Spinbris Bdc Analysis Bdc Xbrl Extraction

BDC Schedule of Investments Extraction

Extract structured portfolio data from Business Development Company SEC filings.

Overview

BDCs file a Consolidated Schedule of Investments in 10-K/10-Q containing:

• Debt Investments: Senior secured loans, subordinated debt, mezzanine, unitranche
• Equity Investments: Common stock, preferred stock, warrants, LLC/LP units

Data comes from SEC Reg S-X Article 12:

• Schedule 12-12: Investments in Securities of Unaffiliated Issuers
• Schedule 12-13: Investments in and Advances to Affiliates

All use Inline XBRL (required since Aug 2022) with the US GAAP Investment Company Taxonomy.

Validated Results

Tested and validated on 4 major BDCs (Jan 2025):

Critical Implementation Notes

1. Data is Split Across Multiple Statements

SOI data is NOT in one clean table. It's distributed across:

• Schedule of Investments → Interest rates, spreads, maturity dates
• Balance Sheet → Fair values
• Balance Sheet Parenthetical → Cost basis

Must query all statements and join by investment ID.

2. Investment IDs are in Context Dimensions

Investment identifiers are NOT in fact values. They're in XBRL context dimensions:

# WRONG - won't find IDs
facts_df['investment_id']  # Doesn't exist as column

# CORRECT - access via contexts
context_ref = fact['context_ref']
dimensions = xbrl.contexts[context_ref].dimensions
investment_id = dimensions.get('InvestmentIdentifierAxis')

3. Namespace Prefixes Vary - Always Normalize

Different BDCs use different namespace prefixes:

• us-gaap:InvestmentOwnedAtFairValue
• InvestmentOwnedAtFairValue (no prefix)
• arcc:CustomElement

Always strip namespace when matching:

def normalize_concept(concept: str) -> str:
    """Strip namespace prefix for consistent matching."""
    if not concept:
        return concept
    if ':' in concept:
        return concept.split(':', 1)[1]
    return concept

4. Classification Rate is 67-81%

Not all positions classify cleanly. Expect:

• 67-81% classified as Debt or Equity
• 19-33% unclassified (aggregate rollups, sector totals, etc.)

Unclassified positions are typically aggregate dimension members, not individual investments.

Output Structure

{TICKER}_10K_debt_investments.csv      # Loans with rate/maturity data
{TICKER}_10K_equity_investments.csv    # Equity with shares/ownership data  
{TICKER}_10K_affiliated_rollforward.csv # Affiliated investment activity
{TICKER}_10K_portfolio_summary.csv     # Totals by type, industry, affiliation

Quick Start

CLI Usage

python extract_soi.py ARCC --identity "Your Name your@email.com"
python extract_soi.py MAIN 10-Q --identity "Your Name your@email.com"
python extract_soi.py FSK 10-K 2023 --identity "Your Name your@email.com"

Python Usage

from edgar import Company, set_identity

set_identity("Your Name your@email.com")

company = Company("ARCC")
filing = company.get_filings(form="10-K").latest()
xbrl = filing.xbrl()

# Use the extraction functions from extract_soi.py
from extract_soi import extract_all_investments
debt_df, equity_df, affiliated_df, summary_df = extract_all_investments(xbrl)

Key XBRL Elements

Core Elements (Both Debt and Equity)

Debt-Specific Elements

Equity-Specific Elements

Extensible Enumerations

Investment ID Format Examples

Investment IDs from context dimensions vary by BDC:

# ARCC format
"Actfy Buyer, Inc., First lien senior secured loan"
"ABC Company, Class A common stock"

# MAIN format  
"XYZ Holdings LLC - Senior Secured Term Loan"
"Portfolio Co Inc - Common Equity"

# FSK format
"Company Name | First Lien Term Loan | SOFR+500"

Entity resolution will require fuzzy matching or normalization.

Debt vs Equity Classification

The script classifies using multiple signals:

1. Investment Type Keywords

   • Debt: loan, debt, note, bond, credit, mezzanine, senior, subordinated
   • Equity: stock, equity, share, warrant, unit, membership, preferred, common

2. Field Presence

   • Has Principal + no Shares → Debt
   • Has Shares + no Principal → Equity

3. Investment Type Axis (for totals)

   • DebtSecuritiesMember
   • EquitySecuritiesMember

Common Issues & Solutions

"No Schedule of Investments facts found"

Cause: Querying wrong concepts or not accessing contexts correctly

Solution:

1. Query all facts, filter by normalized concept name
2. Access investment IDs from xbrl.contexts[context_ref].dimensions

"Namespace prefix issues"

Cause: Concept has us-gaap: prefix in some filings, not others

Solution: Always use normalize_concept() to strip prefixes

"Low classification rate"

Cause: Aggregate dimension members (sector totals, rollups) aren't individual investments

Solution: This is expected. Focus on classified positions for analysis.

"Investment ID parsing"

Cause: ID formats vary widely across BDCs

Solution: For entity resolution/overlap analysis, will need:

• Fuzzy matching
• Company name normalization
• Possibly LEI/CIK lookup

Data Quality Expectations

Based on testing across 4 BDCs:

See Also

• xbrl-elements.md - Complete element reference
• taxonomy-dimensions.md - Dimensional modeling guide
• extract_soi.py - Working extraction script
• FASB Implementation Guide: https://xbrl.fasb.org/impguidance/BDC_TIG/

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