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Portfolio-Aware Capital Allocation for Investor Matching

Author: PlanExe Team
Date: 2026-02-10
Status: Proposal
Tags: portfolio, allocation, optimization, risk, roi

Pitch

Upgrade matching from single-deal recommendations to portfolio-aware allocation so each investor sees opportunities that improve total expected portfolio ROI under risk constraints.

TL;DR

  • Build optimizer that recommends not only “what to invest in,” but also “how much.”

  • Use covariance, concentration, and liquidity constraints.

  • Prioritize deals with positive marginal contribution to portfolio return.

  • Increase IRR consistency while reducing downside clustering.

Problem

Most matching systems rank opportunities independently. Investors, however, deploy capital at portfolio level. Independent rankings can cause:

  • Sector overconcentration

  • Correlated downside exposure

  • Capital fragmentation into low-impact checks

Proposed Solution

Add a Portfolio Allocation Optimizer on top of plan-investor fit scores.

For each investor:

  1. Estimate expected return distribution per plan

  2. Estimate cross-plan correlation using sector + macro + business-model features

  3. Solve constrained optimization for check sizing

  4. Output prioritized shortlist with recommended allocation ranges

Architecture

┌──────────────────────────────┐
│ Plan Return Forecasts        │
│ - Expected MOIC/IRR          │
│ - Volatility + downside      │
└──────────────┬───────────────┘
               │
               ▼
┌──────────────────────────────┐
│ Correlation Estimation       │
│ - Sector links               │
│ - Revenue-model similarity   │
│ - Macro factor exposure      │
└──────────────┬───────────────┘
               │
               ▼
┌──────────────────────────────┐
│ Allocation Optimizer         │
│ - Constraints                │
│ - Position sizing            │
│ - Efficient frontier         │
└──────────────┬───────────────┘
               │
               ▼
┌──────────────────────────────┐
│ Investor Decision UI         │
│ - Recommended checks         │
│ - Risk contribution chart    │
│ - Scenario stress tests      │
└──────────────────────────────┘

Implementation

Phase 1: Return and Risk Inputs

  • Standardize plan-level return forecasts to common horizons.

  • Add downside metrics: probability of loss, expected drawdown, time-to-liquidity.

Phase 2: Optimizer Service

  • Formulate as constrained optimization:

  • Maximize expected portfolio utility (E[R] - λ*Risk)

  • Subject to check size, sector cap, stage cap, and liquidity limits.

  • Run weekly recalculation and event-triggered refreshes.

Phase 3: Decision Layer

  • Render “marginal portfolio impact” per candidate.

  • Provide stress scenarios (recession, funding winter, supply shock).

  • Expose allocation confidence intervals.

Success Metrics

  • Portfolio Sharpe-like Improvement: +15% relative to baseline manual allocation.

  • Concentration Control: No sector > configured cap in 95% of portfolios.

  • Capital Efficiency: Higher deployed capital per decision hour.

  • Downside Reduction: Lower 24-month tail-loss percentile.

Risks

  • False precision in early-stage forecasting → Use wide intervals and robust optimization.

  • Correlation instability → Re-estimate continuously and include regime-switch models.

  • User complexity fatigue → Default to simple recommendations with optional advanced views.

  • Data lag → Ingest milestone updates in near real time.

Why This Matters

Investors care about total portfolio outcomes, not isolated deal quality. Portfolio-aware matching improves capital allocation quality and makes ROI predictions more actionable.

Detailed Implementation Plan

Phase A — Portfolio Model

  1. Define portfolio objective functions (return, risk, diversification).
  2. Add constraint model (sector caps, stage caps, geographic limits).
  3. Ingest candidate plan opportunities as allocatable units.

Phase B — Allocation Solver

  1. Implement optimizer (heuristic + optional convex optimization mode).
  2. Support scenario-based allocation stress tests.
  3. Output recommended allocations with rationale and alternatives.

Phase C — Monitoring and Rebalancing

  1. Track realized vs expected performance.
  2. Trigger rebalance suggestions on drift.
  3. Log decision history for governance review.

Validation Checklist

  • Constraint satisfaction rate
  • Risk-adjusted return vs baseline policy
  • Rebalance action quality over time