This portfolio is mostly invested in stock ETFs, with about 90% in equities and a small sleeve in diversifiers like managed futures, gold, inflation-focused assets, and long Treasuries. The biggest single holding is a broad US equity ETF at 20%, followed by a mix of US and international value, small cap, and momentum strategies typically around 5–10% each. This creates a core‑and‑satellite feel: a diversified equity core plus more focused factor and alternative exposures. Because the history is just over a year, it’s hard to say these weights form a long-term pattern, but structurally it’s a growth‑oriented equity portfolio with a thin but meaningful allocation to risk buffers outside traditional stocks.
Over the short 1.1‑year window, $1,000 in this portfolio grew to about $1,482, implying a compound annual growth rate (CAGR) near 41.8%. CAGR is like the average yearly “speed” of growth, smoothing out bumps along the way. The portfolio’s max drawdown, or worst peak‑to‑trough drop, was about -13.1%, similar to the US and global benchmarks. Over this brief period it outpaced both the US market and global market by more than 12 percentage points per year, which is notable but heavily influenced by momentum and factor performance in this specific timeframe. With so little history, this outperformance should be seen as a snapshot, not a reliable long‑term pattern.
The Monte Carlo projection uses the limited past data to simulate many possible 15‑year paths for $1,000 invested. Monte Carlo is basically a “what if machine” that shakes returns randomly based on historical behavior to get a range of future outcomes. Here, the median outcome lands around $2,633, with a wide middle band from roughly $1,750 to $3,967. There’s about a 72.5% rate of simulations ending positive, and the average annualized return across all paths is about 7.7%. Because the input history is only about a year and unusually strong, these projections likely lean optimistic and shouldn’t be treated as a firm forecast, more as a rough illustration of how variable long‑term equity returns can be.
Asset‑class-wise, this is clearly an equity‑led portfolio: about 90% in stocks, 6% in “other” (which includes things like managed futures and gold), and 4% with no asset‑class data reported. In broad market indices, stocks often dominate growth‑oriented strategies, so this is in line with a return‑focused approach rather than a conservative one. The small slice in alternatives can sometimes move differently from stocks, which may help during equity stress, but at 6% it’s more of a supporting role than a main risk driver. Because there’s limited history, it’s too early to judge how much these diversifiers actually cushioned volatility in real market swings.
This breakdown covers the equity portion of your portfolio only.
Sector exposure is quite spread out, with technology the largest at 23%, followed by financials (16%) and industrials (14%), then consumer discretionary, energy, and others in smaller slices. Compared with common global benchmarks, tech is important but not overwhelmingly dominant, and there is meaningful presence in cyclicals like financials and industrials plus some defensives like health care and utilities. Sector diversification helps avoid being overly dependent on one type of business or economic story. Tech and cyclical tilts can boost returns when growth and risk‑taking are rewarded, but may increase sensitivity to economic slowdowns or higher interest rates. With only a year of data, it’s hard to separate true sector behavior from short‑term cycles.
This breakdown covers the equity portion of your portfolio only.
Geographically, about 52% of the portfolio sits in North America, with the rest spread across developed Europe and Asia, Japan, emerging Asian markets, Latin America, Africa/Middle East, and Australasia. This means more than half the exposure is tied to the US and Canadian economies and currencies, while a substantial minority tracks the rest of the world. Relative to a pure global market index, this looks moderately US‑tilted but still broadly diversified abroad. Geographic spread can help when different regions move on different economic cycles. At the same time, currency moves and local policy changes can drive returns in ways that don’t show up clearly over a single year of history.
This breakdown covers the equity portion of your portfolio only.
By market capitalization, the portfolio leans toward bigger companies: 29% mega‑cap and 24% large‑cap, with the rest in mid (19%), small (12%), and micro (6%) caps. This structure roughly mirrors a broad equity universe but with a notable chunk in smaller stocks via dedicated small‑cap and value ETFs. Market cap matters because larger companies often have more stable earnings and liquidity, while smaller ones can be more volatile but sometimes deliver stronger returns over longer periods. The mix here suggests a base of large, established firms with a meaningful tilt toward smaller names. Given the short track record, though, any apparent size‑related performance pattern should be treated as tentative.
