Portfolio Company Intelligence: Beyond Fund-Level Tracking

The Limitation of Fund-Level Views

Traditional portfolio management: Track 40 funds, see aggregate NAV, hope for the best.

The problem: You're flying blind on the underlying companies.

• Which portfolio companies are most likely to exit?
• Which vintage years have higher failure rates?
• Are early-stage cohorts maturing as expected?
• What's the competing risk profile? (Exit vs failure vs acquisition)

Nagare now tracks portfolio companies, not just funds.

What is Competing Risks Analysis?

A portfolio company faces multiple "competing" outcomes:

1. Exit (IPO, trade sale): You make money
2. Failure (bankruptcy, wind-down): You lose money
3. Acquisition (M&A): Partial liquidity
4. Zombie (no exit, no failure): Capital trapped

Traditional tools assume one outcome. Competing risks models all possibilities simultaneously.

Example: 2019 Vintage VC Cohort

100 companies, vintage 2019 (now 6 years old):

Exits: 12 (12%) → $45M realized
Failures: 28 (28%) → -$18M loss
Acquisitions: 8 (8%) → $12M realized
Zombies: 52 (52%) → $35M NAV, uncertain

Competing Risks Model:
Year 7 probabilities:
- Exit: 15%
- Failure: 10%
- Acquisition: 5%
- Zombie: 70%

This tells you:

• Exits are slowing (only 12% after 6 years, expect 15% more)
• Failures are tapering (most fail by year 5)
• Zombies are persistent (70% likely to remain uncertain)

Use Cases

1. Portfolio Health Assessment

Question: "Which funds have too many zombies?"

Without Portfolio Company Tracking:

• You see fund NAV, that's it
• No visibility into underlying health
• Can't identify weak performers until marks drop

With Nagare:

Fund ABC (2019 Vintage):
  50 portfolio companies
  Exits: 5 (10%) ← Low
  Failures: 8 (16%) ← Normal
  Zombies: 37 (74%) ← HIGH - Red flag!

Diagnosis: Weak exit discipline
Manager shows inability to drive liquidity events

Action: Flag manager for reduced future allocation.

2. Fundraising & Reporting

Question: "Show LPs our exit track record by cohort."

Without Portfolio Company Data:

• Fund-level metrics only (TVPI, IRR)
• Can't explain WHY performance varies
• LPs skeptical of aggregated numbers

With Nagare:

Your Track Record (2015-2022 Vintages):

2015 Cohort (10 years old):
  Exits: 85% → Strong
  Avg time-to-exit: 4.2 years → Fast
  Avg MOIC: 3.2x → Top quartile

2019 Cohort (6 years old):
  Exits: 35% → On track
  Failures: 18% → Below peer avg (22%)
  MOIC trending: 2.8x → Above target

Takeaway: Consistent exit velocity, low failure rate

Impact: Data-backed fundraising materials. LPs see your edge.

3. Vintage Year Analysis

Question: "2018 vs 2020 vintages: Which performed better?"

The Challenge: Market timing vs manager skill?

Nagare's Approach:

2018 Vintage (deployed pre-COVID):
  48 companies
  Current TVPI: 2.4x
  Exit rate: 45%
  Market beta: +0.6x from public comps

2020 Vintage (deployed during COVID):
  62 companies
  Current TVPI: 2.8x
  Exit rate: 28% (younger cohort)
  Market beta: +1.2x from public comps

Adjusted for Market Timing:
  2018 Alpha: +0.3x (modest outperformance)
  2020 Alpha: +0.8x (strong outperformance)

Conclusion: 2020 was better vintage (opportunistic timing)
            AND better company selection

Insight: You can separate timing luck from skill.

4. Capital Deployment Planning

Question: "When should we expect liquidity from 2021 cohort?"

Without Competing Risks:

• Guess based on "typical hold period"
• No probabilistic modeling
• Often wrong

With Competing Risks Model:

2021 Cohort (now 4 years old):
  42 companies, $85M deployed

Liquidity Forecast (Next 3 Years):
  Year 5 (2026):
    - Exit probability: 25% → 10 companies
    - Expected distributions: $42M (0.5x DPI)

  Year 6 (2027):
    - Exit probability: 30% → 13 companies
    - Expected distributions: $65M (0.75x DPI)

  Year 7 (2028):
    - Exit probability: 20% → 8 companies
    - Expected distributions: $35M (0.4x DPI)

Total 3-year liquidity: $142M (1.65x cumulative DPI)

Confidence: P50 scenario (50th percentile)
P90 (bull case): $210M
P10 (bear case): $85M

Action: Plan capital calls and liquidity needs with data, not hope.

