EndowCast

Sample Report

DevAfri Private Foundation — Three-Portfolio Comparison

$500M Private Foundation  ·  10-Year Horizon  ·  1,000,000 Simulations

A: Traditional 60/40
B: Diversified Endowment Model (Current Policy)
C: Growth-Oriented Alternatives

Each portfolio was run through EndowCast's simulation engine using 1,000,000 Monte Carlo paths with Cholesky-decomposed correlated asset returns. All three portfolios share identical capital market assumptions and random seeds — ensuring that differences in outcomes reflect only the effect of asset allocation, spending policy, and rebalancing strategy.

Traditional 60/40

$536.0M

Annualized Return5.72%
Volatility9.10%
Sharpe Ratio0.34
Max Drawdown18.96%
Depletion Prob.0.0%
Diversified Endowment Model
Current Policy

$571.1M

Annualized Return6.36%
Volatility10.37%
Sharpe Ratio0.37
Max Drawdown19.71%
Depletion Prob.0.0%
Growth-Oriented Alternatives

$587.1M

Annualized Return6.68%
Volatility11.44%
Sharpe Ratio0.37
Max Drawdown21.09%
Depletion Prob.0.0%
Key Metrics Comparison
MetricTraditional 60/40Diversified Endowment ModelGrowth-Oriented Alternatives
Median Final Value$536.0M$571.1M$587.1M
Annualized Return5.72%6.36%6.68%
Annualized Volatility9.10%10.37%11.44%
Sharpe Ratio0.340.370.37
Sortino Ratio0.200.310.35
Max Drawdown18.96%19.71%21.09%
Probability of Depletion0.0%0.0%0.0%

Across all 1,000,000 scenarios, the Growth-Oriented Alternatives portfolio (Portfolio C) delivers the highest median terminal value at $587.1M — a 9.53% premium over the Traditional 60/40. However, this comes with 11.44% annualized volatility versus 9.10% for the 60/40, and a maximum drawdown of 21.09% versus 18.96%. The Diversified Endowment Model (Portfolio B) occupies a middle ground — capturing much of the upside ($571.1M) while maintaining a Sharpe ratio of 0.37. The trade-off between return potential and capital preservation is material: Portfolio C has a 15.6% probability of losing 30% or more of purchasing power over the decade, versus 13.1% for the 60/40. Investment committees should weigh this tail risk against the $310.9M in cumulative spending capacity Portfolio C provides — $20.3M more than the 60/40 for mission deployment.

Evaluate Asset Allocation and Spending Policy for Your Institution

EndowCast supports investment teams and committees in assessing trade-offs, downside risk, and policy sustainability across market environments.
Three-Portfolio Comparison — Median Projections
Individual Portfolio Fan Charts
Traditional 60/40
Diversified Endowment Model
Growth-Oriented Alternatives
Percentile Value Table ($M)
YearP10P25MedianP75P90
Portfolio A: Traditional 60/40
Year 0$500M$500M$500M$500M$500M
Year 1$480M$499M$518M$539M$559M
Year 2$460M$497M$535M$579M$622M
Year 5$399M$488M$586M$701M$813M
Year 10$304M$463M$675M$922M$1155M
Portfolio B: Diversified Endowment Model
Year 0$500M$500M$500M$500M$500M
Year 1$478M$501M$525M$551M$578M
Year 2$454M$500M$550M$605M$664M
Year 5$376M$491M$623M$779M$953M
Year 10$268M$456M$738M$1103M$1505M
Portfolio C: Growth-Oriented Alternatives
Year 0$500M$500M$500M$500M$500M
Year 1$476M$500M$525M$555M$586M
Year 2$448M$498M$552M$615M$681M
Year 5$351M$480M$635M$829M$1026M
Year 10$223M$433M$781M$1276M$1762M
Spending Risk Statistics
MetricTraditional 60/40Diversified Endowment ModelGrowth-Oriented Alternatives
Spending Policy MethodSimple Market ValueRolling Average (3 yr)Hybrid (70/30 CPI)
Total Cumulative Spending$290.6M$303.0M$310.9M
Spending Volatility9.20%10.47%8.08%
Negative Spend Years45.38%42.92%41.45%
Avg Spend Drawdown21.19%22.17%19.04%
Max Spend Drawdown78.36%77.81%78.38%
Median Annual Spending Trajectory

