This portfolio is a fully ETF-based mix with roughly two-thirds in stocks, about one-third in bonds, and a small slice in real estate. No single ETF dominates: the largest position is under 13%, and risk is spread across many funds covering different regions, sizes, and styles. That broad structure is what supports the “highly diversified” score. Because everything is held via funds and assumed buy‑and‑hold, changes in market prices rather than trading drive the behaviour. With only about 1.5 years of data, the structure looks carefully spread out, but any conclusions about how this mix behaves across full market cycles should be held lightly.
Over the 1.5‑year window, $1,000 grew to about $1,278, implying a Compound Annual Growth Rate (CAGR) of 18.51%. CAGR is like average speed on a road trip: it smooths out bumps to show the overall pace. The portfolio’s max drawdown — its biggest peak‑to‑trough drop — was about ‑9.5%, which is relatively mild compared with the US and global benchmarks shown. It slightly outpaced the US market but lagged the global market over this short span. Because this period is brief and specific to one market environment, it doesn’t yet say much about long‑term behaviour or how it might hold up in very different conditions.
The Monte Carlo projection uses the short performance history to simulate many possible 15‑year futures, like running thousands of “what if” market paths. The median outcome turns $1,000 into about $2,470, with a wide possible range from roughly $1,255 to $5,068. The average simulated annual return is 6.7%, and around three‑quarters of paths end positive. These numbers help frame expectations, but they lean heavily on limited data. With only 1.5 years of history, the model may over‑ or under‑estimate both upside and downside. It’s best viewed as a rough map of possibilities, not a forecast of what will actually happen.
Asset‑class-wise, around 64% is in stocks, 32% in bonds, and 3% in real estate. This is a classic “balanced” profile: stocks usually drive growth over time, while bonds and real estate can help smooth the ride by reacting differently to economic news. Compared with a pure equity benchmark, this mix naturally tends to have lower swings but may lag in very strong stock markets. The bond side spans Treasuries, corporates, TIPS, mortgage‑backed, and emerging‑markets debt, which adds internal diversification. Still, because the analysis period is short, the actual cushioning effect of these bonds in various stress events hasn’t been fully tested in the available data.
This breakdown covers the equity portion of your portfolio only.
Sector exposure is spread across many areas: technology leads at 13%, followed by financials, industrials, and real estate, with no single sector overwhelming the portfolio. This broad spread is similar in spirit to diversified global equity benchmarks, which is a strong indicator of sector diversification. Different sectors tend to shine at different points in the economic cycle, so this kind of balance helps avoid depending on one theme. The dedicated real estate exposure in both equities and REITs adds a distinct sensitivity to interest rates and property markets. Over just 1.5 years, though, sector behaviour can be dominated by short‑term narratives rather than long‑term patterns.
This breakdown covers the equity portion of your portfolio only.
Geographically, about 27% is in North America, with meaningful allocations across developed Europe, Japan, developed Asia, and several emerging regions including Asia, Latin America, and Africa/Middle East. This is much more globally spread than a typical US‑heavy portfolio and aligns well with the idea of global diversification. Having multiple regions can soften the impact if one economy struggles, though global markets often move together during big shocks. Compared with a world index that’s usually dominated by the US, this portfolio appears more balanced. That said, the 1.5‑year window might not capture full cycles where regional leadership rotates more dramatically.
This breakdown covers the equity portion of your portfolio only.
By market cap, the portfolio holds a mix of mega‑caps through micro‑caps, with notable exposure to mid‑ and small‑caps. Market capitalization refers to company size; larger companies often move more slowly, while smaller ones can be more volatile and more sensitive to local conditions. This spread helps diversify across business models and growth stages. It also explains part of the portfolio’s risk profile: smaller companies can add return potential but may swing more in tough markets. In the short data window, these size effects may not show clearly, because performance over 1.5 years can be driven by a few macro events rather than persistent size patterns.
This breakdown covers the equity portion of your portfolio only.
Looking through the ETFs’ top holdings, coverage is only about 15% of the portfolio, so overlap is likely understated. Within that visible slice, Taiwan Semiconductor, Apple, NVIDIA, Microsoft, Amazon, Alibaba, Tencent, Alphabet, and Samsung appear across multiple funds, creating some hidden concentration in large global leaders. Overlap means that even if each ETF looks modest on its own, the same companies can end up having a bigger combined impact. Still, given how diversified these firms are across industries and geographies, this is broadly aligned with common global equity exposures. With only top‑10 data and a short history, it’s hard to fully gauge long‑term concentration risks.
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 value, momentum, and yield, with a mild tilt away from smaller size and neutral low‑volatility. Factors are like underlying “traits” — such as cheapness (value) or recent winners (momentum) — that help explain why investments move the way they do. A high value tilt means the portfolio leans more into companies priced relatively cheaply versus fundamentals. High momentum suggests exposure to stocks that have been doing well recently, while high yield points to above‑average income. Together, these tilts can behave differently across market regimes, but over just 1.5 years, it’s too early to say how consistently these factor exposures will show up in returns.
Risk contribution measures how much each holding adds to the portfolio’s overall ups and downs, which can differ from its weight. Here, the three largest risk contributors — emerging‑markets equities and international small‑caps — together account for about 28% of total risk, despite each being under 7% in weight. Their risk/weight ratios above 1 show they punch above their size in driving volatility. This is typical: emerging markets and small‑caps often move more sharply than broad developed markets. The pattern suggests that while weightings look nicely spread out, a meaningful slice of the portfolio’s day‑to‑day movement is tied to these higher‑volatility international and small‑company segments, at least in this short period.
Correlation looks at how holdings move together. Highly correlated assets tend to rise and fall in sync, reducing the diversification benefit. Several pairs here — such as the two emerging‑markets bond ETFs, or the fundamental versus standard small‑cap and emerging‑markets equity funds — move almost identically. In practice, that means those pairs behave more like a single exposure than two independent ones during market swings. This doesn’t make them “bad,” but it does mean the number of line items overstates true diversification. Since correlation can shift over longer cycles, the high similarity observed over just 1.5 years might ease or intensify as different macro environments come and go.
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 analysis compares the current mix with other weightings of the same holdings. The current portfolio’s Sharpe ratio — a measure of return per unit of risk — is 1.21, while the max‑Sharpe version reaches 1.97 and the minimum‑variance version 1.27. Being about 6.3 percentage points below the frontier at its risk level indicates that, historically, a different weighting of these same ETFs could have delivered better risk‑adjusted results. However, this is based on a short 1.5‑year window, where a handful of market events can distort the math. Over a longer period, the “optimal” mix could look quite different, so this is more of a learning tool than a firm guide.
The portfolio’s overall dividend yield is about 2.98%, combining income from bonds, REITs, and equities. Several bond and real‑estate funds show relatively high yields, while growth‑focused equity ETFs yield less. Yield is the income paid out as a percentage of the investment; it can be an important component of total return, especially when price gains are modest. Here, income appears to be a meaningful but not dominant part of expected returns. Because payouts can change with interest rates, company profits, and policy decisions, the current yield level shouldn’t be assumed to stay constant over the long term, particularly given only 1.5 years of observation.
Costs are impressively low: the total ongoing fee (Total TER) is about 0.14% per year. TER, or Total Expense Ratio, is like a service charge baked into each fund’s price — lower costs leave more of any returns in the investor’s pocket. Most holdings here use low‑cost index or rules‑based strategies, with only a few edging above 0.30%. This aligns well with best practices for cost control in diversified portfolios. Over long horizons, even small fee differences can compound into large dollar amounts, so starting from such a low average TER is a meaningful structural strength, even though the available performance history is still short.
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