This breakdown covers the equity portion of your portfolio only.
Looking through ETF top‑10 holdings, coverage reaches about a quarter of the portfolio, so the overlap picture is incomplete but still informative. Several major tech‑related names appear multiple times: NVIDIA, Alphabet (both share classes), Apple, Microsoft, Broadcom, plus chipmakers like Micron, SK Hynix, and TSMC. When the same company shows up in several funds, its true influence on returns and risk is higher than any single ETF weight suggests. Here, the repeated presence of large technology and semiconductor names adds a hidden concentration toward that theme. Because only top‑10 holdings are used, actual overlap is likely somewhat higher than shown, especially in cap‑weighted and momentum strategies.
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 exposure shows strong tilts toward quality (very high), momentum (high), and value (high), with a lower tilt toward size and more neutral exposure to yield and low volatility. Factors are like the underlying “traits” that drive return patterns — things like cheapness (value), recent winners (momentum), or financial strength (quality). A strong quality tilt suggests holdings with robust profitability and balance sheets, which historically have weathered downturns better on average. High momentum exposure tends to shine when trends persist but can hurt when markets sharply reverse. The value tilt leans toward cheaper stocks, which sometimes lag for long stretches. With only 1.1 years of data, these factor tilts describe portfolio design more than proven performance behavior.
Risk contribution, which measures how much each holding adds to overall ups and downs, broadly tracks position size here. The 20% US equity ETF contributes about 20.4% of total risk, showing a near one‑to‑one relationship. The S&P 500 momentum fund, at 10% weight, contributes around 12.1% of risk, a bit more than its size suggests, reflecting its higher volatility and factor intensity. Similarly, the small‑cap value and emerging markets funds each contribute just over 10% of risk from 10% weights. The top three holdings together drive roughly 43% of portfolio risk, signaling a meaningful but not extreme concentration. This pattern is fairly typical for an equity‑heavy portfolio centered on broad and factor‑tilted funds.
Correlation looks at how different holdings move together; values near +1 mean they often move in the same direction. Here, some pairs — like the international equity ETF and international small‑cap value ETF, and the two emerging markets equity/value funds — behave very similarly. That’s not surprising, since they draw from overlapping universes. High correlation can limit diversification benefits, especially during region‑specific downturns when everything tied to that area may fall at once. The presence of managed futures, gold, and long Treasuries should introduce assets that can behave differently, but with only about a year of data, their true diversifying power across a full market cycle can’t be confidently assessed yet.
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‑return chart, the current portfolio sits below the efficient frontier by about 18.9 percentage points at its risk level. The efficient frontier is the curve of best possible returns for each level of risk using just these holdings in different mixes. The Sharpe ratio — a measure of return per unit of risk — is 1.89 for the current allocation, versus 3.19 for the “optimal” reweighted mix and 1.44 for the minimum‑variance mix. This suggests that, within this set of ETFs, a different weighting could have delivered better risk‑adjusted returns over the short sample. Because the input history is unusually strong and brief, it’s not clear whether those optimized weights would behave better over a full market cycle.
The portfolio’s overall dividend yield is about 1.74%, which is relatively modest for an equity‑heavy allocation. Yield is the income paid out as dividends relative to price, and it can be an important part of total return over long horizons. Several components, like international value and emerging markets value ETFs, offer yields around 2–3%, while the bond‑like and inflation‑focused holdings show higher yields above 4–5%. On the other side, momentum and quality growth‑oriented funds tend to have lower yields, reflecting a tilt toward companies that reinvest rather than pay out cash. Over just one year, the income stream may look noisy; dividend policies and distributions usually matter more when viewed over many years.
The weighted average total expense ratio (TER) is about 0.22%, which is quite competitive for an actively factor‑tilted and alternative‑inclusive ETF lineup. TER is the annual fee charged by the funds, taken directly from assets, so lower costs leave more return in your pocket over time. Some specialized strategies here, like managed futures and inflation‑focused ETFs, carry higher fees near 0.7–0.8%, but they’re small weights in the portfolio. The core equity holdings sit in the low‑to‑mid 0.1–0.3% range, which is in line with many modern ETFs. Even though the portfolio has only a short history, the structural cost level is clearly a strength that compounds in your favor in the long run.
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