The spending analysis reveals a central trade-off in endowment policy design. Portfolio C's Hybrid policy — blending a 70% CPI-linked baseline with a 30% market value component — delivers the lowest spending volatility (8.08%) and the lowest average spending drawdown (19.04%), despite Portfolio C carrying the highest asset-level risk. This is the Hybrid policy's structural advantage: the CPI-anchored baseline maintains grantmaking continuity during market downturns. Portfolio A's Simple Market Value policy — while generating the highest negative spend year frequency (45.38%) — also provides the most direct link between portfolio performance and spending, ensuring distributions contract quickly in bear markets to preserve long-term purchasing power. Portfolio B's Rolling Average policy occupies an intermediate position but generates the highest total cumulative spending ($303.0M invested in mission over the decade — $12.4M more than the 60/40). Foundations prioritizing grantee predictability should examine Portfolio C's Hybrid approach; those maximizing total mission deployment should consider Portfolio B; those concerned primarily with long-term corpus preservation may prefer the discipline of Portfolio A's market-linked rule.

Rebalancing Impact
Traditional 60/40

Strategy: No rebalancing. Asset class weights drift freely with market returns — serving as the baseline for comparison.

Without Rebalancing (Baseline)
$536.0M
Endowment Model

Strategy: Annual rebalancing — 10 events over the projection horizon. Restores portfolio weights to policy targets each year.

Net Impact of Rebalancing
+$3.5M (+0.61%)
P10 downside improved from $364.6M to $367.3M. Volatility reduced by 6.38%.
Growth Alternatives

Strategy: Threshold-based (5% drift tolerance). Triggers only when allocations deviate beyond bands — fewer events, lower turnover.

Net Impact of Rebalancing
+$2.0M (+0.34%)
P10 downside improved from $357.2M to $358.8M. Volatility reduced by 2.68%.
How EndowCast Calculates Rebalancing Impact

EndowCast uses a dual-run counterfactual methodology to isolate rebalancing impact. Each portfolio configuration is simulated twice against identical market return paths — once applying the configured rebalancing strategy and once allowing the portfolio to drift freely without any rebalancing. The difference between the two runs represents the pure contribution of the rebalancing strategy, separated from all other variables including asset returns, spending policy, and fee structure. This approach is methodologically more rigorous than estimating rebalancing impact from back-of-the-envelope formulas.

Rebalancing Impact — Detailed Comparison
MetricA: 60/40B: Endowment ModelC: Growth Alt.
Rebalancing StrategyNoneAnnualThreshold (5%)
Median Final Value — With Rebal.$536.0M$571.1M$587.1M
Median Final Value — No Rebal.$536.0M$567.6M$585.1M
Net Rebalancing Impact ($)— (baseline)+$3.5M+$2.0M
Net Rebalancing Impact (%)+0.61%+0.34%
P10 Downside — With Rebal.$367.3M$358.8M
P10 Downside — No Rebal.$364.6M$357.2M
Max Drift Prevented30.9%30.9%

Both rebalancing strategies added measurable value over the projection horizon. Annual rebalancing (Portfolio B) contributed +$3.5M to median terminal value while reducing downside dispersion — the P10 outcome improved by $2.7M relative to the no-rebalancing counterfactual. Threshold-based rebalancing (Portfolio C) added +$2.0M with fewer rebalancing events, lowering turnover while still preventing up to 31% allocation drift. Portfolio A, simulated without rebalancing, serves as the baseline — its asset class weights, particularly the 60% public equity allocation, drifted proportionally with relative asset class performance over the decade.

Probability of Principal Loss by Threshold
IRS §4942 Compliance
Traditional 60/40

>99.9%

Compliant

In all 1,000,000 simulation paths, qualifying distributions met or exceeded the required distributable amount in every year of the projection. The 5% spending rate, combined with positive expected returns, eliminates §4942 shortfall risk over this horizon.

Diversified Endowment Model

>99.9%

Compliant

In all 1,000,000 simulation paths, qualifying distributions met or exceeded the required distributable amount in every year of the projection. The 5% spending rate, combined with positive expected returns, eliminates §4942 shortfall risk over this horizon.

Growth-Oriented Alternatives

>99.9%

Compliant

In all 1,000,000 simulation paths, qualifying distributions met or exceeded the required distributable amount in every year of the projection. The 5% spending rate, combined with positive expected returns, eliminates §4942 shortfall risk over this horizon.

