financial-statement-analysis

name	financial-statement-analysis
scope	co-consult
description	Comprehensive Korean financial statement analysis pipeline. Collects DART data, validates accounting integrity, normalizes to canonical financial model, extracts KPIs (profitability, growth, leverage, cash flow), builds a 5+ level ROIC value driver tree, and generates a structured Markdown report. Use when: "financial statement analysis", "ROIC analysis", "value driver tree", "financial KPI", "profitability analysis", "cash flow analysis", "fundamental analysis", "DART analysis", "financial metrics extraction", "corporate financial analysis".
status	active
owner	data-analyst
version	1.0.0
last_reviewed	"2026-07-19T00:00:00.000Z"
prerequisites	["k-dart"]
metadata	{"type":"analysis","tier":"medium","triggers":["financial statement analysis","ROIC analysis","value driver tree","financial KPI","profitability analysis","cash flow analysis","DART analysis","fundamental analysis","corporate financial analysis","financial metrics extraction"]}

Financial Statement Analysis Workflow

Context

End-to-end financial statement analysis pipeline for Korean listed companies. Uses DART (Financial Supervisory Service electronic disclosure system) data to produce validated, normalized financial analysis with ROIC-based value driver decomposition.

Architecture: Bun (TypeScript) orchestration + Python pandas data processing. This hybrid approach complies with the project's Computational Integrity policy (Class A financial calculations must use validated external tools).

Owner Agent: data-analyst (dispatched by PM)

When to Use

• A consulting engagement requires ROIC, profitability, growth, leverage, or cash-flow analysis of a Korean listed company
• The client or engagement lead asks for a "financial statement analysis", "fundamental analysis", or "value driver tree"
• data-analyst needs validated, normalized financial data before building a business case in financial-modeling
• Do NOT use for non-Korean companies (no DART coverage) or for engagements needing only qualitative company research — use company-intelligence instead

Prerequisites

• k-dart skill — for DART data collection (Phase ①)
• Python 3 with pandas installed — for data processing (Phases ②–⑤)
• Bun runtime — for orchestration scripts

Pipeline Stages

① DART Collection (k-dart)
    ↓
② Validation Engine (python/validate.py)
    ↓
③ Normalization (python/normalize.py)
    ↓
④ KPI Extraction (python/kpi.py)
    ↓
⑤ ROIC Value Driver Tree (python/driver_tree.py)
    ↓
⑥ Report Generation (scripts/financial-report.ts)

① DART Data Collection

Use the k-dart skill to collect raw financial data via DART OpenAPI fnlttSinglAcntAll. Output: deliverables/<company>/dart/<company-name>-<corp_code>-<start>-<end>.json

Required parameters:

• Company name or ticker
• Corp code (8-digit DART code)
• Year range (e.g., 2020–2025)
• fs_div: CFS (consolidated) preferred, OFS (separate) as fallback
• reprt_code: 11011 (annual business report)

② Validation Engine

Validates raw DART data for accounting integrity.

Rules:

Rule	Description	Threshold
Accounting Equation	Total Assets = Total Liabilities + Total Equity	Residual < 5B KRW
Cash Flow Reconciliation	Beginning Cash + OCF + ICF + FCF = Ending Cash	Residual < 1B KRW
NI → Retained Earnings	Retained earnings increase ≈ net income	20% tolerance
Anomaly Detection	Flag YoY changes > 200% or sign flips	Base > 1B KRW

Runner: bun scripts/co-consult/financial-validate.ts <dart.json> --output <path>

③ Normalization

Converts Korean DART account names to a Canonical Financial Model using industry-specific mapping tables.

Mapping table: python/mappings/ifrs_general.json

Canonical fields (36 per year):

• Income: revenue, cogs, gross_profit, sg_and_a, rd_expense, depreciation, operating_income, ebit, ebt, nopat, tax_expense, net_income, fin_expense, fin_income
• Balance Sheet: total_assets, current_assets, cash, receivables, inventory, non_current_assets, ppe, intangible_assets, total_liabilities, current_liabilities, st_debt, lt_debt, lease_liabilities, right_of_use_assets, total_equity, retained_earnings
• Derived: invested_capital, working_capital
• Cash Flow: operating_cf, investing_cf, financing_cf, free_cash_flow, dividends_paid
• Cross-period: prev_revenue

Runner: bun scripts/co-consult/financial-normalize.ts <dart.json> --mapping <path> --output <path>

④ KPI Extraction

Computes financial KPIs from the canonical model.

