This portfolio is a 100% stock mix built entirely from seven equity ETFs, with no bonds or cash layer. About one third sits in a broad US core fund, while the rest is spread across US small cap value, international core, international small value, emerging markets, and both US and international momentum strategies. That structure mixes broad “total market” style exposure with more focused factor sleeves. A setup like this puts long‑term growth at the center, with day‑to‑day ups and downs driven by stock markets. The absence of defensive assets means the portfolio will likely move more sharply in both directions compared to mixes that include bonds or cash buffers.
Over the period from late 2021 to mid‑2026, a hypothetical $1,000 in this portfolio grew to about $1,833. That works out to a compound annual growth rate (CAGR) of 13.87%, meaning the money grew roughly 13.87% per year on average, similar to averaging speed over a whole road trip. This beat both the US market (13.08%) and the global market (10.91%) over the same span. The worst peak‑to‑trough fall, or max drawdown, was about −23.9%, slightly milder than the US benchmark. Just 19 days made up 90% of total returns, showing that missing a handful of strong days would have mattered a lot. Past returns like this are useful context but never a guarantee.
The forward projection uses a Monte Carlo simulation, which is basically a thousand “what if” reruns of history with returns shuffled around. Using past volatility and returns as inputs, it estimates many possible 15‑year paths for a $1,000 investment. The median outcome lands near $2,710, with a middle “likely” band between about $1,787 and $4,075, and a wider possible range from roughly $1,090 to $7,696. The average simulated annual return is 8.13%, and about 73% of scenarios end positive. This shows a wide fan of outcomes: same portfolio, very different potential paths. These numbers lean heavily on history and assumptions, so they’re illustrations of risk and range, not promises.
Every dollar here is invested in stocks, with a 0% allocation to bonds, cash, or alternatives. That makes the asset‑class picture simple but also concentrated in one risk type: equity market risk. Broad equity exposure tends to offer higher long‑run growth expectations than safer assets, but with steeper drawdowns when markets fall. Compared to a classic balanced portfolio that mixes in bonds, this setup is more growth‑oriented and more sensitive to stock market swings. The “balanced” label in the risk classification is coming from the tool’s scale, not from an actual split between stocks and bonds. Over shorter stretches, returns can be quite lumpy, even if the long‑term trend is upward.
Sector‑wise, the portfolio is led by technology at 26%, followed by financials at 16% and industrials at 14%. Consumer‑facing areas like discretionary and staples together make up about 14%, while energy, telecoms, and basic materials collectively add meaningful cyclicality. Health care sits at 6%, and more defensive spaces like utilities and real estate are quite small. Compared with a broad global index, the tech allocation is elevated, and the lower utilities and real estate weights reduce classic “defensive” ballast. Tech‑ and cyclicals‑heavy mixes often do well in growth‑friendly environments but can be more sensitive when rates rise or economic expectations suddenly weaken. The spread across many sectors still provides a good base of diversification.
Geographically, about 68% of the portfolio is tied to North America, with the rest spread across developed Europe, Japan, other developed Asia, emerging Asia, Africa/Middle East, Australasia, and Latin America. A typical global equity index today has closer to 60% in North America, so this portfolio has a modest home‑country tilt toward the US. That tilt has helped in recent years, as US markets outpaced many others. Meaningful slices in developed and emerging markets outside North America add currency and economic diversification, so not everything is riding on one region. However, the majority outcome is still linked to the health of the US economy and dollar‑based markets, which can matter if leadership rotates internationally.
The market cap mix is fairly broad: around 31% in mega‑caps, 26% in large‑caps, 19% in mid‑caps, 15% in small‑caps, and 8% in micro‑caps. Compared with a cap‑weighted world index, this is noticeably more tilted toward smaller companies, mainly driven by the dedicated small cap value ETFs. Company size matters because smaller firms often move more sharply, both up and down, and can behave differently from giants dominating the headlines. Broad exposure across the size spectrum can improve diversification since different size groups tend to lead in different cycles. At the same time, the added small and micro allocation can increase volatility and make periods of underperformance versus a large‑cap index feel more pronounced.
