This portfolio is built mainly from equity ETFs, with a 90% allocation to stocks and the rest in diversifiers like managed futures, long-term Treasuries, gold, and an inflation-focused fund. The largest single holding is a broad U.S. equity ETF at 25%, with several 10% positions in U.S., international, emerging markets, small-cap value, and momentum strategies. This creates a core‑satellite feel: a broad core plus more specialized tilts. Because everything is implemented through funds, you’re diversified across many companies rather than a few single stocks. The mix suggests an intentional design to capture equity growth while adding a few non‑stock pieces that may behave differently in stress periods.
Over the roughly 1.1‑year window, a hypothetical $1,000 grew to about $1,449, implying a 39.13% compound annual growth rate (CAGR). CAGR is the “average yearly speed” over the period, smoothing out bumps along the way. That’s noticeably higher than both the U.S. and global market benchmarks, which ran in the mid‑20% range. The portfolio’s maximum drawdown, about -13%, was similar to the benchmarks’ worst dips, so the extra return didn’t come with clearly higher downside over this short span. With only a year of data, though, it’s hard to treat this outperformance or the risk pattern as a stable long‑term characteristic.
The Monte Carlo projection uses the recent return and risk numbers to simulate many possible 15‑year paths for a $1,000 investment. In each simulation, it “shuffles” monthly returns in a realistic way, then calculates the ending value, giving a range instead of a single forecast. Here, the median outcome lands around $2,634, with most simulations falling between about $1,789 and $3,951. The wide $1,042–$7,130 possible range shows how uncertain long‑term results can be. Because the input history is just over a year, these simulations lean heavily on a short, strong performance phase, which makes them less reliable as a guide to what might happen over full market cycles.
Asset‑class‑wise, this is clearly an equity‑driven portfolio: 90% in stocks, 6% in “other” assets, and 4% with no data. That high stock share is typical of growth‑oriented approaches, where long‑term return potential is prioritized over short‑term stability. The “other” slice includes managed futures, gold, and inflation‑focused and long‑duration bond exposures, which tend to behave differently from equities at various times. Even though they’re small weights, these diversifiers can sometimes help smooth the ride when stock markets move together. The 4% “no data” bucket simply reflects missing classification, so it’s treated as an unknown rather than as a specific asset class in this analysis.
This breakdown covers the equity portion of your portfolio only.
Sector exposure is spread across many areas, with technology at 22% followed by financials at 16% and industrials at 14%. Consumer‑related sectors, energy, materials, telecom, and health care are all meaningfully represented, and real estate is a smaller slice. This looks more like a broad, diversified equity mix than a single‑theme portfolio. A notable point is the fairly high tech weight, which is common in modern equity portfolios because many large, influential companies sit in that sector. Tech‑heavy allocations can be especially sensitive to interest‑rate moves and growth expectations, while stronger weights in financials and industrials often tie more to economic cycles and business activity.
This breakdown covers the equity portion of your portfolio only.
Geographically, about 52% of the equity exposure is in North America, with the rest spread across developed Europe and Asia, Japan, emerging Asia, Latin America, Africa/Middle East, and Australasia. That North American tilt is broadly in line with global market weights, where U.S. and Canadian stocks also make up a bit more than half of global market value. Compared with a home‑only portfolio, this structure is meaningfully more global, which helps reduce reliance on a single economy or currency. Non‑U.S. and emerging markets exposure also introduces different growth, inflation, and policy environments, so returns can diverge from U.S. trends at times, adding another layer of diversification.
This breakdown covers the equity portion of your portfolio only.
The market‑cap breakdown shows 29% in mega‑caps and 23% in large‑caps, but also substantial exposure further down the size spectrum: 19% mid‑caps, 12% small‑caps, and 6% micro‑caps. Market capitalization simply measures a company’s total value on the stock market, and different size groups often behave differently across cycles. Mega and large companies tend to be more established and sometimes more stable, while smaller companies can be more volatile but also more sensitive to economic upswings. This blend suggests the portfolio doesn’t just mirror a mega‑cap index; it intentionally reaches into smaller firms, which can change the pattern of returns and risk versus a pure “big company” portfolio.
This breakdown covers the equity portion of your portfolio only.
