensitive domains.<n>This work bridges the gap between theoretical reliability and practical applicability in multi-modal AI systems, offering a scalable solution for hallucination detection and uncertainty-aware decision-making.
  arXiv  Detail & Related papers  (2025-04-24T15:39:46Z)
• Retrieval is Not Enough: Enhancing RAG Reasoning through Test-Time Critique and Optimization [58.390885294401066]
  Retrieval-augmented generation (RAG) has become a widely adopted paradigm for enabling knowledge-grounded large language models (LLMs)<n>RAG pipelines often fail to ensure that model reasoning remains consistent with the evidence retrieved, leading to factual inconsistencies or unsupported conclusions.<n>We propose AlignRAG, a novel iterative framework grounded in Critique-Driven Alignment (CDA)<n>We introduce AlignRAG-auto, an autonomous variant that dynamically terminates refinement, removing the need to pre-specify the number of critique iterations.
  arXiv  Detail & Related papers  (2025-04-21T04:56:47Z)
• Risk-Averse Certification of Bayesian Neural Networks [70.44969603471903]
  We propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN.<n>Our method leverages sampling and optimisation to compute a sound approximation of the output set of a BNN.<n>We validate RAC-BNN on a range of regression and classification benchmarks and compare its performance with a state-of-the-art method.
  arXiv  Detail & Related papers  (2024-11-29T14:22:51Z)
• OCEAN: Offline Chain-of-thought Evaluation and Alignment in Large Language Models [68.17018458283651]
  This work focuses on the offline evaluation of the chain-of-thought capabilities of LLMs. We use knowledge graphs (e.g., Wikidata5m) to provide feedback on the generated chain of thoughts. We show how to optimize LLMs based on the proposed evaluation method.
  arXiv  Detail & Related papers  (2024-10-31T07:48:44Z)
• A Robustness Analysis of Blind Source Separation [91.3755431537592]
  Blind source separation (BSS) aims to recover an unobserved signal from its mixture $X=f(S)$ under the condition that the transformation $f$ is invertible but unknown. We present a general framework for analysing such violations and quantifying their impact on the blind recovery of $S$ from $X$. We show that a generic BSS-solution in response to general deviations from its defining structural assumptions can be profitably analysed in the form of explicit continuity guarantees.
  arXiv  Detail & Related papers  (2023-03-17T16:30:51Z)
• When Demonstrations Meet Generative World Models: A Maximum Likelihood Framework for Offline Inverse Reinforcement Learning [62.00672284480755]
  This paper aims to recover the structure of rewards and environment dynamics that underlie observed actions in a fixed, finite set of demonstrations from an expert agent. Accurate models of expertise in executing a task has applications in safety-sensitive applications such as clinical decision making and autonomous driving.
  arXiv  Detail & Related papers  (2023-02-15T04:14:20Z)

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.