Looking through ETF top‑10 holdings, a handful of big US names stand out, including NVIDIA, Broadcom, Apple, Alphabet, Microsoft, Amazon, Meta, and Micron. These positions appear across multiple funds, so their combined portfolio weights are higher than any single ETF suggests, even though the coverage only captures about 26% of total holdings. For example, NVIDIA shows up around 3.9% overall, while several other mega‑caps sit between 1–2%. This kind of overlap creates some hidden concentration in large US growth names within an otherwise factor‑tilted portfolio. Because only top‑10 holdings are visible, the true overlap is probably somewhat higher and spread across more companies than shown here.
On factor exposure, the standout tilt is toward value at 64%, which is described as “high” and meaningfully above the market‑like 50% level. Factor exposure is basically how much the portfolio leans into certain characteristics, such as cheapness (value) or trendiness (momentum), that research links to long‑term return patterns. The other factors—size, momentum, quality, low volatility, and yield—sit in the neutral band around 50%, so they behave broadly like the overall market. A noticeable value tilt can help when cheaper stocks regain favor after periods when growth and glamour names have led. The flip side is that value‑heavy portfolios can lag during stretches when investors strongly reward fast‑growing or high‑story companies.
Risk contribution measures how much each holding adds to the portfolio’s overall ups and downs, which can differ from its simple weight. The Dimensional US Equity ETF is 35% of the allocation and contributes about 35.3% of total risk, so its impact is very much in line with size. The US small cap value ETF, at 15% weight, contributes around 17.8% of risk, showing it’s slightly more of a “loud instrument” in the overall orchestra. The two Dimensional and Avantis international funds contribute slightly less risk than their weights. Overall, the top three holdings drive roughly 69% of total risk, which is a meaningful but not extreme concentration, consistent with a core‑plus‑satellite structure.
The correlation view flags that the international small cap value ETF and the international ex‑US core ETF have moved almost identically. Correlation is a simple measure of how often two investments move in the same direction: a high number means they usually rise and fall together. When two holdings are very tightly linked, holding both still adds diversification at the company level but adds less diversification at the return‑pattern level. In practice, that means changes affecting non‑US developed markets are likely to show up similarly in both funds. Nonetheless, differences in size, style, and underlying stock lists can still create useful variation within that broad regional bucket over longer periods.
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 vs. return chart shows the current portfolio with a Sharpe ratio of 0.63, below both the optimal portfolio’s 1.03 and the minimum variance portfolio’s 0.68. The Sharpe ratio compares extra return above a risk‑free rate to volatility, a bit like measuring how efficiently risk is being turned into reward. The efficient frontier curve represents the best possible tradeoffs using only these existing holdings with different weightings. At its current risk level, the portfolio sits about 2.9 percentage points below that frontier, meaning the same ingredients could have historically produced a better balance of return and volatility with altered weights. That said, the current mix still lands within a reasonable region of the curve.
The portfolio’s overall dividend yield is about 1.48%, combining higher‑yielding international and value funds with lower‑yielding US and momentum exposures. Dividend yield is simply the cash income from holdings over a year, as a percentage of the investment value. Here, income plays a secondary role; most of the expected return comes from price changes rather than payouts. Some components, like the international momentum ETF at 3.5% yield, offer relatively stronger income contributions, while the US momentum and US core funds yield under 1%. For a 100% equity, factor‑tilted portfolio, this moderate overall yield is typical and can still provide a steady, if modest, stream of cash alongside growth.
The weighted total expense ratio (TER) comes in around 0.20%, which is low for an actively tilted, factor‑oriented equity mix. TER is the annual fee taken by each ETF, expressed as a percentage of assets, a bit like a management subscription baked into the fund price. Individual fund fees range from 0.09% on the broad US ETF up to 0.36% on the international small cap value fund, reflecting the added complexity of those strategies and markets. Over many years, even small fee differences can compound meaningfully. Here, the overall cost is impressively restrained given the use of multiple specialized strategies, helping more of the portfolio’s gross return show up in net performance.
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