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Finance Templates

Financial document analysis and extraction.

Overview

Finance templates are optimized for extracting structured information from financial documents.

Templates

earnings_summary

Type: model

Purpose: Extract key metrics from earnings reports

Best for: - Quarterly earnings (10-Q) - Annual reports (10-K) - Earnings call transcripts

Fields:

Field Type Description
company_name str Company name
quarter str Fiscal quarter
revenue float Total revenue
net_income float Net income
eps float Earnings per share
yoy_growth float Year-over-year growth 
he parse 10q.md -t finance/earnings_summary -l en

ka = Template.create("finance/earnings_summary", "en")
result = ka.parse(earnings_text)

print(f"Revenue: ${result.data.revenue}B")
print(f"EPS: ${result.data.eps}")

ownership_graph

Type: graph

Purpose: Extract company ownership structures

Best for: - Shareholder reports - Proxy statements - Corporate structures

Entities: - Companies - Shareholders - Subsidiaries

Relations: - owns — Ownership relationships - controls — Control relationships - subsidiary_of — Subsidiary relationships

he parse proxy.md -t finance/ownership_graph -l en

ka = Template.create("finance/ownership_graph", "en")
result = ka.parse(proxy_statement)

# Build index for interactive search/chat in visualization
result.build_index()

result.show()  # Shows ownership network with interactive features

event_timeline

Type: temporal_graph

Purpose: Extract financial events with dates

Best for: - Corporate event histories - M&A timelines - Market events

Features:

  • Event dates
  • Event types (merger, acquisition, IPO, etc.)
  • Related entities
he parse history.md -t finance/event_timeline -l en

ka = Template.create("finance/event_timeline", "en")
result = ka.parse(history_text)

print(f"Events: {len(result.nodes)}")
for node in result.nodes:
    print(f"  - {node.name}")

risk_factor_set

Type: set

Purpose: Extract unique risk factors

Best for: - Risk factor sections - Due diligence reports - Risk assessments

he parse 10k.md -t finance/risk_factor_set -l en

ka = Template.create("finance/risk_factor_set", "en")
result = ka.parse(risk_section)

print(f"Found {len(result.data.items)} risk factors")
for risk in result.data.items:
    print(f"  - {risk}")

sentiment_model

Type: model

Purpose: Extract sentiment indicators

Best for: - News articles - Analyst reports - Social media sentiment

Fields:

Field Type Description
sentiment str Overall sentiment (positive/negative/neutral)
confidence float Confidence score
key_points list[str] Key sentiment indicators 
he parse article.md -t finance/sentiment_model -l en

ka = Template.create("finance/sentiment_model", "en")
result = ka.parse(article_text)

print(f"Sentiment: {result.data.sentiment}")
print(f"Confidence: {result.data.confidence}")
for point in result.data.key_points:
    print(f"  - {point}")

Use Cases

Use Case 1: Quarterly Report Analysis

from hyperextract import Template

# Extract earnings
ka = Template.create("finance/earnings_summary", "en")
q1 = ka.parse(q1_report)
q2 = ka.parse(q2_report)

# Compare
print(f"Q1 Revenue: ${q1.data.revenue}B")
print(f"Q2 Revenue: ${q2.data.revenue}B")
print(f"Growth: {(q2.data.revenue - q1.data.revenue) / q1.data.revenue * 100:.1f}%")

Use Case 2: Ownership Structure

ka = Template.create("finance/ownership_graph", "en")
ownership = ka.parse(proxy_statement)

# Find major shareholders
shareholders = [
    e for e in ownership.data.entities 
    if e.type == "shareholder"
]

for sh in sorted(shareholders, key=lambda x: x.stake, reverse=True)[:5]:
    print(f"{sh.name}: {sh.stake}%")

Use Case 3: Risk Assessment

ka = Template.create("finance/risk_factor_set", "en")
risk_factors = ka.parse(risk_section)

# Categorize risks
for risk in risk_factors.data.items:
    if "regulatory" in risk.lower():
        print(f"Regulatory Risk: {risk}")
    elif "market" in risk.lower():
        print(f"Market Risk: {risk}")

Data Sources

These templates work well with: - SEC filings (10-K, 10-Q, 8-K) - Earnings call transcripts - Investor presentations - Analyst reports - Financial news

Tips

  1. Use earnings_summary for quick metrics — Fast extraction of key numbers
  2. Use ownership_graph for structure — Visualize corporate hierarchies
  3. Use event_timeline for history — Track corporate events
  4. Combine templates — Use multiple templates for comprehensive analysis

See Also