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.
The portfolio is a simple three-ETF setup: a broad domestic stock market core at 60%, a growth-tilted domestic sleeve at 20%, and a 20% allocation to international stocks. So it’s 100% in equities with no bonds or cash buffers, which lines up with a growth-oriented approach. This structure makes the portfolio easy to manage and understand, and the heavy use of broad index funds supports diversification within each holding. The flip side is that all risk comes from stocks, so swings can be meaningful. For someone wanting long-term growth and willing to tolerate volatility, this kind of “all equity, three-fund” design is a straightforward, coherent framework.
From 2016 to 2026, $1,000 grew to about $3,680, implying a compound annual growth rate (CAGR) of 13.96%. CAGR is like your average speed on a long road trip, smoothing out the bumps. Compared with the US market benchmark, which returned 14.58%, performance was slightly lower, but it comfortably beat the global market at 12.11%. The max drawdown of about -34% during early 2020 was very similar to both benchmarks, showing equity-like downside. This result indicates the portfolio has delivered strong absolute returns with risk broadly in line with major stock indices. Still, past performance is no guarantee of future results, especially over just one cycle.
The Monte Carlo projection uses historical return and volatility patterns to simulate many possible 15-year paths for a $1,000 investment. Think of it as running 1,000 alternate futures based on past behavior. The median outcome lands near $2,697, with a “middle” range of about $1,746 to $4,317 and a wide 5–95% band from roughly $983 to $7,973. That spread shows how uncertain equity returns can be, even when the long-run expectation (about 8.2% annualized across simulations) looks attractive. Around 72% of simulations end positive, but there’s still a meaningful chance of flat or negative results. These simulations are helpful guides, not promises, since future markets may differ from the past.
All of the portfolio is invested in stocks, with 0% allocated to bonds, cash, or other asset classes. That creates a clean, growth-focused profile that can benefit strongly from long-term equity returns. However, it also means drawdowns will be fully equity-like, with no built-in stabilizers when markets fall. Many blended portfolios use bonds or cash to dampen volatility, sacrificing some upside for smoother rides. Here, the choice is clearly to prioritize capital growth over short-term stability. This design can work well for investors with long horizons who can ride through large swings, but is less suited to those who might need to draw on this money in the near-to-medium term.
Sector exposure is led by technology at 32%, followed by financials at 13%, and a spread across consumer, industrial, telecom, healthcare, and smaller weights in energy, materials, real estate, and utilities. Compared with many broad global benchmarks, this tech weight is on the higher side, reflecting the growth tilt and dominance of mega-cap tech in indices. A tech-heavy portfolio tends to benefit in periods of innovation, falling interest rates, and strong earnings growth, but can be more volatile when rates rise or sentiment turns against growth names. The good news is that other sectors are still meaningfully represented, which helps avoid being entirely dependent on one theme.
Geographically, about 81% is in North America, with modest allocations to developed Europe, Japan, developed Asia, emerging Asia, and small slices in Australasia and Africa/Middle East. This is more US-heavy than a typical global market-cap index, where the US is large but not quite this dominant. The advantage is alignment with the US economy, currency, and many of the world’s largest, most profitable companies. The trade-off is that economic or policy shocks specific to North America will have an outsized impact. The international sleeve is a helpful diversifier, but global exposure is still a secondary driver rather than an equal partner in shaping returns and risk.
Market cap exposure is strongly tilted to mega- and large-caps: roughly 76% sits in the biggest companies, with 17% in mid-caps, 5% in small-caps, and 1% in micro-caps. This pattern closely mirrors broad market indices, where the giants dominate total value. Large companies often bring more stable earnings, deeper liquidity, and lower individual risk than tiny firms, which supports smoother behavior than a small-cap-heavy approach. On the other hand, smaller companies can sometimes deliver higher long-term growth, albeit with more volatility. Here, the emphasis is on market leaders, which is well-aligned with mainstream index investing and helps keep single-company blow-up risk relatively limited outside the very top holdings.
