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Design Objective

Design a portfolio that generates at least 10% annual cash distributions while maintaining long-term cumulative returns that remain competitive with the S&P 500.

Abstract

Most investors are told to avoid high-dividend ETFs because they destroy NAV, underperform broad-market index funds, and sacrifice long-term wealth for current income. For many early income ETFs, that conclusion was justified.

The ETF industry has evolved. Mechanical, fully covered option strategies have increasingly been replaced by more dynamic option-overlay approaches that preserve substantially more upside participation while continuing to generate meaningful cash distributions.

Yield without competitive cumulative return is not wealth creation.

Design for Wealth™ applies engineering design principles to identify high-income ETFs that merit serious consideration for specific investor use cases. Its three-model analysis evaluates actual observed performance, long-term strategy reconstruction, and plausible future conditions before the evidence is translated into portfolio architecture. The framework emphasizes cumulative return, opportunity cost, benchmark-relative performance, issuer quality, and disciplined allocation rather than yield alone.

Portfolio Design Principles

Portfolio construction begins by defining design objectives before selecting solutions. Rather than selecting ETFs based on yield or recent performance, the framework applies consistent engineering principles to evaluate, allocate, and periodically improve the portfolio as new evidence becomes available.

Nine portfolio design principles with their requirements and applications.
No. Design Principle Requirement & Application
01

Design for Supplemental Capital

 Framework Scope

Design for Wealth™ is intended primarily for supplemental capital, not as a replacement for diversified long-term savings. High-distribution ETFs involve greater complexity and risk than broad-market index funds, so the framework is best suited to investors with an established savings foundation or a specific need for current income.

02

Design for Sustainable Wealth

 Primary Optimization Objective

Long-term cumulative return is the primary optimization target. Income is valuable only when achieved while minimizing opportunity cost, avoiding structural NAV erosion, and remaining competitive with broad-market alternatives.

03

Distinguish Sustainable from Destructive ROC

 Distribution Sustainability Standard

Return of capital (ROC) can have two very different meanings. Some ETFs classify part or all of a distribution as ROC for tax purposes even when portfolio income is sufficient to support the payment, potentially deferring taxes by reducing the investor's cost basis. Other high-distribution ETFs pay more than their strategy sustainably earns and effectively return invested capital, often accompanied by long-term NAV erosion. Design for Wealth™ evaluates ROC using distribution coverage, NAV trajectory, and cumulative total return. Destructive ROC fails the Design for Sustainable Wealth principle regardless of headline yield.

04

Design for Reliable Monthly Income

 Cash-Flow Requirement

The portfolio targets recurring cash distributions of at least 10% annually. During accumulation, distributions are reinvested through DRIP. During an income phase, they may instead provide recurring cash flow without requiring routine asset sales.

05

Design for Efficient Option Income

 Option-Structure Standard

Option-income strategies are not economically equivalent. The framework favors structures that preserve more of the underlying equity upside, while traditional covered-call strategies face a higher hurdle because their income is generated by surrendering part of that upside. Put-spread, partial-call, and other dynamic overlays are evaluated on their actual total-return trade-offs rather than strategy labels.

06

Require Objective Evidence

 Validation Standard

Investment decisions are supported by three complementary models: actual observed performance, 15-year historical strategy reconstruction, and forward-looking scenarios with Monte Carlo analysis. Marketing claims, headline yield, and any single forecast are not sufficient evidence. Model results are then considered with issuer quality and portfolio fit.

07

Diversify Independent Sources of Return

 Portfolio Architecture

Diversification extends beyond individual holdings. The portfolio combines complementary market exposures, issuers, investment methodologies, and option strategies to reduce dependence on any single source of return.

08

Allocate Capital by Evidence

 Position-Sizing Standard

Position size reflects the strength of supporting evidence rather than equal weighting. New or less-proven strategies begin with smaller allocations and may earn larger positions as performance, execution, liquidity, and operating history strengthen the case.

09

Continuously Improve the Design

 Engineering Review Cycle

Portfolio design is iterative. Holdings are reconsidered when meaningful new evidence changes expected outcomes, not as a reaction to ordinary short-term market volatility.

Portfolio Design Standards

Prefer diversified index-based or multi-company strategies over concentrated single-stock exposure.

Evaluate distribution sustainability using total return, NAV behavior, and distribution coverage rather than headline yield alone.

Validate strategies through all three models rather than relying on any single historical period, proxy, or forecast.

Introduce experimental strategies with limited allocations until sufficient evidence supports expansion.

Prefer managers who prioritize long-term capital appreciation alongside income generation.

Combine complementary income methodologies rather than relying on a single option strategy.

Consider tax efficiency when economically justified without confusing tax classification with economic sustainability.

Design portfolios to remain mathematically and psychologically durable during severe market declines.

Design Philosophy

A portfolio should not be judged by its current yield. It should be judged by its ability to generate sustainable income with the lowest practical opportunity cost.

Portfolio Design Methodology

Design for Wealth™ uses a structured process to identify high-income ETFs that can support the Design Objective. ETFs are screened, their strategies are understood, and their performance is evaluated through three complementary models. The resulting evidence informs qualification, classification, portfolio role, and allocation, but rank alone does not determine any of those decisions.

Seven-stage Design for Wealth portfolio design methodology, including each stage's evaluation process and primary output.
No. Methodology Stage Process & Evaluation Standard Primary Output
01 Identify ETFs for Evaluation Screen diversified high-income ETFs capable of producing meaningful recurring distributions. Single-stock, cryptocurrency, and narrowly concentrated strategies are generally excluded from consideration as structural Core Holdings. Defined set of high-income ETFs and passive benchmarks for analysis.
02 Understand the Strategy Separate each ETF into its underlying investment exposure and income-generation methodology. Evaluate security selection, option structure, coverage, manager discretion, distribution objective, and commitment to capital appreciation. Standardized strategy profile for meaningful comparison across funds.
03 Validate Through Three Models Evaluate each ETF through three complementary analytical perspectives: Model 1: Actual observed cumulative return and opportunity-cost analysis. Model 2: 15-year historical strategy reconstruction and relative performance rank. Model 3: Forward-looking 15-year structural scenarios and Monte Carlo analysis. Three-model evidence profile combining actual results, reconstructed historical behavior, and performance across plausible future conditions.
04 Evaluate Issuer and Execution Quality Assess the people and organizations responsible for executing each strategy. Consider relevant experience, professional credentials, transparency, operational capability, liquidity, assets under management, sponsor commitment, and demonstrated execution. Issuer and management confidence assessment.
05 Classify and Assign a Portfolio Role Classify each qualifying ETF as Core, Diversification, or Experimental based on the strength and purpose of the supporting evidence. Then define the market exposure, income methodology, diversification benefit, and risk function it contributes. Portfolio classification and defined functional role.
06 Allocate Capital by Evidence Position size reflects the strength of the supporting evidence rather than equal weighting. Core status does not imply equal conviction or equal position size. New or less-proven strategies begin smaller and may earn larger allocations as performance, execution, liquidity, and operating history strengthen the case. Evidence-weighted portfolio allocation.
07 Review and Improve the Design Compare actual outcomes with expectations. Review cumulative return, opportunity cost, benchmark-relative performance, NAV behavior, distributions, manager execution, strategy changes, AUM, and market-stress behavior. Revise the design only when meaningful new evidence supports a change. Documented continuous-improvement cycle.
01
Identify ETFs for Evaluation
Process & Evaluation Standard Screen diversified high-income ETFs capable of producing meaningful recurring distributions. Single-stock, cryptocurrency, and narrowly concentrated strategies are generally excluded from consideration as structural Core Holdings.
Primary Output Defined set of high-income ETFs and passive benchmarks for analysis.
02
Understand the Strategy
Process & Evaluation Standard Separate each ETF into its underlying investment exposure and income-generation methodology. Evaluate security selection, option structure, coverage, manager discretion, distribution objective, and commitment to capital appreciation.
Primary Output Standardized strategy profile for meaningful comparison across funds.
03
Validate Through Three Models
Process & Evaluation Standard Evaluate each ETF through three complementary analytical perspectives: Model 1: Actual observed cumulative return and opportunity-cost analysis. Model 2: 15-year historical strategy reconstruction and relative performance rank. Model 3: Forward-looking 15-year structural scenarios and Monte Carlo analysis.
Primary Output Three-model evidence profile combining actual results, reconstructed historical behavior, and performance across plausible future conditions.
04
Evaluate Issuer and Execution Quality
Process & Evaluation Standard Assess the people and organizations responsible for executing each strategy. Consider relevant experience, professional credentials, transparency, operational capability, liquidity, assets under management, sponsor commitment, and demonstrated execution.
Primary Output Issuer and management confidence assessment.
05
Classify and Assign a Portfolio Role
Process & Evaluation Standard Classify each qualifying ETF as Core, Diversification, or Experimental based on the strength and purpose of the supporting evidence. Then define the market exposure, income methodology, diversification benefit, and risk function it contributes.
Primary Output Portfolio classification and defined functional role.
06
Allocate Capital by Evidence
Process & Evaluation Standard Position size reflects the strength of the supporting evidence rather than equal weighting. Core status does not imply equal conviction or equal position size. New or less-proven strategies begin smaller and may earn larger allocations as performance, execution, liquidity, and operating history strengthen the case.
Primary Output Evidence-weighted portfolio allocation.
07
Review and Improve the Design
Process & Evaluation Standard Compare actual outcomes with expectations. Review cumulative return, opportunity cost, benchmark-relative performance, NAV behavior, distributions, manager execution, strategy changes, AUM, and market-stress behavior. Revise the design only when meaningful new evidence supports a change.
Primary Output Documented continuous-improvement cycle.
Core Qualification Standard

Core status requires strong evidence from all three models and cannot be earned by ranking alone. A Core Holding must also demonstrate competitive long-term total-return potential relative to appropriate alternatives, acceptable NAV behavior, a sound and repeatable methodology, credible execution, and a clear portfolio role. No model may reveal a material unresolved weakness.

Simulation Models

Model 1: Actual Cumulative Return & Opportunity-Cost Analysis

Actual cumulative total return with distributions reinvested, including the return advantage or opportunity cost produced by owning each ETF instead of VOO and its designated passive benchmark. Full-period results cover February 1, 2024 through August 3, 2026.

High-dividend and option-income ETF Cumulative total return scale for the common-period table Opportunity-cost comparison scale Published distribution rates and AUM are shown as of August 2026 and are contextual only.

pp = percentage points. Positive comparison values indicate a return advantage; negative values quantify the opportunity cost.

Full analytical tables shown below. Desktop viewing is recommended for detailed review.

