This portfolio has only about 1.9 years of historical data, based on the youngest asset in the portfolio. Some metrics, projections, and AI insights may be less reliable and should be interpreted with caution.
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A growth oriented global equity portfolio with significant US tilt small cap value and bitcoin exposure

Report created on Dec 13, 2025

Risk profile Info

4/7
Balanced
Less risk More risk

Diversification profile Info

5/5
Highly Diversified
Less diversification More diversification

Positions

The portfolio is concentrated in equities with five ETFs: a global total world ETF (50%), a US momentum ETF (20%), US small cap value (15%), international small cap value (10%) and a bitcoin trust (5%). Compared with a standard global market-cap benchmark it tilts toward the US and applies active tilts via momentum and value small-cap strategies while keeping a small crypto allocation. This allocation is well-balanced and aligns closely with global standards for equity diversification. Recommendation: set explicit target ranges and a rebalancing cadence to maintain intended tilts and avoid unintended concentration over time.

Growth Info

Using a hypothetical $10,000 initial investment, a reported CAGR of 23.5% (CAGR is the Compound Annual Growth Rate which shows average annual growth like the average speed of a car over a trip) would grow that sum to roughly $28,700 over five years. Max drawdown of -17.74% (largest peak‑to‑trough loss) shows meaningful but recoverable volatility. Benchmark context suggests the portfolio has outperformed typical broad indices recently, but note that only 16 days produced 90% of returns indicating high event concentration. Recommendation: expect uneven returns and keep a plan for drawdowns with set rebalancing and contribution rules.

Projection Info

The Monte Carlo analysis ran 1,000 simulations to model many possible future paths by randomly sampling returns and volatility based on historical inputs; it’s a way to see likely outcomes, not a guarantee. Reported percentiles show very positive tails with the 50th percentile at 3,265.5% and a 100% of simulations positive, suggesting historically-driven momentum. However simulated results rely on past return distributions and correlations which can change, so treat projections as scenario guidance. Recommendation: use Monte Carlo outcomes to set realistic goals and stress‑test plans rather than to assume certainty.

Asset classes Info

  • Stocks
    94%
  • Other
    5%
  • Cash
    1%

Asset class weighting is heavily equity oriented at 94% stocks, 5% other (bitcoin) and 1% cash. That creates high expected long‑term return potential but limited built‑in downside protection since fixed income or stabilized assets are nearly absent. For a balanced profile this is a growth‑heavy configuration—diversification score is strong but class mix is risk concentrated. Recommendation: consider whether a small allocation to a low‑volatility asset sleeve (e.g., high quality bonds or defensive alternatives) would better match a balanced risk profile or whether the current pure equity stance is intentional for long‑term growth.

Sectors Info

  • Technology
    22%
  • Financials
    18%
  • Industrials
    12%
  • Consumer Discretionary
    11%
  • Telecommunications
    8%
  • Health Care
    6%
  • Energy
    5%
  • Basic Materials
    5%
  • Consumer Staples
    5%
  • Utilities
    2%
  • Real Estate
    2%

Sector distribution shows technology (22%) and financial services (18%) as the largest exposures, followed by industrials and consumer cyclicals. Compared with broad market benchmarks this is somewhat tilted toward tech and finance which can boost long‑term growth yet increase sensitivity to rate cycles and sector-specific shocks. Tech‑heavy snapshots often see higher volatility in rising rate or regulatory periods. Recommendation: monitor sector weights relative to set thresholds and consider incremental allocation to underweighted sectors to improve cycle resilience if desired.

Regions Info

  • North America
    69%
  • Europe Developed
    11%
  • Japan
    6%
  • Asia Emerging
    3%
  • Asia Developed
    3%
  • Australasia
    2%
  • Africa/Middle East
    1%
  • Latin America
    1%

Geographic exposure is strongly North America at 69% with Europe Developed 11% and Japan 6%; emerging markets sit low at about 3% in Asia Emerging and minimal elsewhere. This US tilt benefits from historical US market leadership but concentrates country and currency risk. International small cap value provides some diversification but is only 10%. Recommendation: if the goal is global balance, consider modest increases to emerging and non‑US developed positions or use currency hedging where appropriate to reduce single‑country dependence.

Market capitalization Info

  • Mega-cap
    30%
  • Large-cap
    24%
  • Mid-cap
    18%
  • Small-cap
    13%
  • Micro-cap
    9%

Market cap mix is broad with mega caps 30%, large caps 24%, mid caps 18%, small caps 13% and micro caps 9%. The Avantis small cap value allocations provide meaningful smaller‑cap exposure which can enhance returns but also raise volatility and liquidity considerations. This spread supports diversification across size factors and captures value and momentum tilts. Recommendation: monitor turnover and bid‑ask spreads for small and micro cap holdings, and maintain rebalancing rules so cap drift doesn’t create unintended concentration in more volatile small‑cap segments.

Risk vs. return

This chart shows the Efficient Frontier, calculated using your current assets with different allocation combinations. It highlights the best balance between risk and return based on historical data. "Efficient" portfolios maximize returns for a given risk or minimize risk for a given return. Portfolios below the curve are less efficient. This is informational and not a recommendation to buy or sell any assets.

Click on the colored dots to explore allocations.

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The Efficient Frontier is a portfolio theory concept that shows the set of allocations that offer the highest expected return for a given level of risk; here optimization would reweight the existing ETFs to seek a better risk‑return tradeoff without adding new asset types. Using only the current assets an optimizer might slightly adjust exposures between global, momentum, and value sleeves to shift the portfolio along the frontier. Recommendation: run a constrained optimization that includes transaction costs, tax impacts and practical limits, and view efficiency as a measure of risk‑adjusted return rather than a guarantee of lower absolute volatility.

Dividends Info

  • Avantis® International Small Cap Value ETF 3.30%
  • Avantis® U.S. Small Cap Value ETF 1.60%
  • Invesco S&P 500® Momentum ETF 0.60%
  • Vanguard Total World Stock Index Fund ETF Shares 1.60%
  • Weighted yield (per year) 1.49%

The portfolio’s blended dividend yield is modest at about 1.49%. Dividend yield is the income paid by holdings relative to price and can support total return and cash flow, particularly for income‑oriented investors. Here dividends play a minor role; the strategy is tilted toward capital appreciation rather than income. Recommendation: if income is a future goal, consider shifting part of the allocation to higher yield or dividend growth holdings, or create a separate income sleeve while keeping the growth core intact.

Ongoing product costs Info

  • Avantis® International Small Cap Value ETF 0.36%
  • Avantis® U.S. Small Cap Value ETF 0.25%
  • Fidelity Wise Origin Bitcoin Trust 0.25%
  • Invesco S&P 500® Momentum ETF 0.13%
  • Vanguard Total World Stock Index Fund ETF Shares 0.07%
  • Weighted costs total (per year) 0.15%

Reported expense ratios range from 0.07% for the global ETF up to 0.36% for the international small cap value ETF with a portfolio total TER around 0.15%. TER stands for Total Expense Ratio and measures fund operating costs similar to a maintenance fee on an investment. The overall cost profile is low and efficient which is a clear strength since lower fees compound into higher net returns over time. Recommendation: retain low‑cost core exposures while periodically evaluating whether higher‑cost active components deliver expected alpha net of fees.

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