How It Works in Nagare

Step 1: Link Portfolio Companies to Funds

Import or manually enter:

• Company name, sector, stage
• Investment date, amount, valuation
• Current status (active, exited, failed)

Step 2: Define Cohorts

Group by:

• Vintage year
• Fund type (VC, Growth, Buyout)
• Sector (Tech, Healthcare, etc.)
• Geography

Step 3: Analyze Competing Risks

System models:

• Time-to-exit distributions (Weibull)
• Failure hazard rates
• Acquisition probabilities

Step 4: Generate Insights

• Cohort survival curves
• Exit timing forecasts
• Risk-adjusted NAV

Technical Foundation

Competing Risks Engine

Weibull Exit Model:

Time to exit ~ Weibull(λ, k)

Calibrated by fund type:
- VC: λ = 6.5 years, k = 1.8
- Growth Equity: λ = 5.2 years, k = 2.2
- Buyout: λ = 4.8 years, k = 2.5

Failure Hazard Model:

Failure rate peaks at years 2-4, then declines
Adjusted by sector, stage, market conditions

Zombie Probability:

P(Zombie | no exit by year X) increases over time
Companies > 8 years old: 75% likely remain zombies

Data Sources

1. Your portfolio data (primary)
2. Cambridge Associates benchmarks (exit timing)
3. Preqin vintage data (failure rates)
4. Pitchbook sector data (multiples, trends)

Validation

• Back-tested against 500+ funds
• Calibrated to industry benchmarks
• Continuously updated as new data arrives

Real-World Example

Client: $800M fund-of-funds, 35 underlying GP funds

Challenge: IC evaluating Fund VIII commitment from existing GP

Process:

1. Analyze GP's historical cohorts:

   Fund IV (2012): TVPI 2.8x, exit rate 85%, failures 12%
   Fund V (2014): TVPI 3.2x, exit rate 90%, failures 8%
   Fund VI (2016): TVPI 2.1x, exit rate 65%, failures 18%
   Fund VII (2019): TVPI 1.6x, exit rate 35%, failures 15%
   

2. Competing risks analysis:

   Fund VI & VII: Higher failure rates
   Fund VI & VII: Lower exit velocity
   Pattern: Performance degrading since 2016
   

3. Insight:

   Funds IV & V benefited from 2010-2015 bull market
   Funds VI & VII faced compressed multiples
   GP skill: Median (not top-quartile)
   

4. Recommendation: Pass on Fund VIII

Outcome: Reallocated $25M to emerging manager with stronger differentiation.

Impact: Data-driven GP selection instead of "they've been good historically."

Getting Started

1. Navigate to Portfolio Companies tab
2. Import holdings (CSV or manual)
3. Link companies to funds
4. Run Cohort Analysis
5. View competing risks charts

Data Requirements:

• Minimum: Company name, fund, investment date
• Optimal: Add valuation, sector, status for deeper insights

Time to Setup: 30-60 minutes for 100 companies

What's Next

December 2025

• Automated data import from fund portals (Juniper Square, Carta)
• X-ray analysis (see-through to portfolio companies from fund holdings)
• Sector concentration across all funds

Q1 2026

• Exit prediction model (ML-based company-level exit timing)
• Manager skill attribution (separate alpha from beta)
• LP reporting templates (cohort analysis + competing risks)

Q2 2026

• Deal flow scoring (predict which companies will exit)
• Portfolio construction optimizer (based on cohort risk profiles)
• Co-investment decision support (company-level analysis)

Pricing

Portfolio Company Intelligence is included in:

• Institutional ($8,500/mo): Up to 500 companies
• Sovereign (Custom): Unlimited companies + custom analysis

Add-on for lower tiers: $1,000/mo for Boutique plan

Try It Today

Track companies, not just funds:

1. Log in: app.nagarehq.com
2. Navigate to Portfolio Companies
3. Import your first cohort
4. Run competing risks analysis

Free trial: 14 days, no credit card required.

Questions? Email hello@nagarehq.com

---

Related:

• European Waterfall Explained
• Building Market-Linked Monte Carlo Engine
• Why Fund Marks Are Useless for Decisions