Full-Period Comparable Funds

All funds were measured over the same February 1, 2024 through August 3, 2026 period using directly observed total-return data. Evidence Confidence is High for every comparison in this section.

Full-period ETF cumulative-return and opportunity-cost comparisons for February 1, 2024 through August 3, 2026.
Ticker ETF Name Cumulative
Total Return		 Published
Distribution Rate		 Opportunity-Cost
Comparison vs. VOO
(pp)		 Designated
Benchmark		 Benchmark Type Benchmark
Cumulative Return		 Opportunity-Cost Comparison
vs. Designated Benchmark
(pp)		 AUM Evidence
Confidence
VOOG Vanguard S&P 500 Growth ETF 78.59% 0.43% +18.79 pp N/A N/A N/A N/A $26.3 billion High
OVL Overlay Shares Large Cap Equity ETF 65.03% 10.46% +5.23 pp VOO Broad S&P 500 opportunity-cost benchmark 59.80% +5.23 pp $347.1 million High
VUG Vanguard Morningstar Growth ETF 63.80% 0.36% +4.00 pp N/A N/A N/A N/A $223.2 billion High
SCHG Schwab U.S. Large-Cap Growth ETF 62.99% 0.39% +3.19 pp N/A N/A N/A N/A $59.8 billion High
GPIQ Goldman Sachs Nasdaq-100 Premium Income ETF 62.09% 10.50% +2.29 pp QQQ Nasdaq-100 growth benchmark 68.17% −6.08 pp $5.0 billion High
OVF Overlay Shares Foreign Equity ETF 60.99% 10.38% +1.19 pp VXUS International-equity benchmark 58.86% +2.13 pp $49.2 million High
VOO Vanguard S&P 500 ETF 59.80% 1.00% 0.00 pp N/A N/A N/A N/A $979.0 billion High
SCHK Schwab 1000 Index ETF 59.21% 1.03% −0.59 pp N/A N/A N/A N/A $5.7 billion High
VXUS Vanguard Total International Stock ETF 58.86% 2.52% −0.94 pp N/A N/A N/A N/A $156.5 billion High
VTI Vanguard Morningstar Total Stock Market ETF 58.70% 1.03% −1.10 pp N/A N/A N/A N/A $660.7 billion High
QQQI NEOS Nasdaq-100 High Income ETF 55.17% 14.01% −4.63 pp QQQ Nasdaq-100 growth benchmark 68.17% −13.00 pp $13.2 billion High
OVS Overlay Shares Small Cap Equity ETF 50.86% 10.52% −8.94 pp IJR S&P SmallCap 600 small-cap benchmark 46.37% +4.49 pp $35.2 million High
JEPQ JPMorgan Nasdaq Equity Premium Income ETF 48.92% 10.45% −10.88 pp QQQ Nasdaq-100 growth benchmark 68.17% −19.25 pp $40.3 billion High
SPYI NEOS S&P 500 High Income ETF 47.67% 12.04% −12.13 pp VOO Broad S&P 500 opportunity-cost benchmark 59.80% −12.13 pp $10.9 billion High
DIA State Street SPDR Dow Jones Industrial Average ETF Trust 43.51% 1.34% −16.29 pp N/A N/A N/A N/A $45.2 billion High
JEPI JPMorgan Equity Premium Income ETF 24.26% 8.40% −35.54 pp VOO Broad S&P 500 opportunity-cost benchmark 59.80% −35.54 pp $45.0 billion High

Funds with Shorter Operating Histories

Each result begins at the fund's calculation start and ends August 3, 2026. Because operating histories differ, cumulative returns should not be compared directly across rows. The opportunity-cost comparison uses each fund's designated benchmark over the same fund-specific period.

Shorter-history ETF cumulative-return and designated-benchmark opportunity-cost comparisons ending August 3, 2026.
Ticker ETF Name Cumulative
Total Return		 Published
Distribution Rate		 Designated
Benchmark		 Benchmark Type Benchmark
Cumulative Return		 Opportunity-Cost Comparison
vs. Designated Benchmark
(pp)		 AUM Calculation
Start		 Observation
Years		 Evidence
Confidence
KQQQ Kurv Technology Titans Select ETF 46.84% 14.50% QQQ Nasdaq-100 growth benchmark 46.67% +0.17 pp $127.8 million 07/23/2024 2.03 High
QDVO Amplify CWP Growth & Income ETF 44.84% 10.97% SCHG Large-cap growth benchmark 39.12% +5.72 pp $726.2 million 08/22/2024 1.95 High
OMAH VistaShares Target 15 Berkshire Select Income ETF 18.30% 15.00% BRK-B Approximate Berkshire-oriented proxy −2.95% +21.25 pp $992.8 million 03/20/2025 1.37 Moderate
CHPY YieldMax Semiconductor Portfolio Option Income ETF 130.74% 40.72% SMH Semiconductor-sector benchmark 154.78% −24.04 pp $1.1 billion 04/02/2025 1.34 Moderate
QUSA VistaShares Target 15 USA Quality Income ETF 5.76% 15.00% QUAL U.S. quality-factor benchmark 25.35% −19.59 pp $21.5 million 05/20/2025 1.20 Moderate
TDAQ TappAlpha Innovation 100 Growth & Daily Income ETF 22.96% 17.10% QQQ Nasdaq-100 growth benchmark 22.29% +0.67 pp $267.3 million 09/04/2025 0.91 Low
ODTE VegaShares SPX NDX RTY Premium Income ETF 8.34% 15.00% Approx. 1/3 VOO + 1/3 QQQ + 1/3 VTWO Constructed S&P 500, Nasdaq-100, and Russell 2000 benchmark 17.95% −9.61 pp $3.1 million 04/02/2026 0.34 Low
DRMY XFUNDS Memory Income ETF −3.08% 34.5% SOXX Broad semiconductor proxy; a memory-specific benchmark is preferred for future review −4.30% +1.22 pp $2.2 million 07/16/2026 0.05 Low
Calculation workbook: Download the Model 1 calculations, benchmark comparisons, source data, and evidence notes (.xlsx)
Model interpretation: Cumulative total return includes reinvested distributions. Each opportunity-cost comparison equals the ETF's cumulative total return minus the return of VOO or the designated passive benchmark over the same measurement period. Positive values indicate a return advantage; negative values quantify the opportunity cost. Evidence Confidence reflects the reliability of the comparison based on operating history and benchmark fit. Distribution rates and AUM do not enter the return calculations. Returns and rankings are presented on a pre-tax basis. Investor-specific tax effects, including differences between currently taxable distributions and return of capital, are evaluated separately in the Tax Trade-Off Analysis.

Model 2: 15-Year Historical Strategy Reconstruction & Relative Performance Rank

Strategy-proxy model comparing how each fund's underlying market exposure and investment or income methodology might have behaved over the 15-year period ending June 30, 2026. Central assumptions are informed by long-history references, live-period evidence, strategy evidence, and fund expenses. The period included exceptional U.S. large-cap growth and technology performance, so these rankings describe that historical environment rather than expected future leadership.

High-dividend and option-income ETFs are highlighted in yellow. Rank 1 represents the highest modeled 15-year cumulative return. Blue figures are model inputs; negative strategy effects use a minus sign and appear in red. Published distribution rates are shown as of August 2026. They are contextual only and are not used to calculate modeled total return.

Full analytical model shown below. Desktop viewing is recommended for detailed review.

