5. Report Data — Periodic Report Data

Deep analysis of non-financial data from periodic reports. Financial statements were covered in 2. Financial Statements; here we cover data provided by periodic report APIs — dividends, employees, shareholders, audit, and more.

Canonical access path: c.show("dividend"), c.show("audit"), etc. via show(topic) is the default. This tutorial also shows deep access patterns via the report namespace for detailed payloads.

• Dividend time series (DPS, dividend yield, payout ratio)
• Employee data (headcount, average salary, tenure)
• Largest shareholder and ownership structure
• Comprehensive shareholder overview
• Total shares and treasury stock
• Segment revenue
• Cost by nature breakdown
• Executive roster and compensation
• Audit opinion and audit fees
• Debt securities and subsidiary investments

Setup

import dartlab

c = dartlab.Company("005930")

Dividends

# canonical path
c.show("dividend")

# report namespace direct access
c.dividend
# year | netIncome | eps | totalDividend | payoutRatio | dividendYield | dps | dpsPreferred

View DPS, dividend yield, and payout ratio as a time series. Easily track payout ratio trends relative to net income.

# check latest dividend info
div = c.dividend
if div is not None:
    last = div.row(-1, named=True)
    print(f"DPS: {last['dps']} KRW")
    print(f"Dividend Yield: {last['dividendYield']}%")
    print(f"Payout Ratio: {last['payoutRatio']}%")

Employee Data

c.employee
# year | totalEmployees | avgTenure | totalSalary | avgSalary

Time series of total headcount, average tenure, total annual salary, and average salary per employee.

Sharp changes in headcount may signal business expansion/contraction or restructuring. Comparing average salary against industry levels provides insight into talent competitiveness.

Largest Shareholder

c.majorHolder
# year | majorHolder | majorRatio | totalRatio | holderCount | ...

Time series of the largest shareholder’s name and stake. For more detail, access via report.extract():

result = c.report.extract("majorHolder")

result.majorHolder   # "이재용"
result.majorRatio    # 20.76
result.totalRatio    # total related-party stake

# individual shareholder list
for h in result.holders:
    print(f"{h.name} ({h.relation}): {h.ratioEnd}%")

A very low largest-shareholder stake may indicate management control risk. A high total related-party stake suggests management stability but may raise minority shareholder concerns.

Comprehensive Shareholder Overview

Query 5%+ holders, minority shareholders, and voting rights comprehensively.

result = c.holderOverview

for bh in result.bigHolders:
    print(f"{bh.name}: {bh.ratio}%")

if result.minority:
    print(f"Minority shareholder ratio: {result.minority.ratio}%")

if result.voting:
    print(f"Exercisable voting shares: {result.voting.exercisableShares}")

Total Shares

c.shareCapital
# year | authorizedShares | issuedShares | treasuryShares | outstandingShares | ...

Time series of issued shares, treasury stock, and outstanding shares. Individual fields accessible via report.extract():

result = c.report.extract("shareCapital")
print(f"Issued shares: {result.issuedShares:,.0f}")
print(f"Treasury shares: {result.treasuryShares:,.0f}")
print(f"Treasury ratio: {result.treasuryRatio:.2%}")

A high treasury stock ratio means fewer shares in circulation, potentially impacting share price. Buyback and cancellation trends are also worth monitoring.

Segment Revenue

Extracts segment information from K-IFRS footnotes.

c.notes.segments
# segment revenue time-series DataFrame

For detailed year-by-year tables, use report.extract():

result = c.report.extract("segments")
print(result.revenue)   # segment revenue time series

for year, tables in result.tables.items():
    for t in tables:
        print(f"[{year}] {t.tableType}: {t.columns}")

Revenue concentrated in a single segment means high sensitivity to that segment’s business cycle. Segment diversification trends reveal business strategy.

Cost by Nature

Time series of cost items: raw materials, labor, depreciation, etc.

c.notes.costByNature
# cost-by-nature time-series DataFrame

Cost ratios and cross-validation also available:

result = c.report.extract("costByNature")
print(result.timeSeries)  # cost time series
print(result.ratios)      # composition ratios
print(result.crossCheck)  # cross-validation results

High raw material costs indicate commodity price sensitivity. High labor costs indicate a labor-intensive business.

Executive Roster

c.executive
# year | totalRegistered | insideDirectors | outsideDirectors | maleCount | femaleCount

Time series of registered executive composition. Tracks inside/outside director ratio, gender composition.

For unregistered executive compensation:

result = c.report.extract("executive")
print(result.executiveDf)   # registered executive time series
print(result.unregPayDf)    # unregistered executive compensation

Executive Compensation

c.executivePay
# year | category | headcount | totalPay | avgPay

Compensation time series by category. For individual compensation above 500M KRW:

result = c.report.extract("executivePay")
print(result.payByTypeDf)   # compensation by type
print(result.topPayDf)      # individuals above 500M KRW

Excessive executive compensation relative to performance is a governance risk signal. The compensation structure (base vs. bonus vs. stock) of top-paid executives is also worth examining.

Audit Opinion

c.audit
# year | auditor | opinion | keyAuditMatters

Time series of audit firm, opinion, and key audit matters. For audit fees:

result = c.report.extract("audit")
print(result.opinionDf)   # audit opinion time series
print(result.feeDf)        # audit fee time series

If the audit opinion is anything other than “Unqualified”, all other analysis results should be re-examined.

Debt Securities

c.bond
# year | totalIssuances | totalAmount | unredeemedCount

Time series of corporate bonds, commercial paper, and other debt securities.

For individual issuance details via report.extract():

result = c.report.extract("bond")
for b in result.issuances[:5]:
    print(f"{b.bondType} | {b.amount}M KRW | {b.interestRate}")

Subsidiary Investments

c.subsidiary
# year | totalCount | listedCount | unlistedCount | totalBook

For individual investee details via report.extract():

result = c.report.extract("subsidiary")
for inv in result.investments[:5]:
    print(f"{inv.name}: {inv.endRatio}%, book value {inv.endBook}")

Comparing Across Companies

The same report payload can be called repeatedly across companies for comparison.

import polars as pl

codes = ["005930", "000660", "035420"]
rows = []

for code in codes:
    c = dartlab.Company(code)
    div = c.dividend
    if div is not None:
        last = div.row(-1, named=True)
        rows.append({
            "Company": c.corpName,
            "DPS": last.get("dps"),
            "Dividend Yield": last.get("dividendYield"),
            "Payout Ratio": last.get("payoutRatio")
        })

print(pl.DataFrame(rows))

Next Steps

• 6. Disclosure Text — Business description, MD&A, company overview
• 7. Advanced Analysis — K-IFRS footnotes, tangible assets, cross-analysis