Looking through the ETFs, a good chunk of exposure sits in a familiar group of mega-cap names: NVIDIA, Apple, Microsoft, Alphabet, Amazon, Meta, Broadcom, Tesla, and Berkshire. Several of these appear via multiple funds, creating hidden concentration even though you only hold three ETFs. That means portfolio behavior will often track how these giants perform, especially in market stress or tech-driven rallies. Because the analysis only covers ETF top-10 holdings, overlap is likely understated, and smaller positions outside the top 10 aren’t captured. The practical takeaway is that diversification across funds still leaves a meaningful tilt toward a handful of very large companies that dominate index weights.
Factor exposure is essentially neutral across the board: value, size, momentum, quality, yield, and low volatility all cluster near the 50% “market-like” mark. Factor exposure is like checking whether your portfolio leans toward certain styles (such as cheap, fast-rising, or stable stocks) that research has linked to returns. In this case, there are no strong tilts either toward or away from any factor, meaning behavior should broadly resemble the overall market rather than a niche style strategy. That neutrality is actually a strength for a simple core portfolio, reducing the chance of large performance gaps versus standard benchmarks driven by style cycles you might not be consciously choosing.
Risk contribution shows how much each holding drives the portfolio’s overall ups and downs, which can differ from its weight. Here, the total US market ETF is 60% of assets and contributes about 60% of risk, so it behaves proportionally. The growth ETF is 20% of assets but about 23% of risk, reflecting its slightly higher volatility, while the international ETF contributes somewhat less risk than its weight. This pattern is quite sensible and balanced: there’s no single holding with an outsized risk footprint relative to size. If desired, nudging weights between growth and international could fine-tune the risk mix, but the current structure already lines up risk and allocation fairly well.
The correlation data shows that the total US market ETF and the US growth ETF move almost identically. Correlation measures how often two assets move in the same direction; when it’s very high, they behave like close cousins rather than independent diversifiers. In practice, that means adding the growth ETF on top of the total market ETF mainly tweaks style exposure within the same market, rather than giving a second, unrelated return stream. This isn’t necessarily a problem, because both are low-cost and broad. It just reinforces that diversification benefits are coming more from the international sleeve than from splitting the US exposure into two highly similar funds.
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 risk/return chart shows the current portfolio sitting on or very near the efficient frontier, which is the curve of best possible return for each risk level given the existing holdings. The Sharpe ratio, a measure of return per unit of risk above the risk-free rate, is 0.59 for the current mix. The maximum Sharpe portfolio scores higher at 0.78 with more risk, while the minimum variance option is slightly lower risk with a Sharpe of 0.63. Since the current allocation is effectively on the frontier, the tradeoff between risk and return is already efficient. Any tweaks would mainly reflect personal comfort with volatility rather than fixing an inefficiency.
The overall dividend yield across the portfolio is around 1.3%, with the international ETF offering the highest yield, the US total market in the middle, and the US growth ETF lowest. Dividend yield is the annual cash payout as a percentage of price, and can be a meaningful part of total return over time. Here, the focus is clearly on capital appreciation rather than income generation, which fits a growth-oriented profile. Reinvesting dividends back into the portfolio can quietly boost compounding. For investors seeking regular cash flow, this yield level would likely feel modest, but for long-term growth, lower-yield, higher-growth holdings can still be entirely appropriate.
Costs are impressively low: expense ratios range from 0.03% to 0.05%, with a blended cost of about 0.04%. The expense ratio is the annual fee charged by a fund, and shaving even tiny fractions of a percent can make a real difference over decades because fees compound in reverse. Being this close to rock-bottom index costs is a major structural strength of the portfolio. It means more of the underlying market return ends up in the investor’s pocket instead of being eaten by fees. From a cost perspective, this setup is already doing exactly what you’d want a long-term core allocation to do.
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