Comparison of 24 ETFs using a 15-year historical strategy reconstruction ending June 30, 2026.
Ticker ETF Name 15-Year
Cumulative
Return Rank		 Modeled Net
CAGR		 Estimated Net
Strategy Effect		 Key Analytical
Rationale		 Historical Exposure
Reference		 Underlying Historical
CAGR Assumption		 Investment & Income/Option
Methodology		 Published Distribution
Rate		 Expense
Ratio		 Evidence
Confidence
KQQQ Kurv Technology Titans Select ETF 1 17.51% −1.0% Live NAV trailed NDX by only ~0.95 pp; after adding back the 0.99% expense ratio, the observed gross residual is near zero. The −1.0% modeled strategy effect applies a conservative allowance relative to the live evidence. QQQ / Nasdaq-100 reference for concentrated tech 19.5% Concentrated portfolio of large technology/innovation companies with selective option writing on portfolio holdings to generate income while retaining substantial equity participation. 14.50% 0.99% Low
GPIQ Goldman Sachs Nasdaq-100 Premium Income ETF 2 17.21% −2.0% Nasdaq-100 evidence raises the historical equity assumption materially; partial/dynamic calls justify a smaller drag than fuller overwrite strategies. QQQ / Nasdaq-100 19.5% Nasdaq-100-oriented equity exposure with a dynamically managed partial call-writing overlay intended to generate premium income while preserving meaningful upside participation. 10.50% 0.29% Moderate
VOOG Vanguard S&P 500 Growth ETF 3 16.93% 0.0% Near-full 15-year issuer history makes the central growth assumption comparatively well anchored. Direct fund history 17.0% Passive exposure to the S&P 500 Growth Index; no option overlay. 0.43% 0.07% High
SCHG Schwab U.S. Large-Cap Growth ETF 4 16.66% 0.0% Passive exact-style proxy; central reflects observed SCHG growth history with proxy-fee normalization and rounding. Direct fund history 16.7% Passive exposure to the Dow Jones U.S. Large-Cap Growth Total Stock Market Index; no option overlay. 0.39% 0.04% High
JEPQ JPMorgan Nasdaq Equity Premium Income ETF 5 16.15% −3.0% Nasdaq-100 reference is strong, but active stock selection plus ELN calls contaminate residuals; retain a material call drag with moderate confidence. QQQ / Nasdaq-100 reference 19.5% Actively selected Nasdaq-oriented large-cap growth equities combined with equity-linked notes that embed one-month out-of-the-money call exposure. 10.45% 0.35% Moderate
VUG Vanguard Morningstar Growth ETF 6 15.47% 0.0% Passive exact fund proxy; benchmark changed historically, so range allows modest reconstruction uncertainty. Direct fund history 15.5% Passive exposure to the Morningstar US Large-Mid Cap Broad Growth Index; no option overlay. 0.36% 0.03% High
QDVO Amplify CWP Growth & Income ETF 7 15.44% −1.0% VOOG is a defensible growth proxy, but active stock selection and tactical partial calls make the strategy effect uncertain. VOOG / S&P 500 Growth proxy for active growth book 17.0% Actively selected large-cap growth equities with tactical covered calls written on only a portion of selected holdings, seeking income while retaining substantial upside participation. 10.97% 0.56% Low
OVL Overlay Shares Large Cap Equity ETF 8 15.21% +1.2% Direct 6.75-year live evidence shows +0.85 pp net residual vs S&P; adding back fund expense supports a positive gross overlay effect. Central +1.2% is below the live +1.64% gross residual. VOO / S&P 500 equity beta 14.8% Broad U.S. large-cap equity exposure combined with an actively managed S&P 500/SPX put-spread overlay designed to monetize option premium while preserving equity upside. 10.46% 0.79% Moderate
QQQI NEOS Nasdaq-100 High Income ETF 9 14.82% −4.0% Nasdaq-100 exposure is clear; high-income NDX call writing should create meaningful upside drag in the 2011–2026 bull regime. Short live history limits precision. QQQ / Nasdaq-100 19.5% Nasdaq-100 equity exposure combined with a tax-aware NDX call strategy that sells calls and may purchase calls to manage upside participation and income. 14.01% 0.68% Low
VOO Vanguard S&P 500 ETF 10 14.77% 0.0% Direct passive S&P 500 proxy with very low fee; one of the strongest historical inputs. Direct fund history 14.8% Passive exposure to the S&P 500 Index; no option overlay. 1.00% 0.03% High
SCHK Schwab 1000 Index ETF 11 14.67% 0.0% IWB/Russell 1000 is a close long-history proxy, but not the exact Schwab 1000 methodology. IWB / Russell 1000 proxy for Schwab 1000 14.7% Passive exposure to the Schwab 1000 Index, representing approximately the largest 1,000 U.S. companies; no option overlay. 1.03% 0.03% Moderate
VTI Vanguard Morningstar Total Stock Market ETF 12 14.47% 0.0% Direct total-market proxy; approximate 15-year evidence supports a central slightly below S&P 500. Direct fund history 14.5% Passive exposure to the Morningstar US Total Market Index; no option overlay. 1.03% 0.03% High
TDAQ TappAlpha Innovation 100 Growth & Daily Income ETF 13 13.67% −5.0% Nasdaq-100 proxy is strong, but daily/0DTE-style call resetting lacks a defensible long-history analogue. Central assumes substantial bull-market upside truncation. QQQ / Nasdaq-100 19.5% Nasdaq-100/innovation-oriented equity exposure combined with daily short-duration call writing intended to generate recurring option income. 17.10% 0.83% Low
DIA State Street SPDR Dow Jones Industrial Average ETF Trust 14 12.54% 0.0% Direct passive DJIA proxy; central reflects the available long-history range and current fee. Direct fund history 12.7% Passive price-weighted exposure to the 30-stock Dow Jones Industrial Average; no option overlay. 1.34% 0.16% High
DRMY XFUNDS Memory Income ETF 15 12.49% −4.5% Broad semiconductor proxy is only an approximation for memory stocks and the multi-leg option structure has essentially no representative live history. Wide range required. SOXX / broad semiconductor proxy 18.0% Actively selected memory-semiconductor equities combined with sold and purchased options/spreads intended to generate income alongside capital appreciation. 34.5% 1.01% Low
QUSA VistaShares Target 15 USA Quality Income ETF 16 11.95% −2.0% Quality proxy is reasonable but not exact; Target 15 covered-call overlay is very young, so strategy drag is judgment-based with a wide range. QUAL / U.S. quality factor (S&P 500 cross-check) 14.9% Rules-based U.S. quality equity selection combined with out-of-the-money covered calls under a Target 15 income framework. 15.00% 0.95% Low
SPYI NEOS S&P 500 High Income ETF 17 11.42% −2.7% Live fund materially lagged S&P in the bull period; adding back expense implies a strategy drag around the high-2% range. BXM provides directional confirmation but is structurally more aggressive. VOO / S&P 500 14.8% S&P 500 equity exposure combined with a tax-aware SPX call strategy that sells calls and may purchase calls to retain part of the market's upside. 12.04% 0.68% Moderate
OVS Overlay Shares Small Cap Equity ETF 18 11.17% +1.0% Equity beta is S&P SmallCap 600 while puts are SPX-family. Same put-spread concept as OVL supports a positive central overlay effect, discounted for cross-index basis risk. IJR / S&P SmallCap 600 equity beta; SPX option overlay 11.0% S&P SmallCap 600/IJR-type small-cap equity exposure combined with a separate S&P 500/SPX put-spread overlay; the equity and option reference markets are different. 10.52% 0.83% Moderate
CHPY YieldMax Semiconductor Portfolio Option Income ETF 19 10.97% −6.0% Semiconductor proxy is broad and option program is aggressive; high distribution is not treated as return, but structure supports a large uncertain drag. SOXX / broad semiconductor proxy 18.0% Actively selected semiconductor equities combined with an aggressive option-income program on portfolio holdings. 40.72% 1.03% Low
ODTE VegaShares SPX NDX RTY Premium Income ETF 20 10.74% −3.5% Equity exposure is approximated using S&P 500, Nasdaq-100 and Russell 2000 (VTWO) references. 0DTE calls lack long-history analogues, so the strategy effect remains low-confidence. ~1/3 VOO + ~1/3 QQQ + ~1/3 VTWO (Russell 2000) 15.0% Approximately one-third S&P 500, Nasdaq-100 and Russell 2000 equity exposure with short-dated SPX, NDX and RUT call writing and frequent resets. 15.00% 0.76% Low
OMAH VistaShares Target 15 Berkshire Select Income ETF 21 10.55% −2.0% BRK.B-centered proxy captures the architecture imperfectly; Target 15 option overlay is too young for validation, so central drag remains modest but uncertain. BRK.B-centered proxy for Berkshire-select equity book 13.5% Berkshire Hathaway-centered equity architecture combined with a Target 15 option-income overlay on the Berkshire-select portfolio. 15.00% 0.95% Low
JEPI JPMorgan Equity Premium Income ETF 22 10.45% −4.0% Using S&P 500 as the reference requires the strategy-effect term to absorb both lower-vol active equity selection and ELN call overwrite. Longer live history helps, but decomposition remains uncertain. VOO / S&P 500 reference; active lower-vol equity differs 14.8% Actively selected lower-volatility U.S. large-cap equities combined with ELNs that embed one-month out-of-the-money S&P 500 call exposure. 8.40% 0.35% Moderate
OVF Overlay Shares Foreign Equity ETF 23 8.17% +1.0% VEA is used as the developed-market proxy; issuer methodology identifies SPX/SPXW put spreads despite the fund’s foreign equity beta. The positive overlay effect is informed by OVL/OVS evidence, with an allowance for cross-index uncertainty. VEA / developed ex-U.S.; minority emerging markets; SPX option overlay 8.0% Developed ex-U.S. equity exposure, with minority emerging-markets exposure, combined with an S&P 500/SPX-SPXW put-spread overlay. 10.38% 0.83% Moderate
VXUS Vanguard Total International Stock ETF 24 6.65% 0.0% Direct passive ex-U.S. history from inception is close to the full 15-year window; strongest international control case. Direct fund history 6.7% Passive broad developed- and emerging-market equity exposure outside the United States; no option overlay. 2.52% 0.05% High
Calculation workbook: Download the Model 2 calculations, sensitivity analysis, live-validation evidence, and source data (.xlsx)
Model interpretation: Modeled net CAGR equals the underlying historical CAGR assumption plus the estimated net strategy effect, less the fund expense ratio. For actively managed funds, the strategy-effect term may include both security selection and option implementation relative to the historical reference. Historical exposure CAGR assumptions are stated before the expense ratio shown in the table. When direct fund history is used, the observed return is normalized so the expense ratio is not counted twice. The Key Analytical Rationale summarizes the principal evidence and judgment supporting each reconstruction, including relevant live-period evidence where available, reference quality, strategy structure, and important limitations. The cumulative-return rank is determined from the resulting 15-year compounded outcome. Rankings are assumption-driven analytical comparisons rather than forecasts or guarantees of future performance. Evidence Confidence applies specifically to this historical reconstruction and may differ from the confidence assigned in another model. Returns and rankings are presented on a pre-tax basis. Investor-specific tax effects, including differences between currently taxable distributions and return of capital, are evaluated separately in the Tax Trade-Off Analysis.

Model 3: 15-Year Forward-Looking Scenarios & Monte Carlo Analysis

Forward-looking comparison of 24 ETFs across three coherent economic regimes, supported by a 50,000-path joint Monte Carlo rank-stability analysis. Base Rank is the primary deterministic comparison; detailed Monte Carlo outputs are provided in the downloadable workbook, and published distribution rates reflect the August 2026 model snapshot.

Base U.S. Reindustrialization & Broad-Based Expansion

Continued mega-cap strength combines with reshoring, strategic domestic investment, expanded power infrastructure, and growth among smaller U.S. suppliers.

Conservative Capex Overbuild & Valuation Compression

Industrial and AI investment continues, but overcapacity, depreciation, elevated costs, or lower stock valuations reduce shareholder returns. In practical terms, international equities outperform the broad U.S. market, while defensive and value-oriented U.S. strategies outperform U.S. growth.

Upside AI Productivity & Strategic-Moat Reinforcement

AI and industrial capital investment generate high utilization, productivity growth, successful reshoring, and stronger competitive advantages for leading U.S. companies.

These scenarios are coherent economic regimes used to test the sensitivity of relative ETF rankings; they are not claims that any specific future outcome is known. Monte Carlo also assigns a separate 15% chance to a future in which international stocks, starting from lower prices relative to earnings, outperform U.S. stocks. Detailed rank-stability results remain in the downloadable workbook.

High-dividend and option-income ETFs are highlighted in yellow. Rank 1 represents the highest modeled 15-year cumulative return; lower numerical ranks are better. Blue figures are model inputs; negative strategy effects use a minus sign and appear in red. Published distribution rate is contextual only and is not used to calculate modeled total return.

Full analytical model shown below. Desktop viewing is recommended for detailed review.

