The risk profile, derived from past market volatility, reflects the level of risk the portfolio is exposed to. This assessment helps align your investments with your financial goals and comfort with market fluctuations.
The diversification assessment evaluates the spread of investments across asset classes, regions, and sectors. This ensures a balanced mix, reducing risk and maximizing returns by not concentrating in any single area.
This portfolio is built mainly from diversified equity ETFs, with a strong tilt toward value and smaller companies, plus some momentum exposure. Around four-fifths sits in stocks, with the rest spread across managed futures, inflation-focused strategies, long Treasuries, gold, and a small Bitcoin slice. This mix blends long-term growth assets with explicit diversifiers that can behave differently in inflation spikes or equity drawdowns. Structurally, it’s more complex than a basic stock–bond mix, but that complexity is purposeful rather than random. The key takeaway is that the structure aims for higher long-term returns than a plain index approach while consciously layering in inflation hedges and alternative risk drivers to smooth the ride somewhat.
Over the measured period, $1,000 grew to about $1,574, implying a compound annual growth rate (CAGR) of 22.69%. CAGR is the “average speed” of growth per year, smoothing out bumps. This comfortably beat both the US and global equity benchmarks by a bit over 5 percentage points per year, which is impressive. At the same time, the worst decline from peak to trough was about -14.1%, actually smaller than the US market’s -18.8% drawdown. That combination of higher return and lower max drawdown is excellent, but it’s based on a short window. Past performance is helpful context, not a guarantee that the same pattern will repeat.
The Monte Carlo projection uses many randomized paths built from historical return and volatility patterns to estimate a range of future outcomes. Think of it as running 1,000 “what if the next 15 years behaved like past markets in different sequences?” experiments. Here, the median outcome grows $1,000 to about $2,692, with a wide but reasonable range from roughly $1,066 to $6,879. The average simulated return is 7.7% per year, and about three-quarters of paths end with a profit. These numbers are scenario-based, not promises: they assume the future looks statistically similar to the past, which may not hold if regimes change. Still, they give a realistic sense of both upside potential and downside risk.
Asset-class-wise, about 80% in stocks anchors the portfolio firmly toward long-term growth, which fits a “balanced but growth-leaning” risk profile. A small 2% slice in long-duration Treasuries adds classic recession and deflation protection, while 3% in crypto and 5% in other diversifiers like gold and managed futures provide uncorrelated return streams. There’s also a 10% bucket labeled “No data,” which simply means the asset-class breakdown isn’t available for those positions, not that anything is wrong. Overall, this mix is more adventurous than a traditional 60/40, but the added diversifiers aim to offset some of the higher equity exposure’s volatility over full cycles.
This breakdown covers the equity portion of your portfolio only.
Sector exposure is pleasantly well-balanced. Financials, industrials, and technology form the largest buckets, but none is overwhelmingly dominant. Compared with many modern portfolios that lean heavily on tech, this one looks more evenly spread across the economic landscape, with meaningful stakes in energy, materials, and cyclicals like consumer discretionary. That can be helpful if leadership rotates away from a narrow group of tech giants. On the flip side, balanced sector exposure also means the portfolio won’t fully participate if a single sector massively outperforms. Still, from a diversification standpoint, this sector mix aligns nicely with broad-market norms and supports resilience across different business cycles and interest-rate environments.
This breakdown covers the equity portion of your portfolio only.
Geographically, about 42% sits in North America, with the rest spread across developed Europe, developed Asia, Japan, emerging Asia, Latin America, and smaller allocations to emerging Europe, Africa/Middle East, and Australasia. This is much more global than a US-heavy portfolio, aligning closely with the idea of owning a slice of the entire world. That global spread helps reduce reliance on any one economy, currency, or political regime. It means that if one region struggles—for example, due to local recession or policy shocks—other areas can offset the drag. The tradeoff is that performance can diverge from purely US benchmarks, especially in periods when the US market significantly out- or underperforms other regions.
This breakdown covers the equity portion of your portfolio only.
The market-cap breakdown shows a healthy balance: meaningful exposure across mega, large, mid, small, and even micro caps. This is different from many index portfolios that cluster heavily in mega- and large-cap names. Smaller companies tend to be more volatile but historically have offered higher long-term return potential, while mega caps can provide stability and liquidity. Having all sizes represented spreads risk across different business models and growth stages. It also complements the value tilt, since value opportunities often show up more in smaller and mid-sized firms. The result is a more “complete” equity exposure that can benefit from both established leaders and up-and-coming challengers.
