Overview
The LLM Financial Research Assistant converts financial metrics, company filings, news, and valuation data into structured equity-research memos. The project focuses as much on output validation as generation, with explicit checks for weak evidence, unsupported claims, and inconsistent reasoning.
Problem
LLMs can accelerate financial research, but fluent output can hide hallucinations, stale information, weak sourcing, repetition, or contradictions. A useful research assistant therefore needs a disciplined evidence and validation workflow rather than a single prompt that produces an answer.
Dataset / Inputs
- Financial statements and valuation metrics
- Company filings
- Market and company news
- Structured source metadata
- Research prompts and adversarial test cases
System Architecture
- Data collection and normalization layer
- Structured memo-generation workflow
- Source-grounding and claim-validation checks
- Output-quality evaluation for repetition, inconsistency, and weak evidence
- Deterministic fallback paths when source coverage or model confidence is insufficient
Methodology
Outputs are evaluated for hallucinations, unsupported claims, source quality, repetition, and internal inconsistencies. Prompt and data failure modes are tested deliberately, and deterministic fallbacks are used where a generated conclusion cannot be supported reliably.
What I Built
- Structured equity-research memo workflow
- Multi-source financial context assembly
- Hallucination and unsupported-claim evaluation
- Source-quality and consistency checks
- Deterministic fallback logic for incomplete or unreliable outputs
Results / Proof Points
- Converts multiple financial information types into a consistent research structure
- Evaluates generated conclusions against their supporting evidence
- Surfaces weak sourcing, repetition, and contradictions for review
- Treats failure analysis as part of the product rather than an afterthought
Business Value
The system demonstrates how generative AI can support research without obscuring uncertainty. It reduces the time needed to organize evidence while keeping unsupported conclusions visible to the analyst.
Limitations
The assistant supports research and review; it does not replace professional investment judgment. Output quality remains dependent on source coverage, data freshness, and the effectiveness of validation rules.
Next Steps
- Finalize a repeatable evaluation dataset and scoring rubric
- Add citation-level traceability throughout generated memos
- Expand deterministic financial calculations
- Publish the project report and implementation evidence