Ticker ETF Name Base
15-Year
Rank		 Base
Modeled Net
CAGR		 Conservative
15-Year
Rank		 Upside
15-Year
Rank		 Estimated Net
Strategy Effect
— Base		 Key Analytical
Rationale		 Forward
Exposure / Index		 Base Underlying
CAGR
Assumption		 Investment &
Income/Option
Methodology		 Published
Distribution
Rate		 Expense
Ratio		 Evidence
Confidence
OVS Overlay Shares Small Cap Equity ETF 1 7.27% 13 7 +0.5% Domestic small companies can participate as suppliers and service providers to reshoring, grid and industrial investment. The SPX put-spread overlay may add premium, but the small-cap equity/SPX option mismatch creates wide rank uncertainty. S&P SmallCap 600 / IJR-type equity exposure 7.6% S&P SmallCap 600-oriented equity exposure combined with actively managed SPX-family put spreads. 10.52% 0.83% Moderate
VUG Vanguard Morningstar Growth ETF 2 7.17% 14 1 0.0% Base assumptions allow U.S. large-growth leaders to keep compounding through AI investment, reshoring and productivity gains without extrapolating the exceptional prior 15-year return. The conservative regime explicitly tests valuation compression. Morningstar US Large-Mid Cap Broad Growth Index 7.2% Passive exposure to the Morningstar US Large-Mid Cap Broad Growth Index; no option overlay. 0.36% 0.03% High
SCHG Schwab U.S. Large-Cap Growth ETF 3 7.16% 15 2 0.0% Broad large-cap growth exposure is positioned to benefit from continued U.S. technology leadership and capital investment. Its lower expense ratio slightly improves modeled results relative to otherwise similar growth exposure. Dow Jones U.S. Large-Cap Growth Total Stock Market Index 7.2% Passive exposure to the Dow Jones U.S. Large-Cap Growth Total Stock Market Index; no option overlay. 0.39% 0.04% High
DIA State Street SPDR Dow Jones Industrial Average ETF Trust 4 7.14% 2 11 0.0% The Dow's value, industrial and quality characteristics are positioned to benefit from manufacturing, infrastructure and grid investment. Its concentrated price-weighted structure limits how fully it captures the broader opportunity. Dow Jones Industrial Average 7.3% Passive price-weighted exposure to the 30-stock Dow Jones Industrial Average; no option overlay. 1.34% 0.16% High
VOOG Vanguard S&P 500 Growth ETF 5 7.13% 16 3 0.0% S&P 500 growth exposure captures profitable mega-cap platforms and other growth leaders. The Base and Upside regimes reflect potential benefits from reindustrialization, AI investment, and productivity growth, while the Conservative regime tests substantial valuation compression. S&P 500 Growth Index 7.2% Passive exposure to the S&P 500 Growth Index; no option overlay. 0.43% 0.07% High
VTI Vanguard Morningstar Total Stock Market ETF 6 7.03% 10 4 0.0% Total-market exposure most directly combines mega-cap platforms with mid- and small-company beneficiaries of reshoring, power demand and domestic supply-chain investment. It is therefore a broad expression of the central U.S. expansion regime. Morningstar US Total Market Index 7.1% Passive broad U.S. total-market exposure; no option overlay. 1.03% 0.03% High
SCHK Schwab 1000 Index ETF 6 7.03% 10 4 0.0% Broad U.S. exposure combines large-company leadership with smaller domestic beneficiaries. Its modeled inputs and expense ratio are identical to VTI, producing an exact analytical tie. Schwab 1000 Index 7.1% Passive exposure to the Schwab 1000 Index; no option overlay. 1.03% 0.03% High
OVL Overlay Shares Large Cap Equity ETF 8 7.01% 8 9 +0.8% S&P 500 exposure participates in U.S. large-cap leadership while the SPX put-spread overlay may harvest volatility premium. The Base regime applies a modest positive overlay effect, with a smaller assumed benefit in the strongest equity regime. S&P 500 / VOO-type equity exposure 7.0% S&P 500-oriented equity exposure combined with actively managed SPX put spreads. 10.46% 0.79% Moderate
VOO Vanguard S&P 500 ETF 9 6.97% 9 6 0.0% The S&P 500 captures the best-capitalized U.S. platforms and major industrial beneficiaries of domestic investment. The Base regime assumes continued participation in broad U.S. expansion, while the Conservative regime tests the effect of valuation compression. S&P 500 Index 7.0% Passive exposure to the S&P 500 Index; no option overlay. 1.00% 0.03% High
GPIQ Goldman Sachs Nasdaq-100 Premium Income ETF 10 6.31% 21 8 −1.0% Nasdaq-oriented exposure benefits from the AI and capital-investment thesis. Recent partial call coverage supports more upside participation than a full overwrite, although the overlay still creates increasing drag in stronger markets. Nasdaq-100-oriented equity exposure 7.6% Nasdaq-100-oriented equity exposure with a dynamically managed partial call overlay. 10.50% 0.29% Moderate
QDVO Amplify CWP Growth & Income ETF 11 6.14% 20 12 −0.5% Active growth selection and partial calls may preserve more upside than a full overwrite. The U.S. growth thesis improves the underlying outlook, but short history and inseparable stock-selection/option effects keep confidence low. Actively selected large-cap growth equities 7.2% Actively selected large-cap growth equities combined with tactical, partial call writing on portfolio holdings. 10.97% 0.56% Low
KQQQ Kurv Technology Titans Select ETF 12 6.11% 22 10 −0.5% Concentrated technology exposure benefits from stronger U.S. growth assumptions, while selective calls impose more drag in sustained upside. Short history, concentration and active selection create a wide simulated rank range. Concentrated technology/innovation portfolio 7.6% Concentrated active technology/innovation portfolio with selective option writing on holdings. 14.50% 0.99% Low
VXUS Vanguard Total International Stock ETF 13 6.05% 1 14 0.0% International equities may benefit from lower starting valuations and favorable global investment conditions. The Monte Carlo analysis includes a separate regime in which international stocks catch up with and outperform U.S. stocks, while the Base regime assumes stronger relative support from U.S. reindustrialization and broad domestic expansion. FTSE Global All Cap ex US Index 6.1% Passive developed- and emerging-market equity exposure outside the United States; no option overlay. 2.52% 0.05% High
JEPQ JPMorgan Nasdaq Equity Premium Income ETF 14 5.75% 18 13 −1.5% Active Nasdaq-oriented stock selection and ELN calls benefit from the U.S. technology thesis but cannot be cleanly decomposed. The strategy is expected to hold up better in weak markets and surrender more upside in strong ones. Actively selected Nasdaq-oriented equities 7.6% Actively selected Nasdaq-oriented equities combined with ELNs embedding one-month out-of-the-money Nasdaq-100 calls. 10.45% 0.35% Moderate
OVF Overlay Shares Foreign Equity ETF 15 5.67% 4 16 +0.4% International equities retain a meaningful catch-up case, while SPX put spreads may add premium. Currency exposure and the foreign-equity/SPX option mismatch produce substantial basis risk and a wide Monte Carlo range. Developed ex-U.S. with minority emerging-markets exposure 6.1% Developed and emerging ex-U.S. equity exposure combined with actively managed SPX/SPXW put spreads. 10.38% 0.83% Moderate
JEPI JPMorgan Equity Premium Income ETF 16 5.65% 5 18 −1.0% Lower-volatility quality/value selection and ELN calls are aligned with the conservative regime and can benefit from industrial broadening. The same defensive structure is expected to lag in the strongest growth-led outcome. Actively selected lower-volatility U.S. large-cap equities 7.0% Actively selected lower-volatility U.S. large-cap equities combined with ELNs embedding S&P 500 call exposure. 8.40% 0.35% Moderate
OMAH VistaShares Target 15 Berkshire Select Income ETF 17 5.50% 3 22 −0.8% Berkshire-inspired quality and value holdings align with industrial, energy and infrastructure expansion. The young fund's equity selection and option overlay cannot be separated reliably, so its conservative strength comes with a very wide simulated range. BRK.B plus Berkshire's largest disclosed holdings 7.2% Berkshire Hathaway and Berkshire-inspired holdings combined with a dynamically managed options portfolio targeting income. 15.00% 0.95% Low
DRMY XFUNDS Memory Income ETF 18 5.49% 24 15 −2.0% Domestic chip investment and AI demand increase memory-sector upside, while a near-flat conservative assumption reflects cyclicality and execution risk. Concentration and complex option spreads generate extreme favorable and unfavorable rank outcomes. 8–15 memory-related semiconductor equities 8.5% Concentrated memory-semiconductor equity portfolio combined with synthetic covered calls and credit call/put spreads. N/A 1.01% Low
QUSA VistaShares Target 15 USA Quality Income ETF 19 5.45% 6 21 −0.8% Quality companies can benefit from domestic investment and strong balance sheets, while options provide income and downside support. The fund's short record and combined selection/option effect justify low confidence and wide uncertainty. U.S. quality equity selection 7.2% Rules-based U.S. quality equity selection combined with out-of-the-money covered-call and option positions. 15.00% 0.95% Low
CHPY YieldMax Semiconductor Portfolio Option Income ETF 20 5.27% 23 19 −2.5% Semiconductor reshoring and AI infrastructure support stronger Base and Upside return assumptions for the sector. High concentration, cyclicality and aggressive call spreads produce an unusually wide distribution of possible ranks. 15–30 semiconductor companies 8.8% Concentrated semiconductor equity portfolio combined with call spreads on portfolio holdings. 40.72% 1.03% Low
QQQI NEOS Nasdaq-100 High Income ETF 21 4.92% 19 17 −2.0% Nasdaq-100 exposure benefits from AI-led growth, but the high-income call-spread objective creates meaningful upside monetization. A broad strategy-effect range reflects execution and market-path uncertainty. Nasdaq-100 Index 7.6% Nasdaq-100 equity exposure combined with tax-aware sold and purchased NDX call options. 14.01% 0.68% Moderate
ODTE VegaShares SPX NDX RTY Premium Income ETF 22 4.64% 12 24 −2.0% Its multi-index exposure spans large-cap, Nasdaq and small-cap beneficiaries, but short-dated calls repeatedly reset upside participation. High path dependence produces a materially weaker upside rank and wide strategy uncertainty. Approximately equal S&P 500, Nasdaq-100 and Russell 2000 exposures 7.4% Approximately equal S&P 500, Nasdaq-100 and Russell 2000 exposure combined with short-dated SPX, NDX and RUT calls. 15.00% 0.76% Low
SPYI NEOS S&P 500 High Income ETF 23 4.52% 7 23 −1.8% S&P 500 exposure participates in domestic expansion, while sold and purchased SPX calls can improve weak-market resilience. The income objective creates increasing drag in a strong, persistent equity advance. S&P 500 Index 7.0% S&P 500 equity exposure combined with tax-aware sold and purchased SPX calls. 12.04% 0.68% Moderate
TDAQ TappAlpha Innovation 100 Growth & Daily Income ETF 24 4.27% 17 20 −2.5% Nasdaq exposure participates in the AI-productivity regime, but daily short-duration calls can repeatedly cap rebounds and sustained upside. Limited live evidence and high path dependence keep the base rank low. QQQM / Nasdaq-100-oriented exposure 7.6% QQQM/Nasdaq-100-oriented exposure combined with a daily short-duration call overlay. N/A 0.83% Low
Calculation workbook: Download the complete Model 3 assumptions, calculations, simulation outputs, and source log (.xlsx)
Model interpretation: Base, Conservative, and Upside ranks are deterministic results under the three public structural regimes. Exact ties share ranks. The downloadable workbook contains the 50,000-path joint Monte Carlo Rank-Stability Analysis, which draws among four persistent structural regimes, generates correlated fat-tailed annual market outcomes, applies market-path-dependent strategy effects and fund expenses, and ranks all 24 ETFs within each common path. Keeping the Monte Carlo median, rank range, probabilities, validation tests, and reproducibility details in the workbook preserves the audit trail without overcrowding the displayed comparison. Returns and rankings are presented on a pre-tax basis. Investor-specific tax effects, including differences between currently taxable distributions and return of capital, are evaluated separately in the Tax Trade-Off Analysis.