Risk Metrics Comparison
Risk MetricTraditional 60/40Diversified Endowment ModelGrowth-Oriented Alternatives
Sharpe Ratio0.340.370.37
Sortino Ratio0.200.310.35
CVaR (95%)-42.87%-43.39%-45.97%
Max Drawdown18.96%19.71%21.09%
Probability of Loss40.79%35.04%33.98%
Prob. of Depletion0.0%0.0%0.0%

The risk analysis reveals a clear risk-return gradient. Portfolio C carries the most severe tail risk — a 15.6% probability of losing 30% or more of purchasing power over the decade, meaning in roughly 1 in 6 market environments, the foundation would experience severe capital impairment. For a foundation with committed multi-year grant obligations, this tail risk may be unacceptable despite the higher expected return. Portfolio A's 13.1% probability of a 30% loss represents a more conservative risk posture. The Sortino ratios — 0.20, 0.31, and 0.35 respectively — indicate that Portfolio C most efficiently compensates for its downside risk, but the absolute magnitude of that downside (21.09% max drawdown) may exceed what many investment committees are willing to accept.

Portfolio Configurations
Traditional 60/40
Asset Allocation
Asset ClassWeightValue
Public Equity60%$300.0M
Public Fixed Income40%$200.0M
Spending Policy

Method: Simple

Rate: 5.00%

Rebalancing

Disabled

Diversified Endowment Model
Asset Allocation
Asset ClassWeightValue
Public Equity45%$225.0M
Private Equity15%$75.0M
Public Fixed Income20%$100.0M
Real Assets10%$50.0M
Diversifying Strategies7%$35.0M
Private Credit3%$15.0M
Spending Policy

Method: Rolling

Rate: 5.00%

Period: 3 years

Rebalancing

Annual

Growth-Oriented Alternatives
Asset Allocation
Asset ClassWeightValue
Public Equity35%$175.0M
Private Equity25%$125.0M
Public Fixed Income10%$50.0M
Real Assets15%$75.0M
Diversifying Strategies10%$50.0M
Private Credit5%$25.0M
Spending Policy

Method: Hybrid

Rate: 5.00%

Blend: 70% CPI / 30% Market Value

Rebalancing

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Capital Market Assumptions
Asset ClassExpected ReturnVolatility
Public Equity7.50%15.00%
Private Equity10.50%22.00%
Public Fixed Income4.00%4.00%
Real Assets5.00%9.00%
Diversifying Strategies5.50%8.00%
Private Credit7.00%10.00%
Cash3.00%0.50%
Correlation Matrix
Pub EqPriv EqFixed IncReal AstDiv StratPriv CredCash
Pub Eq1.00.70.20.50.60.50.1
Priv Eq0.71.00.10.40.60.60.1
Fixed Inc0.20.11.00.20.10.20.3
Real Ast0.50.40.21.00.40.30.1
Div Strat0.60.60.10.41.00.50.1
Priv Cred0.50.60.20.30.51.00.1
Cash0.10.10.30.10.10.11.0
Methodology

This simulation used 1,000,000 Monte Carlo paths with Cholesky-decomposed correlated asset returns to preserve the correlation structure between all seven asset classes. Annual returns were sampled from a multivariate normal distribution calibrated to the capital market assumptions above. The simulation incorporates the configured spending rule applied to each path's portfolio value sequence, threshold-based rebalancing evaluated annually, liquidity constraints preventing forced selling of illiquid assets, and IRS §4942 compliance evaluation comparing projected qualifying distributions against the calculated distributable amount in each year and path. Rebalancing impact is calculated by running each portfolio twice against identical market return paths — once applying the configured rebalancing strategy and once allowing the portfolio to drift freely — isolating the pure contribution of rebalancing from all other variables.

Important Disclaimer

This report is a hypothetical illustration generated by EndowCast for demonstration purposes only. It does not represent actual investment results or advice. The Clearwater Family Foundation is a fictional entity. All portfolio values, returns, allocations, percentiles, and statistics are illustrative. Capital market assumptions are hypothetical and do not reflect any specific investment consultant's or OCIO's published assumptions. Past performance does not guarantee future results. Monte Carlo simulation results are inherently dependent on the input assumptions and should not be interpreted as predictions. Any foundation considering changes to its spending policy, asset allocation, or investment strategy should consult with its investment advisor, legal counsel, and tax professionals.