KPI Groups:

Group	KPIs
Profitability	Gross Margin, Operating Margin, EBITDA Margin, Net Margin, ROE, ROA, ROIC
Growth	Revenue YoY, Operating Income YoY, Net Income YoY, Revenue 3yr CAGR
Leverage & Liquidity	D/E, D/A, Equity Ratio, Current Ratio, Quick Ratio, Interest Coverage, Net Debt
Cash Flow	OCF Margin, FCF, OCF/Debt, Dividend Payout, Cash Ratio, Asset Turnover, Receivables Turnover, Fixed Asset Turnover
Efficiency	COGS/Revenue, SG&A/Revenue, R&D/Revenue, Depreciation/PPE

Runner: bun scripts/co-consult/financial-kpi.ts <canonical.json> --output <path>

⑤ ROIC Value Driver Tree

Builds a 5+ level decomposition of ROIC into its operational drivers.

Tree structure:

ROIC (L0)
├── NOPAT (L1)
│   ├── Revenue (L2) → Growth Drivers (L3) → YoY, CAGR (L4)
│   ├── Operating Margin (L2) → Gross/SGA/R&D/Depreciation (L3) → Detail (L4)
│   └── Tax Efficiency (L2) → Effective Rate, NOPAT Margin (L3)
└── Invested Capital (L1)
    ├── Working Capital (L2) → Receivables/Inventory/Payables Days (L3) → Ratios (L4)
    ├── Net Fixed Assets (L2) → PPE/Revenue, Capex (L3) → Turnover (L4)
    ├── Intangible Assets (L2) → Intangibles/Revenue, YoY (L3)
    └── Cash & Debt (L2) → Net Cash, D/E (L3)

Each node contains: value, YoY change, weight (contribution), comment (AI-filled).

Runner: bun scripts/co-consult/financial-driver-tree.ts <canonical.json> --output <path>

⑥ Report Generation

Generates a structured Markdown report from all pipeline outputs.

Sections:

1. Executive Summary
2. Financial Highlights
3. Profitability Analysis (with Returns)
4. Growth Analysis
5. Leverage & Liquidity
6. Cash Flow Analysis
7. ROIC & Value Driver Tree
8. Investment View (AI agent synthesizes)
9. Appendix A: Validation Summary
10. Appendix B: Data Coverage

Runner: bun scripts/co-consult/financial-report.ts <canonical.json> <validation.json> <kpi.json> <tree.json> --output <path>

End-to-End Pipeline

Run all stages in sequence:

bun scripts/co-consult/financial-pipeline.ts <dart.json> --company <name> --output-dir <dir>

This creates the full output structure:

<output-dir>/
├── dart/dart-YYYY-MM-DD.json
├── validation/validation-report-YYYY-MM-DD.json
├── canonical/canonical-model-YYYY-MM-DD.json
├── kpi/kpi-report-YYYY-MM-DD.json
├── driver-tree/driver-tree-YYYY-MM-DD.json
└── reports/financial-analysis-<company>-YYYY-MM-DD.md

Execution Steps

(also referred to as the Execution Protocol for the data-analyst agent)

1. Receive dispatch from PM with company name and year range
2. Collect data using k-dart skill (or use existing data if available)
3. Run pipeline:

   bun scripts/co-consult/financial-pipeline.ts <dart-file> --company <name>
   

4. Review outputs — check validation pass rate (>90% acceptable), coverage (>80% acceptable)
5. Synthesize Investment View — fill section 8 of the report with analytical insights
6. Hand off to communications-lead or strategy-analyst as appropriate

Output Format

The pipeline emits one Markdown report (see §⑥ Report Generation for the section list) plus the intermediate JSON artifacts it was built from:

Output	Destination	Format
Raw DART data	deliverables/<company>/dart/	JSON
Validation report	deliverables/<company>/validation/	JSON
Canonical model	deliverables/<company>/canonical/	JSON
KPI report	deliverables/<company>/kpi/	JSON
Driver tree	deliverables/<company>/driver-tree/	JSON
Final report	deliverables/reports/	Markdown

Error Handling

Stage	Failure Mode	Recovery
② Validation	Pass rate < 80%	Flag data quality issues, proceed with warnings
③ Normalization	Coverage < 60%	Check industry mapping, may need custom mapping
④ KPI	Missing key fields	Report which KPIs are unavailable due to missing inputs
⑤ Driver Tree	NOPAT/IC unavailable	Report tree with null nodes and notes

Related Skills

• k-dart — DART data collection (prerequisite)
• financial-modeling — Consulting ROI/NPV business cases (complementary)
• company-intelligence — Broader company research (upstream)

Notes

• All financial computations are performed by Python pandas (Class A Computational Integrity compliance)
• A NodeJS-Polars validation engine was considered as an alternative to python/validate.py and explicitly rejected — the pipeline keeps the Bun/TypeScript orchestration + Python/pandas computation split as-is
• The AI agent must NOT calculate financial figures directly
• Korean DART account names vary by company; the normalization mapping absorbs this variation
• The pipeline is loosely coupled — each stage can run independently with JSON I/O
• Future expansion: SEC EDGAR (US), EDINET (Japan) support via new mapping tables only