Portfolio Design Architecture

Portfolio construction is separated into distinct decisions: determine which strategies qualify for inclusion, classify each holding according to the strength and purpose of its supporting evidence, and allocate capital within defined structural constraints.

Step 1 Evaluate the Evidence Three-model results, absolute return quality, strategy economics, issuer execution, liquidity, and portfolio fit are evaluated.
Step 2 Assign Classification Each holding is classified as Core, Diversification, or Experimental based on the strength and purpose of the supporting evidence.
Step 3 Constrain Allocation Classification determines the range of portfolio capital a strategy may receive.
Core Qualification Standard
Core status requires strong evidence from all three models and cannot be earned by ranking alone. A Core Holding must also demonstrate competitive long-term total-return potential relative to appropriate alternatives, acceptable NAV behavior, a sound and repeatable methodology, credible execution, and a clear portfolio role. No model may reveal a material unresolved weakness.
3 / 3 Models Required for
Core Qualification
Allocation Requirement

A minimum of 80% of portfolio assets should be allocated to Core Holdings. Remaining capital may be assigned to Diversification Holdings and Experimental Holdings that satisfy defined diversification, validation, or asymmetric-return objectives.

The allocation requirement does not lower the Core qualification standard. If too few funds qualify, additional funds must be evaluated rather than promoting weaker holdings merely to satisfy the allocation target.

Core classification establishes eligibility for foundational use. It does not imply equal conviction or equal position size. Allocation within Core should reflect evidence strength, liquidity, concentration, operating history, and implementation risk.

Core Holdings · 85%
Diversification Holdings · 11.5%
Experimental Holdings · 3.5%
Core Holdings
Evidence-Qualified Portfolio Foundation
85%

Strategies that satisfy the Core qualification standard through strong evidence from all three models and also demonstrate competitive total-return potential, credible methodology and execution, adequate liquidity, and a clear portfolio role.

OVL · GPIQ · QDVO · OVS
Diversification Holdings
Purposeful Diversification
11.5%

Strategies that do not satisfy every Core requirement but earn inclusion because they add meaningful, non-redundant market exposure, factor exposure, issuer or manager diversification, investment methodology, or return behavior.

OMAH · SPUC · OVF · KQQQ · QUSA
Experimental Holdings
Controlled Validation & Asymmetry
3.5%

Deliberately limited strategies included either to evaluate newer or less-validated methodologies or to capture unusually high asymmetric-return potential when the associated volatility, concentration, or execution risk warrants strict position limits.

ODTE · DRMY · CHPY
Purposeful Diversification Standard Diversification must be purposeful rather than additive. A Diversification Holding must contribute a distinct source of market exposure, investment methodology, issuer or manager diversification, or return behavior that is not already adequately represented by the Core Holdings.

Functional Portfolio Architecture

Classification determines structural status; functional categories define what each holding must contribute to the portfolio.

Functional Category Design Objective Current Allocation Current ETFs Selection Standard
Core Holdings · 85% of Portfolio
Broad-Market Income Portfolio Foundation Preserve diversified U.S. large-cap equity exposure while generating sustainable monthly income with a minimized opportunity cost in long-term cumulative return. 57.0% OVL Must provide diversified broad-market holdings, strong long-term NAV behavior, competitive cumulative return, sustainable option-income generation, and high confidence in manager execution.
Technology Growth Income Innovation Exposure Participate in long-term technology innovation and mega-cap earnings growth while converting a portion of expected return into recurring monthly income. 14.0% GPIQ Must provide diversified Nasdaq-oriented growth exposure, credible upside participation, sustainable premium generation, and strong evidence that the option methodology does not impose an excessive opportunity cost.
Large-Cap Growth Income Growth Participation Capture long-term earnings growth from high-quality large-cap companies while producing meaningful income and preserving substantial upside participation. 7.0% QDVO Must emphasize diversified growth-oriented holdings and use a selective or partial option overlay that does not unnecessarily suppress long-term capital appreciation.
Small-Cap Income Market-Capitalization Diversification Expand the portfolio beyond large-cap equities by combining long-term small-cap appreciation with income from a differentiated SPX put-spread overlay. 7.0% OVS Must provide broad small-cap exposure, a defensible SPX option-income methodology, competitive total-return potential, and meaningful diversification from large-cap and Nasdaq-focused holdings. The mismatch between small-cap equities and SPX options must remain acceptable.
Diversification Holdings · 11.5% of Portfolio
Quality & Intrinsic-Value Income Factor Diversification Add exposure to profitable, financially resilient companies and Berkshire-style intrinsic-value investing that may perform differently from technology-led growth portfolios. 6.0% OMAH · QUSA Must emphasize strong balance sheets, durable earnings, quality or value characteristics, and an income overlay designed to retain meaningful participation in the underlying holdings.
Complementary Broad-Market Income Manager and Methodology Diversification Add a second broad U.S. large-cap income implementation with an independently managed equity and option process, reducing dependence on a single broad-market premium-generation methodology. 3.0% SPUC Must preserve broad-market participation, generate meaningful recurring income, and demonstrate acceptable upside capture, NAV behavior, and execution under the current strategy before consideration for Core.
Additional Technology Diversification Manager and Methodology Diversification Supplement the primary technology allocation with a complementary manager and option structure rather than concentrating all Nasdaq exposure in one fund or premium-generation technique. 1.0% KQQQ Must provide a differentiated technology implementation, credible income generation, and a portfolio role that complements rather than merely duplicates the primary Technology Growth Income allocation.
International Income Geographic Diversification Reduce exclusive dependence on U.S. equity valuations and economic conditions through a limited allocation to broad international equities, primarily developed markets with a smaller emerging-markets component. 1.5% OVF Must provide diversified international exposure, a defensible SPX-family option overlay, competitive long-term total-return potential, and acceptable cross-index risk between foreign equities and U.S. index options.
Experimental Holdings · 3.5% of Portfolio
Broad-Index Income Innovation Methodology Validation Evaluate a specialized short-duration option methodology designed to increase cash generation while retaining diversified exposure to multiple broad U.S. equity indexes. 1.0% ODTE Broad underlying exposure is required, but allocation remains limited until the short-dated option process demonstrates consistent execution, sustainable distributions, and acceptable NAV and total-return behavior.
Speculative High-Reward Asymmetric Return Potential Maintain tightly controlled exposure to exceptionally high-income strategies positioned to benefit from favorable semiconductor, memory, and artificial-intelligence investment cycles. 2.5% DRMY · CHPY Elevated volatility, concentration, and limited operating history are permitted only when potential returns are unusually high. Position limits must prevent these strategies from threatening the portfolio's primary objectives.
Core Holdings · 85% of Portfolio
Broad-Market Income Portfolio Foundation
Design Objective Preserve diversified U.S. large-cap equity exposure while generating sustainable monthly income with a minimized opportunity cost in long-term cumulative return.
Current Allocation 57.0%
Current ETF OVL
Selection Standard Must provide diversified broad-market holdings, strong long-term NAV behavior, competitive cumulative return, sustainable option-income generation, and high confidence in manager execution.
Technology Growth Income Innovation Exposure
Design Objective Participate in long-term technology innovation and mega-cap earnings growth while converting a portion of expected return into recurring monthly income.
Current Allocation 14.0%
Current ETF GPIQ
Selection Standard Must provide diversified Nasdaq-oriented growth exposure, credible upside participation, sustainable premium generation, and strong evidence that the option methodology does not impose an excessive opportunity cost.
Large-Cap Growth Income Growth Participation
Design Objective Capture long-term earnings growth from high-quality large-cap companies while producing meaningful income and preserving substantial upside participation.
Current Allocation 7.0%
Current ETF QDVO
Selection Standard Must emphasize diversified growth-oriented holdings and use a selective or partial option overlay that does not unnecessarily suppress long-term capital appreciation.
Small-Cap Income Market-Capitalization Diversification
Design Objective Expand the portfolio beyond large-cap equities by combining long-term small-cap appreciation with income from a differentiated SPX put-spread overlay.
Current Allocation 7.0%
Current ETF OVS
Selection Standard Must provide broad small-cap exposure, a defensible SPX option-income methodology, competitive total-return potential, and meaningful diversification from large-cap and Nasdaq-focused holdings. The mismatch between small-cap equities and SPX options must remain acceptable.
Diversification Holdings · 11.5% of Portfolio
Quality & Intrinsic-Value IncomeFactor Diversification
Design ObjectiveAdd exposure to profitable, financially resilient companies and Berkshire-style intrinsic-value investing that may perform differently from technology-led growth portfolios.
Current Allocation6.0%
Current ETFsOMAH · QUSA
Selection StandardMust emphasize strong balance sheets, durable earnings, quality or value characteristics, and an income overlay designed to retain meaningful participation in the underlying holdings.
Complementary Broad-Market IncomeManager and Methodology Diversification
Design ObjectiveAdd a second broad U.S. large-cap income implementation with an independently managed equity and option process, reducing dependence on a single broad-market premium-generation methodology.
Current Allocation3.0%
Current ETFSPUC
Selection StandardMust preserve broad-market participation, generate meaningful recurring income, and demonstrate acceptable upside capture, NAV behavior, and execution under the current strategy before consideration for Core.
Additional Technology DiversificationManager and Methodology Diversification
Design ObjectiveSupplement the primary technology allocation with a complementary manager and option structure rather than concentrating all Nasdaq exposure in one fund or premium-generation technique.
Current Allocation1.0%
Current ETFKQQQ
Selection StandardMust provide a differentiated technology implementation, credible income generation, and a portfolio role that complements rather than merely duplicates the primary Technology Growth Income allocation.
International IncomeGeographic Diversification
Design ObjectiveReduce exclusive dependence on U.S. equity valuations and economic conditions through a limited allocation to broad international equities, primarily developed markets with a smaller emerging-markets component.
Current Allocation1.5%
Current ETFOVF
Selection StandardMust provide diversified international exposure, a defensible SPX-family option overlay, competitive long-term total-return potential, and acceptable cross-index risk between foreign equities and U.S. index options.
Experimental Holdings · 3.5% of Portfolio
Broad-Index Income Innovation Methodology Validation
Design Objective Evaluate a specialized short-duration option methodology designed to increase cash generation while retaining diversified exposure to multiple broad U.S. equity indexes.
Current Allocation 1.0%
Current ETF ODTE
Selection Standard Broad underlying exposure is required, but allocation remains limited until the short-dated option process demonstrates consistent execution, sustainable distributions, and acceptable NAV and total-return behavior.
Speculative High-Reward Asymmetric Return Potential
Design Objective Maintain tightly controlled exposure to exceptionally high-income strategies positioned to benefit from favorable semiconductor, memory, and artificial-intelligence investment cycles.
Current Allocation 2.5%
Current ETFs DRMY · CHPY
Selection Standard Elevated volatility, concentration, and limited operating history are permitted only when potential returns are unusually high. Position limits must prevent these strategies from threatening the portfolio's primary objectives.
Portfolio Implementation Reference Reference allocation established August 2026
OVL · 57.0%Broad-Market Income
GPIQ · 14.0%Technology Growth Income
QDVO · 7.0%Large-Cap Growth Income
OVS · 7.0%Small-Cap Income
OMAH · 5.0%Quality & Intrinsic-Value Income
SPUC · 3.0%Complementary Broad-Market Income
DRMY · 1.5%Speculative High-Reward
OVF · 1.5%International Income
ODTE · 1.0%Broad-Index Income Innovation
CHPY · 1.0%Speculative High-Reward
KQQQ · 1.0%Additional Technology Diversification
QUSA · 1.0%Quality & Intrinsic-Value Income
Live allocation: the portfolio may change as new evidence is incorporated. View current portfolio allocation →

