Portfolio Analyzer

Portfolio Analyzer — Fund-Level Risk and Return

Individual deal quality is necessary but not sufficient for fund returns. A portfolio of individually strong biotech investments can still produce poor fund-level returns if the assets are correlated (all in the same therapeutic area, same phase, same mechanism class), under-reserved (insufficient capital for follow-on), or poorly timed (vintage concentration). This skill analyzes biotech venture portfolios at the fund level, evaluating concentration, correlation, expected value, and construction.

Fund-construction benchmarks: MOIC/PoS thresholds, HHI bands, correlation-by-risk-source, phase-mix, and reserve ratios are in references/portfolio-construction-benchmarks.md (LOA base rates live in pos-base-rates' transition-probability-tables.md).

How to Run

Input

Steps

Step 1 — Expected Portfolio Value (PoS-Weighted rNPV)

Calculate the probability-weighted expected value of the entire portfolio:

PORTFOLIO EXPECTED VALUE

| Asset | TA | Phase | Investment | rNPV | LOA | Expected Value | EV/Investment |
|-------|-----|-------|------------|------|-----|----------------|---------------|
| A     | Onc | Ph 2  | $15M       | $300M| 12% | $36M           | 2.4x          |
| B     | Imm | Ph 1  | $8M        | $500M| 6%  | $30M           | 3.8x          |
| C     | Rare| Ph 3  | $25M       | $200M| 55% | $110M          | 4.4x          |
| D     | CNS | Ph 2  | $12M       | $150M| 10% | $15M           | 1.3x          |
| ...   |     |       |            |      |     |                |               |

Portfolio Totals:
  Total Invested: $[X]M
  Total Expected Value: $[X]M
  Portfolio Expected Multiple: [X]x
  Portfolio IRR (time-weighted): [X]%

Benchmark: Top-quartile biotech venture funds target 3-4x gross MOIC. Expected portfolio multiple should exceed 3x to account for losses on failed programs.

Step 2 — Portfolio Probability of Success

The probability that at least one asset in the portfolio succeeds (reaches approval and generates meaningful return):

P(at least one success) = 1 - Π(1 - PoS_i)   [for independent assets]

Example:
  5 assets with LOA of 10%, 8%, 12%, 6%, 15%
  P(all fail) = 0.90 x 0.92 x 0.88 x 0.94 x 0.85 = 0.582
  P(at least one success) = 1 - 0.582 = 41.8%

Fund viability threshold: P(at least one success) should exceed 70% for a viable biotech fund. Below 50%, the fund has meaningful probability of total loss.

Important caveat: This calculation assumes independence. Correlated assets (same TA, same target class) have lower effective portfolio PoS than the independent calculation suggests. See Step 4.

Step 3 — Therapeutic Area Concentration (Herfindahl Index)

Measure portfolio concentration using the Herfindahl-Hirschman Index (HHI):

HHI = Σ (share_i)^2

Where share_i = investment in TA_i / total portfolio investment

HHI INTERPRETATION:
  <0.15 (1,500):  Diversified — no single TA dominates
  0.15-0.25:      Moderate concentration — acceptable for focused funds
  0.25-0.40:      High concentration — deliberate strategy required
  >0.40:          Very high concentration — single-TA risk

THERAPEUTIC AREA DISTRIBUTION:
| Therapeutic Area | # Assets | Investment | Share | Share^2 |
|------------------|----------|------------|-------|---------|
| Oncology         | 3        | $40M       | 40%   | 0.160   |
| Immunology       | 2        | $25M       | 25%   | 0.063   |
| Rare Disease     | 2        | $20M       | 20%   | 0.040   |
| CNS              | 1        | $15M       | 15%   | 0.023   |

HHI = 0.285 → High concentration (oncology-heavy)

Also calculate by modality, mechanism class, and geography for multi-dimensional concentration assessment.

Step 4 — Correlation Modeling

Biotech assets are not independent. Correlation reduces effective diversification:

Sources of correlation:

Correlation-adjusted portfolio PoS:

When assets are correlated, the effective portfolio PoS is lower than the independent calculation:

P(at least one success, correlated) ≈ 1 - Π(1 - PoS_i) x (1 + correlation_adjustment)

Correlation adjustment:
  For 2 correlated assets: factor = ρ x PoS_1 x PoS_2 / [P(both fail, independent)]
  
Simplified approach for portfolio-level:
  Average pairwise ρ across portfolio
  Effective PoS = Independent PoS x (1 - average_ρ/2)

Portfolio implication: A portfolio of 5 oncology assets with average pairwise ρ = 0.3 provides the diversification equivalent of ~3.5 independent assets, not 5.

Step 5 — Phase Distribution Analysis

Optimal biotech venture portfolios balance risk across development phases:

PHASE DISTRIBUTION

| Phase | # Assets | Investment | % of Fund | PoS Range | Expected Data |
|-------|----------|------------|-----------|-----------|---------------|
| Preclinical | [N] | $[X]M | [X]% | 3-5% LOA | [timeline] |
| Phase 1     | [N] | $[X]M | [X]% | 5-10% LOA | [timeline] |
| Phase 2     | [N] | $[X]M | [X]% | 10-25% LOA | [timeline] |
| Phase 3     | [N] | $[X]M | [X]% | 40-65% LOA | [timeline] |

OPTIMAL BENCHMARKS (for balanced biotech fund):
  Preclinical + Phase 1: 20-35% of capital (high risk, high multiple)
  Phase 2: 35-45% of capital (core portfolio)
  Phase 3+: 20-35% of capital (de-risked, lower multiple)

J-curve consideration: Early-stage heavy portfolios have longer J-curves (3-5 years before first positive cash event). Late-stage heavy portfolios have shorter J-curves but lower return potential. Balance based on fund LP expectations and fund life.