This breakdown covers the equity portion of your portfolio only.
Looking through the top holdings across all ETFs, no single stock clearly dominates overall exposure. The largest identifiable line is actually a Bitcoin trust at about 3%, reflecting the small crypto allocation. After that, positions like Taiwan Semiconductor, SK Hynix, Micron, Exxon, and the big US tech names each sit well below 1% of the total portfolio. That suggests hidden single-stock concentration is limited, even though overlap is probably understated because only ETF top-10s are used. This is a positive sign: the portfolio gets its tilts mainly from broad baskets and factor exposures rather than oversized bets on individual companies, which generally supports more stable diversification.
Factor exposures are estimated using statistical models based on historical data and measure systematic (market-relative) tilts, not absolute portfolio characteristics. Results may vary depending on the analysis period, data availability, and currency of the underlying assets.
The standout factor tilt here is value, with exposure marked as “very high.” Factor exposure describes how much the portfolio leans into traits like cheapness (value), size, or momentum that research has tied to long-run returns. A strong value tilt means this portfolio favors companies that look cheaper relative to fundamentals like earnings or book value. Historically, value has gone through long cold spells and powerful rebounds; this tilt should help during value-friendly environments but can lag when expensive growth names dominate. Quality exposure is also above average, which typically means more robust balance sheets and profitability. That pairing—value plus quality—is a thoughtful combo that aims for cheaper companies without overly sacrificing business strength.
Risk contribution measures how much each holding drives the portfolio’s overall ups and downs, which can differ from its weight. Here, the largest US large-cap value ETF is about 20% of the portfolio and contributes a very similar share of risk, so its influence is proportional. More interestingly, the US small-cap value and US mid-cap momentum funds are each 10% by weight but contribute roughly 13% of the risk apiece. That’s normal: smaller and momentum-tilted stocks tend to be bumpier. The key insight is that these intentional tilts are also the main risk engines. If the goal is to dial volatility up or down over time, adjusting these higher-risk contributors is the most direct lever.
Correlation describes how assets move together; highly correlated assets rise and fall in tandem, while uncorrelated ones dance to different beats. The pairs flagged here—international equity with international small value, and emerging markets equity with emerging value—move almost identically. That makes sense because they fish in overlapping ponds with similar styles. The implication is that, even though these pairs are separate funds, they behave much like one asset in stress periods, limiting diversification between them. Diversification benefits are more likely to come from the mix of regions, factors, and alternatives like managed futures and gold, rather than from owning multiple nearly parallel strategies within the same region.
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.
The efficient frontier chart shows the portfolio is currently below the best achievable risk/return combination using the same ingredients. The Sharpe ratio—return per unit of risk above cash—is 1.29 now, versus 2.21 for the optimal mix and 1.57 for the minimum-variance mix. Being about 9 percentage points below the frontier at the current risk level means there’s room to improve the balance between volatility and return simply by reweighting existing holdings, not by adding new ones. The good news is that the building blocks are clearly strong; the optimization result suggests they could be arranged in a more efficient pattern to squeeze more “work” from the same overall risk budget.
The portfolio’s overall yield sits around 2.08%, reflecting a blend of equity income, bond coupons, and payouts from alternative strategies. Yield is the cash income you receive as dividends or interest, separate from price changes. Some holdings, like the inflation-focused ETF and long Treasuries, offer relatively high stated yields, while others—such as momentum and some US value exposures—sit lower. This level of income is solid but clearly secondary to the growth and factor-tilt objectives; it won’t behave like a high-yield income portfolio. For investors who reinvest distributions, these dividends still quietly boost compounding over time, but the main return engine remains capital appreciation from the equity and factor exposures.
The weighted average ongoing fee (TER) of about 0.30% is quite reasonable for a portfolio using specialized factor, inflation, and alternatives strategies. Some pieces, like the managed futures and all-weather inflation ETFs, are naturally pricier because they’re more complex to run, while core Avantis equity funds and plain-vanilla gold or Treasury ETFs are very cost-efficient. Costs matter because every 0.1% saved each year compounds over decades. In this case, the balance is sensible: the portfolio keeps costs low where simple exposure is enough and spends a bit more only where it’s buying genuinely differentiated strategies. That’s a strong structural foundation for long-term performance after fees.
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