This portfolio is a simple four‑fund mix, with a clear tilt toward active US stocks. Roughly 45% sits in an actively managed US equity fund, about 25% in a broad US stock ETF, around 24% in a total bond market fund, and the remaining 7% in a dynamic allocation ETF. So most of the risk and return comes from US stocks, with bonds acting as a stabilizer. The structure is easy to understand and broadly diversified across many underlying holdings. Because the history is only about 1.9 years, anything that looks like a pattern in this mix could just be noise, not a lasting trait, so it’s better to think of these numbers as a snapshot rather than a long-term verdict.
Over the short 1.9‑year window, a hypothetical $1,000 in this portfolio grew to about $1,503, ahead of both the US and global market benchmarks. That translates to a compound annual growth rate (CAGR) of 24.77%, versus 17.32% for the US market and 19.29% for the global market. CAGR is like the average speed of a road trip, smoothing out bumps along the way. The portfolio’s worst drop, or max drawdown, was about -14.8%, milder than both benchmarks. Only 13 days accounted for 90% of returns, showing results were driven by a few strong days. With less than two years of data, though, this outperformance might not be a stable long‑term pattern.
The forward projection uses a Monte Carlo simulation, which basically means the system replays many alternate futures by mixing and matching past return patterns. Here, 1,000 paths over 15 years produce a median outcome of about $2,689 from $1,000, with most outcomes landing between roughly $1,929 and $3,607. The average annual return across simulations is 7.26%, and about 77% of runs end positive. This gives a sense of the range of what “could” happen, not what “will” happen. Because the engine only has about 1.9 years of history to work with, these projections are especially shaky and may not capture how this mix behaves through full market cycles or major regime shifts.
By asset class, the portfolio sits at about 76% stocks and 24% bonds, which lines up with a classic “balanced but growth‑leaning” mix. Stocks are the main engine of long‑term growth, but they swing more, while bonds tend to smooth the ride and add income. Compared with many broad benchmarks that are nearly all equity, this portfolio’s bond sleeve is a clear volatility dampener. The total bond market fund in particular spreads exposure across many issuers and maturities, which can help reduce the impact of any single bond segment. With only 1.9 years of history, though, it’s worth remembering that this balance hasn’t yet been tested across a full interest rate or credit cycle.
This breakdown covers the equity portion of your portfolio only.
Sector-wise, the equity portion leans most heavily into technology at 23%, followed by health care at 15% and industrials at 9%, with smaller slices in areas like financials, consumer sectors, and utilities. This pattern is broadly similar to many modern US equity benchmarks, which are also tech‑heavy, so the portfolio isn’t taking an extreme sector bet. Tech‑rich allocations often participate strongly in growth phases but can be more sensitive when interest rates jump or when markets rotate toward more defensive areas. Because sector returns can be very cyclical over time, a 1.9‑year snapshot may emphasize recent winners and understate how leadership might change over longer horizons.
This breakdown covers the equity portion of your portfolio only.
Geographically, about 67% of the portfolio’s equity exposure points to North America, with modest allocations to developed Europe, emerging Asia, Japan, and other developed Asian markets. This creates a clear home‑country tilt toward the US, which is common given its large share of global market value. The presence of developed and emerging markets, even in smaller weights, does add some global diversification and exposure to different economic and currency environments. Around a quarter of exposure is classed as “no data,” which typically reflects holdings without a clean regional tag in this look‑through framework. Given only 1.9 years of returns, it’s hard to judge how this regional mix behaves across different global cycles.
This breakdown covers the equity portion of your portfolio only.
Market capitalization exposure is dominated by mega‑cap and large‑cap companies, which together make up more than half of the equity allocation, with additional exposure to mid, small, and a small slice of micro‑caps. This is very much in line with broad market‑cap‑weighted approaches, where the largest companies naturally take the biggest weights. Large and mega‑caps tend to be more established and often somewhat more stable, while mid and small companies can be more volatile but sometimes grow faster. Having some exposure across the size spectrum can add diversification because different size segments often lead at different times. Over just 1.9 years, however, any apparent benefit from size tilts may simply reflect short‑term leadership rather than persistent behavior.