This reference allocation is intentionally retained as a documented implementation point rather than continuously rewritten to match market movement or every portfolio adjustment. Classifications and functional categories describe roles within the Design for Wealth™ framework rather than permanent endorsements of individual funds.

Core Holdings: Share Price Return vs. Cumulative Total Return

High-distribution ETFs can appear to lag broad-market benchmarks when evaluated using share-price movement alone because a meaningful portion of their economic return is distributed as cash. The comparison below examines the four Core Holdings and VOO over the same measurement period, first using share price return and then cumulative total return with distributions reinvested.

Share Price Return Only

Distributions Excluded
Google Finance share-price-return comparison of OVL, OVS, QDVO, GPIQ, and VOO from August 11, 2025 through August 7, 2026
Figure 1. Google Finance share-price comparison from August 11, 2025 through August 7, 2026. When cash distributions are excluded, VOO appears to outperform OVL, GPIQ, and QDVO, while OVS materially outperforms the benchmark.
Share price return measures only one component of investor return. For high-distribution ETFs, cumulative total return provides the more complete comparison because it incorporates both changes in share value and the cash distributions generated by the investment.

Cumulative Total Return

Distributions Reinvested
Cumulative total-return comparison of OVL, OVS, QDVO, GPIQ, and VOO from August 11, 2025 through August 7, 2026 with distributions reinvested
Figure 2. Over the same measurement period, cumulative total return with distributions reinvested was 39.71% for OVS, 26.43% for OVL, 25.74% for GPIQ, 23.03% for VOO, and 17.09% for QDVO. The comparison illustrates why Design for Wealth™ evaluates high-income ETFs using cumulative total return rather than share-price movement alone.
Calculation workbook: Download the daily price, distribution, reinvestment, and calculation data used for Figure 2 (.xlsx)
Core Group Observation
Weighted Core Return 25.69%
VOO Benchmark 23.03%

Individual Core Holdings are not required to outperform VOO independently. Each is selected to perform a defined portfolio role. At the Core allocation weights used in this comparison, the four holdings produced a 25.69% weighted cumulative total return versus 23.03% for VOO while providing substantially greater recurring cash distributions and differentiated large-cap, growth, technology, and small-cap exposure.

This comparison covers August 11, 2025 through August 7, 2026 and does not establish future outperformance. Total-return calculations begin with a $100 investment and reinvest each distribution during the measurement period. The weighted Core return applies the relative portfolio weights of the four Core Holdings, normalized within the Core group.

Case Study

This case study documents a real supplemental taxable implementation of the Design for Wealth™ framework using real capital, actual positions, and documented July 2026 results. It should not be interpreted as a complete retirement or household investment strategy, proof of future performance, or a recommended allocation. Its purpose is to show how the portfolio architecture translates into cash distributions, reinvestment, and observed capital behavior. The holdings and allocations in this section are intentionally retained as a historical snapshot corresponding to the brokerage records and are not updated to match the live portfolio.

Portfolio Market Value $217,899.68

Brokerage account value shown in August 2026

July 2026 Distributions $2,386.05

Actual cash distributions received

July Annualized Distribution $28,632.60

July result multiplied by 12 for comparison

July Annualized Distribution Rate 13.14%

July income annualized using August 2026 portfolio value

July Monthly Distribution Rate 1.095%

July distributions divided by portfolio value

Core Holdings Allocation 85%

OVL, QDVO, GPIQ, and OVS at the documented snapshot

Actual July 2026 Cash Distributions

Actual cash distributions received during July 2026 from the portfolio holdings that made distributions during the month. Portfolio percentages are the documented allocations represented by the August 2026 brokerage holdings snapshot used in this case study.

ETF Portfolio Allocation at Case-Study Snapshot Published Distribution Rate July Cash Distribution
OVL 56.0% 10.52% $1,006.02
GPIQ 11.0% 9.70% $485.01
QDVO 13.0% 10.69% $249.19
JEPQ 2.5% 10.45% $220.57
CHPY 1.0% 40.96% $145.42
OMAH 4.5% 15.00% $102.82
OVS 5.0% 10.44% $78.74
KQQQ 1.5% 14.02% $47.78
QUSA 1.0% 15.00% $25.65
OVF 1.5% 8.80% $24.85
Total $2,386.05

The distribution rows above represent 97.0% of the documented portfolio. The remaining positions in the historical holdings snapshot were ODTE (1.5%) and DRMY (1.5%). The table reports only the cash distributions included in the documented July brokerage activity; DRMY had not made its first distribution by the end of July 2026.

Observed Implementation Results

1 Recurring income without selling shares. The portfolio generated recurring cash distributions without requiring routine liquidation of ETF positions.

2 July income pace exceeded the design objective. The July distribution rate, when annualized for comparison, exceeded the portfolio's 10% annual cash-distribution objective.

3 Core allocation requirement satisfied. Core Holdings represented 85% of portfolio capital, exceeding the framework's minimum 80% allocation requirement.

4 DRIP reinvestment demonstrated. Distributions can be reinvested during accumulation to increase share count and future distribution capacity.

5 Multiple income methodologies represented. Portfolio income was produced through several ETF and option-overlay methodologies rather than dependence on a single implementation.

6 Potential tax efficiency. Certain option-income ETF distributions may receive tax treatment that defers recognition of some taxable income.

Tax Treatment and Return of Capital

Certain option-income ETF distributions may receive tax-deferred return-of-capital treatment that reduces cost basis rather than creating immediate taxable income. ROC is evaluated together with NAV and cumulative-return behavior rather than treated as automatic evidence of economic deterioration.

Capital Trajectory

The monthly income result should not be evaluated independently from the portfolio's capital trajectory. The principal Core Holdings generated substantial recurring distributions while maintaining an overall positive longer-term share-price or NAV slope during the measured period.

The observed combination is consistent with the framework's objective of generating high current income while minimizing opportunity cost in long-term cumulative wealth. The corresponding cumulative-return evidence is evaluated separately through the three performance models.

This case study reflects one brokerage account and one monthly distribution period. Portfolio value, ETF prices, NAV, published distribution rates, tax classifications, and cash payments will vary. The annualized figures convert the July result into an annual rate for comparison and do not forecast identical future monthly payments. Final tax treatment depends on fund reporting and the investor's individual circumstances. Long-term success is evaluated using cumulative total return, NAV behavior, distribution sustainability, and performance across multiple market environments. The case-study holdings and allocation percentages are historical evidence and intentionally remain fixed even when the live portfolio later changes.

Brokerage Documentation

Selected Charles Schwab account records document the actual portfolio implementation and cash distributions reported in this case study.

Portfolio Holdings

Charles Schwab portfolio holdings showing the ETF positions used in the Design for Wealth case study VIEW FULL RESOLUTION
Actual portfolio positions. Charles Schwab holdings record showing the ETF positions and $217,899.68 portfolio market value documented in the case study.

July 2026 Distribution Activity

Charles Schwab transaction history showing July 2026 ETF distributions and reinvestment activity VIEW FULL RESOLUTION
Actual distribution activity. Charles Schwab transaction history documenting the July 2026 cash distributions and corresponding DRIP reinvestment transactions used in the case study.

Brokerage records are presented as historical documentation of this case study. Portfolio holdings, market values, distributions, and reinvestment amounts change over time and do not represent expected future results.

Tax Trade-Off Analysis

Tax treatment can materially affect the value of current income in a taxable portfolio. Neither high-distribution ETFs nor periodic sales of passive investments such as VOO are inherently more tax-efficient. Selling high-basis shares can generate cash while realizing relatively little taxable gain, while distributions classified as return of capital can defer current taxes by reducing cost basis.

Case Study Tax Conclusion

As of August 2026, most of the high-distribution ETFs used in the Design for Wealth™ case study report recent distributions that are predominantly or entirely estimated return of capital. ROC generally becomes taxable once cost basis reaches zero. With DRIP on, reinvested distributions continually create new cost basis, helping keep that from happening while allowing money that would otherwise have been paid in current taxes to remain invested and compound.

JEPQ illustrates why distribution tax character matters. J.P. Morgan states that its distributions are taxed primarily as qualified or ordinary income in the year received, while Goldman Sachs has historically classified the majority of GPIQ distributions as return of capital. In a taxable account, funds with similar headline distribution rates can therefore create meaningfully different current tax drag and after-tax compounding.

The relative advantage depends on cost basis, distribution tax character, tax rates, holding period, DRIP usage, and when assets are ultimately sold.

Detailed analysis: Download the Tax Trade-Off Analysis, including case-study ETF tax classifications, VOO sale scenarios, and ROC plus DRIP cost-basis examples (.xlsx)

Distribution classifications shown in the detailed analysis are issuer estimates and may change. Final tax reporting is generally provided on Form 1099-DIV. This analysis is educational and is not individualized tax advice.

Issuer and Management Confidence Assessment

This assessment evaluates each issuer and management organization independently of the performance or merits of any single ETF. The question is whether the firm's organization-wide investment experience, institutional resources, management and operational depth, product-development record, scale, and demonstrated execution would support confidence in its ability to launch and operate investment strategies through changing market conditions.