Step 6 — Portfolio Optimization

Given a set of available deals and a capital constraint, optimize portfolio construction:

Objective: Maximize expected portfolio value subject to:

• Total investment <= fund allocation for new deals
• TA concentration HHI <= target threshold
• Phase distribution within target ranges
• Minimum portfolio PoS threshold met

Marginal contribution analysis for a new asset:

MARGINAL PORTFOLIO CONTRIBUTION — [New Asset Name]

Standalone Metrics:
  rNPV: $[X]M
  LOA: [X]%
  Expected Value: $[X]M
  EV/Investment: [X]x

Portfolio Impact:
  Portfolio Expected Value: $[X]M → $[X]M  (Δ = +$[X]M)
  Portfolio PoS (≥1 success): [X]% → [X]%  (Δ = +[X]%)
  TA HHI: [X] → [X]  ([improving/worsening] concentration)
  Phase distribution: [impact on phase balance]
  Correlation with existing assets: ρ = [X] ([Low/Moderate/High])

Marginal Verdict: [Adds diversification / Increases concentration / Neutral]

Step 7 — Vintage Year Analysis

Biotech venture fund returns are vintage-dependent. Assess portfolio timing:

VINTAGE DISTRIBUTION

| Vintage Year | # Assets | Investment | Key Catalyst Year |
|--------------|----------|------------|-------------------|
| 2023         | 2        | $20M       | 2025-2026         |
| 2024         | 3        | $35M       | 2026-2027         |
| 2025         | 2        | $25M       | 2027-2028         |

Concentration risk: [X]% of capital deployed in single vintage year
Data readout clustering: [N] assets with data in same 12-month window

Risk: If multiple assets have data readouts in the same window and the market is risk-off, mark-to-market losses can be severe even for programs that are proceeding well.

Step 8 — Reserve Allocation for Follow-On

Biotech venture investing requires capital reserves for follow-on rounds in winning assets:

RESERVE ANALYSIS

Total Fund Size: $[X]M
Initial deployment (new deals): $[X]M  ([X]% of fund)
Follow-on reserves: $[X]M  ([X]% of fund)
Management fees + expenses: $[X]M  ([X]% of fund)

Reserve Adequacy:
  Assets expected to need follow-on: [N] (assets advancing past Phase 2)
  Average follow-on per advancing asset: $[X]M
  Total follow-on demand (expected): $[X]M
  Reserve coverage ratio: [X]x  (>1.5x recommended)

RESERVE ALLOCATION POLICY:
  Phase 1 → Phase 2 advance: Reserve $[X]M per asset
  Phase 2 → Phase 3 advance: Reserve $[X]M per asset
  Phase 3 → Commercial: Reserve $[X]M per asset (if pre-revenue company)

Reserve sufficiency: [Adequate / Tight / Insufficient]

Critical rule: Under-reserved funds are forced to dilute in winning positions (selling winners to other investors at lower returns) or allow pro-rata to lapse. Top-quartile funds reserve 40-60% of capital for follow-on.

Output

PORTFOLIO ANALYSIS — [Fund Name / Portfolio]
Date: [assessment date]

SUMMARY METRICS:
  Total Assets: [N]
  Total Invested: $[X]M
  Portfolio Expected Value: $[X]M
  Expected Multiple: [X]x
  Portfolio PoS (≥1 success): [X]%
  TA HHI: [X] ([diversified/moderate/concentrated])

CONCENTRATION ANALYSIS:
  Top TA: [name] at [X]% of capital
  Top Modality: [name] at [X]% of capital
  Top Mechanism Class: [name] at [X]% of capital

PHASE DISTRIBUTION: [balanced / early-heavy / late-heavy]

CORRELATION ASSESSMENT: Average pairwise ρ = [X]
  Effective independent assets: [X] (vs [N] actual)
  Correlation-adjusted PoS: [X]%

RESERVE STATUS: [Adequate / Tight / Insufficient]
  Reserve coverage ratio: [X]x

NEW ASSET FIT (if evaluating): [Adds value / Neutral / Increases risk]

KEY PORTFOLIO RISKS:
1. [Top risk — e.g., TA concentration]
2. [Second risk — e.g., vintage clustering]
3. [Third risk — e.g., reserve inadequacy]

OPTIMIZATION RECOMMENDATIONS:
1. [Action to improve portfolio — e.g., seek CNS asset for diversification]
2. [Action — e.g., reserve additional capital for Phase 3 advances]
3. [Action — e.g., reduce oncology exposure in next deployment]

Error Handling

Cross-Domain Connections

• deal-synthesis/diligence-scorecard: Individual asset scores are inputs to portfolio-level analysis
• deal-synthesis/investment-memo-writer: Portfolio fit is a section in the investment memo
• asset-valuation/rnpv-modeler: rNPV per asset feeds expected portfolio value calculation
• probability-of-success/pos-calculator: PoS per asset feeds portfolio PoS calculation
• competitive-intelligence/market-dynamics: TA market dynamics affect correlation assumptions