How Nagare Calculates Projections for Each Asset Type

When you're managing a portfolio of private funds alongside public holdings, understanding how projections work helps you make better decisions. This guide explains how Nagare's four calculation engines forecast returns for each asset type.

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The Architecture: Four Specialized Engines

Nagare uses four specialized projection calculators, each designed for a specific asset category:

1. PE Projection Calculator — Private equity, venture capital, growth, buyout, real estate, infrastructure, secondaries, fund of funds
2. Credit Projection Calculator — Private credit and direct lending
3. Public Market Calculator — Stocks, bonds, ETFs, custom portfolios
4. Money Market Calculator — Cash and money market funds

Each engine takes your fund parameters and produces period-by-period projections of capital calls, distributions, NAV, and performance metrics (IRR, TVPI, DPI, RVPI).

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Private Equity & Venture Capital Engine

Handles: Venture Capital, Growth Equity, Buyout, Real Estate, Infrastructure, Secondary, Fund of Funds

The Inputs

How the Engine Works

Step 1: Generate the Deployment Schedule

The engine uses your deployment curve to determine when capital is called. We offer preset curves calibrated to industry data:

• Front-loaded (VC): 70% called in years 1-3, remaining 30% in years 4-5
• Linear: Even deployment over the commitment period
• Back-loaded: Slower initial deployment, accelerating later

For each period, we calculate:

Capital Call = Remaining Unfunded × Deployment Rate for Period

Step 2: Model Value Creation (MOIC-First)

Unlike simple compound interest models, private funds create value primarily upon exit. Nagare uses a MOIC-First (Multiple on Invested Capital) approach:

1. We calibrate a MOIC Amplitude that ensures the fund hits its target TVPI (e.g., 2.5x) exactly.
2. Value is realized at the moment of exit, based on this multiple.
3. Unrealized NAV reflects the cost basis of remaining assets plus any accrued value, but the primary driver of returns is the exit event.

This "outcome-based" modeling is more stable and realistic than assuming a constant 15% annual growth rate, which often leads to inaccurate terminal values.

Step 3: Generate Exit/Distribution Schedule (Yearly Cohort Model)

We use a sophisticated Yearly Cohort Engine to model exit timing. Capital deployed in each vintage year follows a specific survival curve:

• Survival Function: Probability that an asset is not exited by age t.
• Distributions: Calibrated to industry data (Cambridge Associates/Preqin).

The exit curve determines when cash is returned, while the MOIC determines how much. This separation allows us to stress-test timing (liquidity risk) independently of performance (valuation risk).

Step 4: Calculate Fees and Carry

We model the European waterfall structure:

1. Return of Capital: LP gets contributed capital back first
2. Preferred Return: LP earns hurdle rate (typically 8% IRR) on drawn capital
3. GP Catch-Up: GP receives most of distributions until carried interest target is met
4. Profit Split: Remaining profits split (typically 80% LP / 20% GP)

Step 5: Compute Metrics

For each period, we calculate:

• TVPI = (NAV + Total Distributions) / Total Called
• DPI = Total Distributions / Total Called
• RVPI = NAV / Total Called
• IRR = Discount rate that makes NPV of cashflows = 0 (using Newton-Raphson with Brent's fallback)

Special Handling: Portfolio Companies

If you've entered individual portfolio companies (for direct co-investments or company-level tracking), we use a cohort-based model:

• Each company has an investment date and cost basis
• Exit timing is sampled from competing-risks distributions (IPO, M&A, write-off)
• Company-level growth rates can differ from fund-level assumptions

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Private Credit Engine

Handles: Private Credit, Direct Lending

The Inputs

How the Engine Works

Step 1: Deployment Schedule

Credit funds typically deploy faster than PE (borrowers need capital now):

Deployed_t = Commitment × Cumulative Deployment Factor

We model deployment over 12-24 months for most direct lending funds.

Step 2: Interest Income

Unlike PE, credit generates regular cash income:

Interest Payment = Outstanding Principal × (Coupon Rate / Payment Frequency)

PIK interest accrues to principal rather than paying out:

New Principal = Principal + (Principal × PIK Rate × Period)

Step 3: Principal Repayment

Loans mature or prepay based on the amortization schedule:

Principal Return_t = Outstanding × Repayment Factor + Prepayments

Step 4: Credit Losses

We model expected defaults and recoveries:

Loss = Outstanding × Default Rate × (1 - Recovery Rate)

This reduces both principal and projected income.

Step 5: Compute Metrics

Credit funds report:

• Current Yield = Annual Interest / NAV
• Total Return = (Income + Principal Change) / Cost
• TVPI/IRR = Same as PE, but typically lower and more stable

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Public Market Engine

Handles: Public Equity, Public Debt, Public Portfolio

The Inputs

How the Engine Works

Step 1: Project Market Value

We use a simple growth model:

Value_t = Initial Value × (1 + Expected Return)^t

For Monte Carlo, we sample from a distribution:

Return_t = μ + σ × Z  (where Z is standard normal)

Step 2: Income Projection

Dividends/coupons are projected based on yield:

Income_t = Value_t × Dividend Yield / 4  (for quarterly)

We support reinvestment or distribution of income.

Step 3: Correlation with Private Holdings

This is where public market projections get interesting. We model how your public portfolio moves with:

• Market indices (S&P 500, MSCI, etc.)
• Your private fund valuations (with a lag)

This helps answer: "If markets drop 20%, what happens to my total portfolio?"

Special Case: Custom Portfolios

For PUBLIC_PORTFOLIO fund type, you can specify individual holdings:

• Each position has quantity, price, dividend yield
• We aggregate position values and income
• Portfolio-level statistics (beta, volatility) are computed from holdings

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Money Market Engine

Handles: Money Market, Cash

The Inputs

How the Engine Works

Money market is the simplest engine:

Step 1: Interest Accrual

Interest_t = Balance × (Yield Rate / 12)

Step 2: Balance Update

Balance_t+1 = Balance_t + Interest_t + Inflows - Outflows

Step 3: Liquidity Tracking

The engine tracks available liquidity for capital calls:

Available = Balance - Minimum Balance - Upcoming Calls

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How the Engines Work Together

The Calculation Engine orchestrates all four calculators:

1. Load Fund Parameters — For each fund in your portfolio
2. Resolve Data — Merge actuals (transactions) with projections, using priority: Actual > Forecast > Master > Scenario
3. Route to Calculator — Based on fund type
4. Generate Projections — Period-by-period for each fund
5. Aggregate to Portfolio — Sum across funds, convert currencies
6. Run Monte Carlo (optional) — 1000+ iterations for risk analysis

Data Priority (The Waterfall)

When we have both actual data and projections, actuals always win:

This ensures your projections blend seamlessly with real data.

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Monte Carlo: Adding Uncertainty

The deterministic projections above show a single expected path. The Enhanced Monte Carlo Engine shows the range of outcomes.

How It Works

1. Sample Market Returns — Draw from historical distributions
2. Correlate Across Funds — PE and VC don't move independently
3. Apply to NAV and Exit Timing — Market conditions affect valuations and exit windows
4. Repeat 1000+ Times — Build a distribution of outcomes

What You Get

Plus distributions for:

• Peak NAV timing
• Breakeven period
• Final TVPI and IRR

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Questions?

If you'd like to understand how your specific portfolio would be modeled, request a demo and we'll walk through it with real numbers.

For the full mathematical specification, see our Financial Modeling Methodology documentation.