Management teams and organizational information as of August 2026.

Organization-Wide Investment Experience Breadth and relevance of experience across equities, derivatives, structured products, portfolio management, and exchange-traded funds.
Institutional Resources & Scale Firmwide assets, research depth, risk controls, compliance, trading infrastructure, distribution capability, and organizational stability.
Management & Operational Depth Strength of investment leadership, trading, operations, finance, compliance, and supporting teams rather than reliance on one visible individual.
Product & Platform Execution Demonstrated ability to design, launch, operate, scale, and sustain investment products across changing market environments.
Comparative ranking of ETF issuers and management teams by relevant experience, institutional resources, management depth, and execution evidence.
Rank Issuer and Relevant ETFs Issuer / Management Confidence Assessment of Management Credentials
1 Goldman Sachs Asset ManagementRelevant ETF: GPIQ Very High

Goldman Sachs combines global institutional scale with deep quantitative-equity, derivatives, trading, research, risk, compliance, and operational capabilities. Senior portfolio leaders including Raj Garigipati, Aron Kershner, and John Sienkiewicz add directly relevant ETF and systematic-equity experience. The organization has the breadth, infrastructure, and management depth to support complex investment products without dependence on a single strategy or individual.
2 Amplify ETFsRelevant ETF: QDVO Very High

Amplify has built a broad ETF platform with more than $20 billion in assets under management as of July 31, 2026. Founder and CEO Christian Magoon has launched more than 100 U.S. ETFs, while President William Belden brings more than 30 years of product-development and financial-services experience. The firm combines experienced ETF leadership, legal and compliance depth, national distribution, strategic investment partnerships, and a demonstrated ability to develop and scale actively managed and index-based strategies across income, growth, and risk-managed categories.
3 Liquid Strategies / Overlay SharesRelevant ETFs: OVL, OVS, OVF Very High

Liquid Strategies has focused on options-based investing since its 2013 founding, with professional options-trading experience on the portfolio-management team dating to 1997. Shawn Gibson began as an options market maker and later helped oversee a multibillion-dollar options portfolio; Adam Stewart, CFA adds more than two decades of investment and trading experience. The firm has maintained core investment leadership since inception, expanded into a broader alternatives platform, and surpassed $2 billion in firmwide AUM in August 2026. Its smaller scale than the largest ETF sponsors is offset by unusually deep, continuous, and directly relevant derivatives experience plus meaningful investment, finance, operations, and compliance depth.
4 Simplify Asset ManagementRelevant ETF: SPUC High

Simplify has rapidly built a multi-billion-dollar ETF platform focused on bringing institutional-style alternative, derivative, fixed-income, and risk-managed strategies into liquid ETF structures. David Berns, PhD, Shailesh Gupta, Jeff Schwarte, CFA, and a dedicated trading and risk-management organization provide substantial technical depth. The principal reason the rating remains below Very High is the platform's shorter organizational history relative to the three higher-ranked firms, not the merits or history of SPUC itself.
5 VistaShares / TidalRelevant ETFs: OMAH, QUSA High

VistaShares combines experienced ETF entrepreneurship, investment research, company-building, and options expertise with Tidal's ETF operating infrastructure. Adam Patti previously founded and scaled IndexIQ before its acquisition by New York Life; the broader team includes experienced executives, academics, quantitative researchers, and options professionals. VistaShares surpassed $2 billion in AUM in July 2026, demonstrating unusually rapid platform adoption. The principal limitation is the issuer's comparatively short public operating history, not the performance of any individual VistaShares ETF.
6 VegaSharesRelevant ETF: ODTE Moderate

Adam Stempel has more than two decades in finance and built multibillion-dollar structured-note and exchange-traded-note platforms, while Sunny Wong brings more than two decades in derivatives trading and senior structured-products experience. The professional backgrounds are highly relevant, but VegaShares remains a young ETF organization with less firmwide operating history, scale, and demonstrated platform breadth than the higher-ranked issuers.
7 YieldMax / TidalRelevant ETF: CHPY Moderate

The YieldMax management organization combines substantial options-market experience with Tidal's established ETF operating platform. Jay Pestrichelli has more than 30 years in financial markets, and Scott Snyder brings more than 40 years of market experience including two decades as an independent CBOE options market maker. The organization has meaningful technical and operating capabilities, while the current management structure and platform record remain shorter than those of the higher-ranked firms.
8 Kurv Investment ManagementRelevant ETF: KQQQ Moderate

Kurv's leadership brings genuine institutional experience. Howard Chan has more than 20 years in investment management including roles at Goldman Sachs and leadership of European ETFs at PIMCO, while Dominique Tersin brings extensive ETF and liquidity-management experience from PIMCO. The limiting factors are primarily organizational scale and the shorter public operating history of the Kurv platform compared with the larger and longer-established issuers above it.
9 XFUNDS / Nicholas Wealth / TidalRelevant ETF: DRMY Developing

David Nicholas has more than 18 years in financial services, and Cory Reed, CFA adds experience managing concentrated and long-short equity strategies. Tidal contributes ETF trading, compliance, administration, and fund operations, while experienced options personnel add technical support. The organization remains the least proven in this comparison because Nicholas Wealth and XFUNDS have a smaller institutional footprint and shorter product-development and public ETF operating history.
Research sources: view the official biographies and fund documents used in this assessment
Goldman Sachs: GPIQ fund and management information.
Amplify: official platform, AUM, and leadership information.
Liquid Strategies: official firm history, team biographies, and platform evolution and firm AUM and ETF information.
Simplify: official leadership biographies and investment-team biographies.
VistaShares: official team biographies and July 2026 AUM announcement.
VegaShares: official leadership biographies.
YieldMax and Tidal: CHPY prospectus and manager information.
Kurv: KQQQ and team information.
XFUNDS and Nicholas Wealth: DRMY prospectus and manager biographies.
Interpretation of the Assessment

Issuer confidence is intentionally separate from ETF selection. A high ranking means the organization has the experience, resources, depth, and execution record to inspire greater confidence in its ability to design and operate investment products. It does not mean every ETF the organization launches should qualify for the Design for Wealth™ portfolio.

Individual funds must still earn inclusion through their own methodology, total-return and NAV evidence, distribution sustainability, three-model results, liquidity, operating history, and functional portfolio role. Likewise, a short or disappointing record for one ETF does not automatically reduce confidence in an otherwise strong issuer.

Rankings are comparative assessments based on publicly available professional backgrounds, organizational resources, stated management responsibilities, and relevant investment experience. They are analytical judgments, not credit ratings, guarantees of investment performance, or permanent conclusions. Rankings may change as management teams, organizational resources, assets under management, or operating histories evolve.

Use Cases

Design for Wealth™ addresses a recurring financial challenge: generating meaningful current income while maintaining long-term equity participation and controlling the opportunity cost of income-oriented strategies. These examples illustrate potential applications of the framework and are presented for educational purposes rather than individualized investment recommendations.

01

Early Retirement Income Bridge

Employment-to-Retirement Transition

Objective: Generate meaningful monthly income during the period between leaving full-time employment and becoming eligible for penalty-free retirement-account withdrawals, pensions, or other retirement benefits.

Framework Application Portfolio distributions may help fund living expenses while reducing the need to sell long-term assets during an extended transition period.

02

Semi-Retirement

Reduced Employment Dependency

Objective: Replace a portion of employment income with systematic portfolio distributions while maintaining participation in long-term capital appreciation.

Framework Application Recurring investment income may make reduced work schedules, consulting, seasonal employment, or lower-stress careers financially practical.

03

Supplemental Retirement Income

Retirement Cash-Flow Diversification

Objective: Provide an additional stream of recurring income that complements Social Security, pensions, annuities, and retirement-account withdrawals while seeking to preserve long-term purchasing power.

Framework Application Portfolio income may reduce dependence on any single retirement-income source while providing additional flexibility for discretionary spending.

04

Small Business Operating Income

Business Cash-Flow Support

Objective: Create a dedicated investment portfolio that helps fund recurring business expenses such as software, telecommunications, insurance, professional services, utilities, website hosting, and administrative overhead.

Framework Application Monthly portfolio distributions may help fund recurring business expenses and, for a lean or home-based business, may cover the full cost of routine operating overhead without relying on current client revenue.

05

Nonprofit Endowment Income

Mission and Operating Support

Objective: Produce recurring income to support charitable programs and operating expenses while seeking to preserve the long-term value of donated capital.

Framework Application Income-producing investments may supplement fundraising revenue within the organization's approved investment and spending policies while allowing the underlying capital base to remain invested for future generations.

06

Lower-Cost Living Abroad

Geographic Cost-of-Living Advantage

Objective: Use recurring portfolio income to support living expenses in a lower-cost foreign location.

Framework Application Lower living costs can allow the same portfolio distributions to cover a larger share of expenses, reducing the capital required for financial independence.

07

Family Financial Reserve

Financial Resilience

Objective: Maintain a diversified income-producing portfolio whose distributions can be reinvested during normal periods and redirected to cash when major household expenses, unexpected life events, career transitions, health-related disruptions, or other financial needs arise.

Framework Application Temporarily turning off DRIP can redirect recurring portfolio distributions into a meaningful cash reserve for major expenses without requiring the immediate sale of invested assets or the realization of losses during an unfavorable market.

08

Private School Tuition Funding

Education Cash-Flow Planning

Objective: Generate recurring portfolio income that can help fund preschool through Grade 12 private-school tuition and related education expenses while keeping long-term investment capital substantially invested.

Framework Application Recurring portfolio distributions can be accumulated for scheduled tuition payments, reducing the need to sell stocks or ETFs when markets may be temporarily depressed and helping limit forced-sale risk.

09

Financial Independence

Long-Term Income Replacement

Objective: Build a portfolio capable of generating sufficient recurring income to support living expenses while maintaining long-term participation in equity-market growth.

Framework Application A high-income portfolio may reduce reliance on predetermined withdrawal schedules while providing flexibility during periods of market volatility.

Application Principle
The same framework can be adapted to different objectives depending on income needs, time horizon, tax circumstances, geographic location, liquidity needs, and other available financial resources. The framework does not assume that one distribution target, allocation, or opportunity cost is appropriate for every use case. Each application requires its own objectives, cash reserves, constraints, risk limits, and validation criteria.

These use cases are educational examples only. They do not constitute personalized investment, tax, retirement, legal, immigration, education, or financial planning advice.

Assumptions / Notes

Long-horizon portfolio design requires explicit assumptions about markets, investment instruments, taxation, model interpretation, and how investment evidence is applied in practice. These items make those assumptions and design principles visible so they can be evaluated and revised rather than remaining implicit within the framework.

