AI Economist Agent: An Agentic Framework for Evidence-Based Economic and Financial Analysis with RAG, Knowledge Graphs, and Large Language Models

Battle Count: 50

The paper is moderately relevant to quantitative trading. It provides a framework for macro-financial scenario generation and bank capital analysis that could inform risk management and portfolio stress testing. The VAR-based scenario generation, severity scoring, and transmission channel identification are directly applicable to macro-driven trading strategies and risk assessment. However, the paper focuses on regulatory stress testing and economic analysis rather than direct trading signal generation or execution. The knowledge graph approach to economic relations could support factor-based strategies, and the acceptance test framework could be adapted for trading model validation. The framework's emphasis on evidence traceability and model-based calculation (rather than LLM-generated numbers) aligns with quantitative trading's need for reproducible, auditable analysis.

The implementation is highly complex, involving: (1) multi-agent orchestration with 7 specialized agents (coordination, evidence retrieval, model requests, execution, testing, consistency checking, report generation); (2) knowledge graph construction with controlled vocabularies, evidence classes, and source tracking; (3) hybrid retrieval combining BM25, semantic similarity, and GraphRAG with path enumeration; (4) registered quantitative model execution with compatibility checking; (5) TDD-inspired acceptance tests at every interface; (6) strict numerical token resolution with hash verification; (7) append-only execution records with full reproducibility metadata; (8) temporal data management with publication dates, vintages, and eligibility rules; (9) multiple graph construction conditions (G0-G5); (10) bank capital accounting recursions with external validation thresholds. The system requires careful coordination between LLM agents, retrieval systems, quantitative models, and validation infrastructure.

The paper provides extensive reproducibility materials including: source manifests with SHA-256 hashes, code, fixed configurations, saved API records (372 LLM calls with full payloads, responses, parsed outputs, token counts, hashes, and costs), numerical outputs, notebooks, and tests. Model run records retain requests and input versions. Agent records include attempted calls and termination reasons. The LLM model identifier is pinned (gpt-4o-2024-08-06), temperature is set to zero, and strict JSON schemas are used. All prompts, inputs, and configurations are frozen. The paper explicitly states that replication materials are included. However, some planned numerical comparisons using dated data rules are noted as not yet reported.