Institutional analytics for illiquid private markets

Quantitative risk intelligence for private equity portfolios.

J-curve-aware, multivariate fat-tailed cash-flow models for evaluating downside risk, upside potential, and regime sensitivity across US private equity portfolios, aiming LP portfolios stress-testing, LP-interest lending collateral quality evaluation, IC- and board-reporting, and more.

Projected net cash flows 2026–2036
Median P90 upside P10 downside

Expansion

+12.4%

Volatility: Low

Base Case

+7.1%

Volatility: Medium

Stress

-4.8%

Volatility: High

The problem

Private equity portfolios do not behave like public-market portfolios.

Cash flows are path-dependent, illiquid, vintage-sensitive, and highly regime-dependent. Static NAV views and normal-return assumptions miss the risks lenders and LPs care about most.

J-curve dynamics distort simple volatility measures
Capital calls and distributions clusters are regime sensitive
Tail outcomes are driven by vintage, managers' quality, sector, and size
Liquidity risk is amplified by weak public market outcomes

Modeling engine

J-curve-aware simulations across market regimes.

The engine projects capital calls, distributions, NAV evolution uncertainty, and cash-flow volatility using factor-conditioned stochastic cash-flow behavior rather than generic benchmark assumptions.

J-curve-aware stochastic cash-flow modeling
Multivariate fat-tailed simulations
Public, private, and macro-factor regimes
Vintage, sector, fund-size, and manager-quality effects
Regime-aware Liquidity-at-risk and capital-call clustering metrics
Algo- and Human-verified US private equity cash-flow data

Factor sensitivity heatmap

Stress impact by portfolio characteristic

Public
Private
Macro
Liquidity
Vintage
Sector
Manager Quality
Fund Size

Simulation paths

50k+

Validated data

US PE

Use cases

Decision support for lenders, LPs, allocators, and risk teams.

LP NAV Lending

Evaluate PE portfolio collateral quality, liquidity coverage, and downside cash-flow risk for LP-interest lending.

What-If Allocation

Model the impact of adding new funds by vintage, sector, size, or manager quality.

Stress Testing

Quantify cash-flow volatility and tail outcomes under recessionary, liquidity-constrained, or denominator-effect regimes.

Portfolio Construction

Balance diversification, capital-call clustering, vintage exposure, and expected distributions over time.

Secondary Pricing Support

Estimate probabilistic future cash-flow distributions to support LP-interest valuation and transaction diligence.

IC & Risk Reporting

Produce decision-ready reports for lenders, LPs, investment committees, risk teams, and boards.

Outputs

Institutional-grade reports and Agent-built interpretation support.

Each analysis translates model outputs into lender-ready and IC-ready exhibits: cash-flow forecasts, scenario comparisons, downside percentiles, liquidity gaps, and portfolio-specific risk drivers.

Cash-flow projections with percentile bands
Three-regime what-if analysis
Vintage, size, sector, and manager-quality decomposition
Dedicated Agent reports interpretation with results drill-down
Portfolio Stress SummarySample excerpt
Net CF
7.1%
Tail Loss
-14.6%
Liquidity Gap
1.2x
Volatility
18.3%
Regime comparison
RegimeNet CFVolGap
Expansion+12.4%Low1.8x
Base Case+7.1%Medium1.2x
Stress-4.8%High0.7x

Data advantage

Built on algorithmically processed and human-verified historical cash flows.

The edge is cash-flow-level granularity: cleaned, normalized, verified, and tagged across market regimes for the US private equity market.

1
Raw fund cash flows
2
Validation and anomaly checks
3
Size normalization and outliers categorization
4
Regime classification
5
Portfolio simulation engine
6
Decision-ready reporting

Understand liquidity risk before markets do.

Get in touch or grab the sample report to see how regime-aware PE cash-flow analytics can support collateral evaluation, stress testing, and portfolio decisions.