This breakdown covers the equity portion of your portfolio only.
The look‑through holdings show that familiar names like Apple, NVIDIA, Microsoft, and Amazon appear via the ETFs, but they only add up to small slices of the total portfolio, with the largest at about 1.7%. This suggests there’s no single stock dominating exposure through these top‑10 ETF positions. The presence of broad international ETFs in the look‑through, such as emerging markets and developed ex‑US funds, reinforces that some global diversification is coming through the index ETF and the dynamic allocation ETF. Coverage of only about 15% of the portfolio means hidden overlap could be higher beneath the surface. With limited history, it’s not yet clear how these overlapping positions influence overall behavior in very stressed markets.
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.
On the factor side, the clearest signal is a very low size exposure, meaning the portfolio tilts strongly away from smaller companies and toward larger ones. Factor exposure is like checking which “traits” drive returns, such as company size or yield. A strong tilt away from small size often leads to behavior that tracks bigger, more established firms more closely, which can feel more stable but may miss periods when smaller stocks outperform. Yield exposure is low, suggesting income isn’t the dominant driver of returns, and low volatility sits around neutral, indicating no strong tilt there. With no data for value, momentum, and quality, and only 1.9 years of history, deeper factor conclusions would be speculative.
Risk contribution shows how much each holding drives the portfolio’s ups and downs, which can differ a lot from its weight. Here, the active US equity fund at about 44.5% weight contributes nearly 70% of total portfolio risk, meaning its behavior largely sets the tone. The broad US stock ETF roughly matches its weight in risk share, while the dynamic allocation ETF adds modest risk. The total bond market fund, despite being almost a quarter of the portfolio by weight, contributes only about 1.6% of total risk, acting mainly as a stabilizer. Overall, the top three holdings account for over 98% of risk, so concentration is really about how those equity funds behave, especially the active fund, which hasn’t yet been observed over a full market cycle.
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‑versus‑return view compares the current mix to an “efficient frontier,” which shows the best expected return for each risk level using only these existing holdings in different weights. Right now, the portfolio sits about 1.12 percentage points below that curve at its current volatility, with a Sharpe ratio of 1.19 versus 1.44 for the optimal mix. The Sharpe ratio measures return per unit of risk, like miles per gallon for a car — higher is better efficiency. This suggests there’s theoretical room to improve the balance just by reweighting, without adding new funds. However, since the inputs are based on less than two years of data, the exact location of the frontier and the “optimal” mix is quite uncertain and could shift meaningfully over time.
The reported total yield of about 12.77% looks very high at first glance, driven mainly by a 25.6% figure attributed to the active equity fund, alongside more typical yields from the bond fund and ETFs. Dividend yield is the income paid out each year as a percentage of the current value. It can matter a lot for investors who care about cash flow, but extremely high yields often flag special factors, such as one‑off distributions or data quirks, rather than a steady income stream. With only 1.9 years of history and such an unusually large number on one holding, it’s safer to treat this yield snapshot cautiously rather than assuming it reflects an ongoing, repeatable income pattern.
Costs for this portfolio are impressively low overall. The total expense ratio (TER) is about 0.13%, with the bond fund at 0.02%, the broad US ETF at 0.03%, and the active equity fund at 0.27%. TER is the annual fee charged by funds, and keeping it low means more of any gross return stays in the portfolio. Over long periods, even small fee differences can compound into meaningful amounts. This fee profile aligns well with cost‑efficient practices, especially given the blend of index and active management. Since these cost figures don’t depend on the short 1.9‑year return history, they’re among the more reliable parts of the analysis and form a solid structural advantage for the portfolio.
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