Looking through the ETFs’ disclosed top‑10 holdings, a few large names show up repeatedly: NVIDIA, Alphabet (both share classes), Apple, Broadcom, Taiwan Semiconductor, Micron, SK Hynix, Amazon, and Microsoft. Together, these account for a modest portion of the overall portfolio, with the single largest around 2.2%. Overlap matters because the same company held via multiple funds can quietly drive more of the overall performance than any single ETF weight suggests. Here, overlap appears concentrated in leading tech and semiconductor names, which reinforces the sector’s influence. Since only ETF top‑10 holdings are used, actual overlap is likely higher, so the true concentration may be understated by these figures.
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.
Factor data points to strong tilts toward both value and momentum. Factor exposure is like checking which “ingredients” dominate the recipe: value stocks trade at lower prices relative to fundamentals, while momentum stocks have been recent strong performers. A 73% value score and 75% momentum score both sit clearly above the neutral 50% level, indicating purposeful tilts rather than subtle drift. Size exposure is 21%, which suggests a mild lean away from the smallest stocks overall, even though specific small‑cap funds are present. Yield and low‑volatility both sit near neutral. Historically, value and momentum can behave quite differently across market regimes, so combining them can sometimes diversify factor‑specific ups and downs.
Risk contribution shows how much each holding drives the portfolio’s overall ups and downs, which can differ from simple weights. The largest U.S. equity ETF is 25% of assets and contributes about 26% of total risk, roughly proportional. The S&P 500 momentum ETF and U.S. small‑cap value ETF each sit at 10% weight but contribute slightly more than 11–12% of risk, reflecting their somewhat higher volatility. The top three positions together account for about 49% of portfolio risk, close to half. That level of concentration is noticeable but not extreme for a 14‑holding mix, indicating that while risk is somewhat focused in the core U.S. sleeves, it isn’t dominated by a single fund.
The correlation data flags two pairs that have historically moved almost in lockstep: the international equity ETF with the international small‑cap value ETF, and the emerging markets equity ETF with the emerging markets value ETF. Correlation measures how often assets move together: a correlation close to 1 means they usually rise and fall at the same time. When funds are this tightly linked, holding both still adds diversification at the stock level but less so at the “behavior” level during big market moves. Over just 1.1 years, though, correlation readings can be noisy; they might change as markets cycle through different macro environments and leadership patterns.
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.
On the risk‑versus‑return chart, the current mix sits below the efficient frontier, with a Sharpe ratio of 1.81. The Sharpe ratio compares excess return (over cash) to volatility, like asking how much “reward” you’re getting for each unit of “choppiness.” The optimal portfolio, using the same set of holdings but different weights, shows a higher Sharpe of 3.12 at slightly higher volatility, while the minimum‑variance mix has lower risk but also lower returns. Being 18.8 percentage points below the frontier at this risk level suggests that, historically, other combinations of these same ETFs would have delivered better risk‑adjusted results. With only a year of data, though, this optimization is very sensitive to recent winners.
The blended dividend yield sits around 1.79%, reflecting a mix of low‑yield growth‑oriented ETFs and higher‑yield bond, inflation, and managed futures funds. Dividend yield is the annual cash payout relative to price, and it can be an important part of total return over long periods. Several international and value‑oriented funds yield above 2%, while momentum‑focused equity ETFs tend to yield under 1%, consistent with their growth and price‑trend focus. The long‑duration Treasury and inflation funds show higher stated yields, which are more interest‑like. Over such a short history, the pattern of dividend payments hasn’t had much time to compound, but structurally the portfolio is set up to draw more return from price movement than from income.
The weighted ongoing fee, or total TER, is about 0.22% per year. TER (Total Expense Ratio) is what the funds charge annually to cover management and operating costs, and it’s taken directly out of returns. This level is quite competitive for an actively tilted but ETF‑based portfolio and compares favorably with many traditional mutual funds. Some specialized diversifiers, like managed futures and inflation strategies, carry higher TERs (0.6–0.8%), but they’re small weights so they don’t meaningfully push up the overall cost. Keeping the blended fee low helps more of the portfolio’s gross returns show up in your net results over time, especially once compounding plays out over many years.
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