No. Assumption or Note Framework Treatment
01

Long-Term Market Direction

 High-Probability Assumption

A high probability is assigned to diversified U.S. equities producing a positive cumulative total return over a 20-year investment horizon, despite the likelihood of significant interim declines, recessions, and periods of weak or negative performance. Extended stagnation or negative long-term returns have occurred in other major markets, including Japan, demonstrating that this outcome is not guaranteed.

02

Continued Relevance of Capital Markets

 High-Probability Assumption

A high probability is assigned to stocks, ETFs, and listed options remaining materially relevant mechanisms for capital ownership and investment income over the next 20 years. Major technological or economic shifts, including artificial intelligence, humanoid robotics, or sustained-abundance scenarios, could materially alter how capital ownership and investment income function.

03

Option-Market Capacity and Strategy Crowding

 High-Probability Assumption

A high probability is assigned to U.S. listed options markets retaining sufficient depth and adaptability over the next 20 years to support option-based portfolio strategies. The future economics of particular option-income strategies are less certain. Increased adoption could cause multiple funds to sell similar options or spreads on the same underlying assets, increasing supply at particular strikes and expirations and potentially compressing option premiums. Market depth, evolving volatility regimes, changing market structure, and continued product innovation are expected to limit, but not eliminate, crowding risk.

04

Tax-Efficient Distribution Design

 High-Probability Assumption

A high probability is assigned to option-income ETF issuers continuing to consider tax efficiency when structuring portfolio and distribution mechanics where economically and legally available. ETF distributions may receive different tax classifications, including ordinary dividends, qualified dividends, capital-gain distributions, and nondividend distributions such as return of capital. Option transactions can also affect the tax character of fund income. These differences can materially affect after-tax cash flow, making tax treatment an intentional consideration in taxable-account portfolio design rather than an incidental feature of the distribution.

05

Benchmark Discipline and Market Humility

 Design Philosophy Note

Design for Wealth™ does not assume that persistent market outperformance can be reliably identified in advance or sustained over an investor's lifetime. The S&P 500 therefore serves as the primary universal opportunity-cost benchmark rather than a return target the portfolio is expected to exceed. Exposure-specific passive benchmarks are also used to determine whether a strategy adds value relative to the market exposure it is designed to provide. Sector, thematic, and manager-driven strategies may outperform for extended periods, but the framework does not depend on forecasts that technology or any other market segment will continue to lead indefinitely. The design objective is to generate high recurring income while keeping long-term cumulative return competitive with broad U.S. equities.

This principle also reflects Charlie Munger's enduring emphasis on intellectual humility: markets have a way of humbling even exceptionally intelligent and accomplished investors.
06

Controlled Exposure to Technology Adoption S-Curves

Design Philosophy Note

The framework permits a deliberately limited allocation to emerging opportunities when observable evidence indicates that a technology, industry, or economic development may be progressing through a significant adoption S-curve. As of August 2026, the current implementation reserves approximately 4% of portfolio capital for Experimental Holdings, allowing participation in structural developments without requiring the portfolio's long-term success to depend on a thematic forecast. Relevant evidence may include accelerating adoption, capital investment, supply constraints, industry activity, changing customer behavior, or firsthand professional and consumer observations. Because even correctly identified technology S-curves may already be reflected in market prices, or may fail to produce superior investment returns, exposure remains intentionally constrained.

07

Applied Framework and Iterative Validation

 Framework Development Note

Design for Wealth™ is an applied portfolio-design framework developed through actual capital deployment, comparative observation, modeling, and iterative refinement rather than as a purely conceptual investment exercise. The principles and classification rules were developed through repeated observation of ETF behavior across differing market conditions, including sharp single-day declines, strong market advances, multi-day corrections, and changing volatility environments. Differences in downside participation, recovery behavior, NAV trajectory, option-income generation, and relative performance were used alongside quantitative modeling to refine portfolio roles and allocation rules. The resulting framework therefore reflects both modeled evidence and observed implementation experience. Short-term implementation observations are treated as additional evidence rather than substitutes for the longer-horizon three-model analysis.

08

ROC Interpretation in AI Audits

 Model-Audit Note

AI-generated investment analysis has historically incorrectly treated return of capital (ROC) as automatic NAV destruction. Anyone using AI to audit the Design for Wealth™ framework should require ROC to be evaluated using NAV trajectory, cumulative total return, option-premium economics, and distribution coverage. OVL can be provided as a useful counterexample because it has reported high ROC classifications while preserving and growing NAV.

09

Three-Model Interpretation and Uncertainty

 Model Interpretation Note

The three models are complementary analytical perspectives rather than independent forecasts of the future. Model 1 measures actual observed results, Model 2 reconstructs strategy behavior through a longer historical environment, and Model 3 examines plausible future structural regimes and uncertainty. Shared evidence may influence more than one model, and no ranking establishes a known future outcome. Greater weight is placed on conclusions that remain credible across different analytical perspectives.

10

Fund Continuity and Strategy Change

 Implementation Note

Portfolio roles are expected to be more durable than individual ETFs. Funds may close, merge, change managers, modify investment methodology, alter option coverage, change fees, or experience deterioration in execution quality. A holding remains in the design only while its current structure and evidence continue to satisfy the requirements of its assigned role.

11

Assumptions Remain Revisable

 Continuous Improvement

These assumptions are working design inputs rather than permanent conclusions. The framework should be revised when credible new evidence materially changes their probability, relevance, or effect on the portfolio design.

Probability descriptions are qualitative design judgments used to make the framework's foundational assumptions explicit. They are not statistical forecasts or estimates of a specific numerical probability.

Glossary

Plain-language definitions of investment and modeling terms used throughout Design for Wealth™.

0DTE

“Zero Days to Expiration.” An option that expires on the same trading day, allowing strategies to repeatedly generate short-duration option exposure and premium.

AUM

Assets Under Management. The total value of investor assets held in a fund.

Benchmark

An investment or market index used as a reference for judging another investment's performance.

CAGR

Compound Annual Growth Rate. The single annual growth rate that would turn a starting value into its ending value over a specified period.

Call Option

A contract giving its buyer the right to purchase an investment at a predetermined price. The seller receives cash for accepting the obligation to sell at that price if exercised.

Covered Call

An income strategy in which an investor owns shares and receives cash for giving someone else the right to buy those shares at a predetermined price. Strong price gains may therefore be partially surrendered.

Cumulative Total Return

The total percentage gain or loss over a period after including both changes in investment value and distributions.

Distribution

Cash paid by a fund to shareholders. It may come from dividends, interest, option income, capital gains, return of capital, or a combination of sources.

Distribution Rate

An annualized measure of a fund's current cash distributions relative to its share value. Calculation methods vary by issuer, and distribution rate is not the same as investment return.

DRIP

Dividend Reinvestment Plan. Cash distributions are automatically used to purchase additional shares rather than being taken as cash.

ELN (Equity-Linked Note)

A structured security whose return is linked to an equity index or investment strategy. Some income ETFs use ELNs to obtain option-like exposure and generate distributions.

ETF

Exchange-Traded Fund. A fund whose shares trade on an exchange like a stock and which owns investments or follows a defined investment strategy.

Evidence Confidence

A qualitative assessment of how strongly the available history, benchmark fit, strategy evidence, and other inputs support an analytical conclusion. It does not measure the probability that a forecast will be correct.

Expense Ratio

The annual operating cost of a fund expressed as a percentage of its assets. Fund expenses reduce investor returns.

Modeled Net CAGR

The estimated annualized return produced by a model after combining the assumed market exposure return, estimated strategy effect, and fund expenses.

Monte Carlo Rank-Stability Analysis

A simulation that tests how consistently an ETF ranks across many plausible future market paths and assumptions rather than relying on one projected outcome.

Monte Carlo Simulation

A computer model that runs many different possible future market paths to estimate a range of outcomes rather than assuming one knowable future.

NAV

Net Asset Value. The per-share value of what a fund owns after subtracting what it owes.

Option

A financial contract giving one party specific rights to buy or sell an investment at an agreed price while creating a corresponding obligation for the seller.

Option Overlay

An options strategy added to an underlying investment portfolio to alter its income, risk, or return characteristics.

Option Premium

The cash an option buyer pays to an option seller for the rights created by the contract.

Opportunity Cost

The return gained or surrendered by choosing one investment instead of an appropriate alternative. Design for Wealth™ uses opportunity-cost comparisons to evaluate whether income generation justifies any reduction in cumulative return.

pp (Percentage Points)

A unit used to express the arithmetic difference between two percentages. Moving from 10% to 12% is +2 pp; moving from 12% to 10% is −2 pp.

Put Option

A contract giving its buyer the right to sell an investment at a predetermined price. The seller receives cash for accepting the obligation to buy at that price if exercised.

Put Spread

A strategy combining put options at different predetermined prices to create a defined income or risk profile.

Return of Capital (ROC)

A tax classification in which part of a distribution is generally treated as a return of invested capital rather than current taxable income, often reducing cost basis and potentially deferring tax until the investment is sold. ROC does not automatically indicate economic loss or NAV destruction.

Share Price Return

The percentage change in an investment's market price over a period, excluding cash distributions.

Strategy Effect

The estimated return added or subtracted by an ETF's investment and income methodology relative to its reference exposure. For actively managed funds, it may include both security selection and option implementation.

Structural Scenario

A coherent set of economic and market assumptions used to test how investments may perform under a particular future environment. A scenario is an analytical case, not a prediction that the future will unfold that way.

Total Return

Investment performance after including both changes in value and cash distributions.

Underlying

The investment, group of investments, or market index on which a fund or options strategy is based.

Volatility

The degree to which prices or returns fluctuate. Higher volatility generally means a wider range of possible gains and losses.

Portfolio Engineering

Meet the Portfolio Engineer

Noah O'Brien

Creator, Design for Wealth™

Noah O'Brien, creator of Design for Wealth

He holds a Master of Science in Engineering with a concentration in Industrial and Lean Systems, including application of Six Sigma, statistical analysis, and Monte Carlo simulation, as well as a Master of Business Administration (MBA) with a focus in corporate finance, accounting, and investment analysis.

His professional career began in field of reliability engineering and progressed into the design and implementation of World Class Manufacturing and Design for X systems for large international manufacturing organizations. His work has centered on analyzing complex systems, identifying sources of loss, evaluating trade-offs, improving reliability and performance, and developing structured methods for evidence-based design and continuous improvement.

Design for Wealth™ applies those same engineering and financial-analysis principles to portfolio design through objective definition, trade-off analysis, three-model validation, evidence-based allocation, and continuous improvement.