This portfolio is a pure equity mix built from broad index funds and several momentum and small‑cap value ETFs. Around half sits in a total US stock market fund, with another chunk in a total international fund, forming a global core. The remaining positions lean into specific styles: US large growth, US and international small value, and both US and international momentum. This kind of “core plus satellites” structure is common: a broad base plus targeted tilts layered on top. With everything in stocks, there’s no built‑in stabilizer like bonds or cash, so day‑to‑day moves will reflect equity markets quite closely. The short 1.2‑year history means any pattern seen so far should be treated as early and potentially temporary.
One or more local-currency benchmark funds are unavailable for this report.
Over roughly 1.2 years, $1,000 in this portfolio grew to about $1,373, implying a compound annual growth rate (CAGR) near 29.8%. CAGR is the “average speed” of growth per year, smoothing out bumps along the way. Compared with the global market benchmark, the portfolio’s return was higher, and maximum drawdown — the deepest peak‑to‑trough fall — was similar at about –14%. Ten days accounted for 90% of returns, which is typical for equities where a few strong days matter a lot. Because momentum and growth styles have done well recently, and the period is short, this outperformance may simply reflect a favorable window rather than a reliable long‑term pattern.
The forward projection uses Monte Carlo simulation, which takes the portfolio’s recent return and volatility patterns and shuffles them thousands of times to create many possible 15‑year paths. Think of it as rolling dice based on past behavior to see a range of future outcomes. The median outcome grows $1,000 to about $2,667, with a wide “likely” band and some paths ending near the starting value. The average simulated annual return is around 8%. Because the input history is only about 1.2 years, these numbers are especially fragile — they lean heavily on a strong recent run and may not reflect full market cycles, interest‑rate environments, or stress periods that didn’t appear in the data.
Asset‑class exposure here is straightforward: 100% stocks. There’s no allocation to bonds, cash, or alternatives. This is important because different asset classes usually react differently to economic news; mixing them can help smooth overall portfolio swings. With only equities, especially when combined with growth and momentum tilts, the ride will generally be bumpier than a blend that includes steadier assets. Over long horizons, stocks have historically offered higher potential returns than bonds, but also deeper and more frequent drawdowns. Given the limited 1.2‑year sample, the relatively mild maximum drawdown seen so far doesn’t fully represent how a pure‑stock mix might behave across more severe or prolonged downturns.
Sector‑wise, technology is the largest slice at about 30%, followed by financials, industrials, and consumer areas, with smaller pieces in energy, materials, staples, utilities, and real estate. This is more tech‑heavy than many broad global benchmarks, reflecting both the total‑market funds’ composition and the growth and momentum satellites. Sector weights matter because different parts of the economy respond differently to interest rates, inflation, and regulation. Tech‑heavy portfolios often see larger swings when growth expectations or interest rates shift sharply. The presence of multiple other sectors helps, but leadership has recently been concentrated in technology‑related names, which means the strong short‑term performance could be closely tied to this particular sector environment.
Geographically, about 77% is in North America, with the rest spread across developed Europe, Japan, other developed Asia, emerging Asia, Australasia, and a small slice in Africa/Middle East. That US‑heavy tilt is common among American investors and has worked well in the past decade as US markets outpaced many others. Relative to a fully global market index, though, it does lean more toward the US. Geographic spread matters because economies, currencies, and policy cycles can diverge. A strong US bias means returns are more tied to one economy and one currency, even though there is still meaningful exposure to other regions. Over only 1.2 years, it’s hard to see how this global mix behaves across different global market regimes.
By market capitalization, the portfolio leans toward larger companies: roughly 38% mega‑cap, 28% large‑cap, then meaningful stakes in mid‑cap, small‑cap, and a smaller micro‑cap slice. This resembles a broad market base but with a slightly stronger tilt to smaller companies than a pure mega/large‑cap index would have. Size exposure matters because small‑caps often move more sharply — both up and down — than giant firms, reflecting more business risk and less diversified revenue. Integrating small and micro‑caps can increase the diversification of drivers behind returns, but also adds volatility. In just over a year of data, the specific impact of this small‑cap segment may be muted or exaggerated by the particular phase of the market we’ve just experienced.
Looking through the ETFs’ top holdings, a handful of large US technology‑related companies dominate the visible overlap: NVIDIA, Apple, Microsoft, Amazon, Alphabet, Broadcom, Meta, Tesla, and Berkshire Hathaway together make up a noticeable chunk. These names appear across multiple funds, especially the total‑market, growth, and momentum ETFs, creating “hidden” concentration even though each fund looks diversified on its own. Because only ETF top‑10 holdings are used, actual overlap is likely higher than the reported 30.9% coverage. This kind of overlap means portfolio performance can be heavily influenced by how these few mega‑cap stocks behave, especially in short periods like the 1.2‑year window used here, whether they are in favor or out of favor.
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 two notable tilts: a very low size score (13%) and high momentum (75%), with other factors roughly neutral and no data for quality. Factors are characteristics like value, size, or momentum that research links to long‑term return patterns. A very low size score means a strong tilt toward larger companies and away from smaller ones overall, despite some small‑cap funds. High momentum indicates a preference for stocks that have recently performed well. In friendly markets, momentum can boost returns as trends persist, which may partly explain the strong short‑period performance. However, during sharp reversals, momentum strategies can lag. With only 1.2 years of history, the measured factor effects may not reflect how these tilts behave through full cycles.
Risk contribution data shows that the 50% allocation to the total US stock market fund contributes about 50% of total portfolio risk, almost exactly in line with its weight. The total international fund at 15% weight contributes slightly less risk than its size, while the US large‑cap growth and momentum ETFs contribute modestly more risk than their weights suggest. In total, the top three holdings account for about 75% of overall risk, which is consistent with a concentrated core structure. Risk contribution matters because a position’s impact on volatility can be very different from its dollar share. Here, the core funds largely drive the portfolio’s ups and downs, with the satellites adding incremental, not dominant, risk so far.
The correlation section highlights that the Schwab US Large‑Cap Growth ETF moves almost identically to the total US stock market ETF. Correlation measures how similarly two assets move; a value close to 1 means they tend to go up and down together. Highly correlated holdings offer less diversification benefit because they respond similarly to market shocks. In this case, the growth ETF is effectively a more focused slice of what the total market fund already owns, which can amplify exposure to similar underlying drivers like big US tech and growth names. Over the limited 1.2‑year window, this tight linkage may partly reflect the dominance of a few mega‑cap stocks, and it could evolve if leadership in the market broadens or shifts.
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‑return optimization chart compares the current portfolio to an “efficient frontier” built from the same holdings. The current mix has a Sharpe ratio of about 1.34, while the optimal mix (best risk‑adjusted return) is higher at 2.05, and the minimum‑variance mix sits in between. Sharpe ratio is a way of comparing return to volatility, similar to asking which car covers the most distance per unit of fuel. Being about 9 percentage points below the efficient frontier at the current risk level means that, historically, different weightings of these same ETFs would have produced higher return for similar or lower volatility. With only 1.2 years of data, though, this “inefficiency” may mostly reflect a short, style‑driven period.
The portfolio’s overall dividend yield is about 1.46%, driven mainly by the international small‑cap value and international developed momentum funds, which have higher yields than the growth and momentum‑heavy US funds. Dividend yield is the cash income investors receive each year as a percentage of investment value. Here, income plays a secondary role compared with capital growth, which fits with the tilt toward growth and momentum styles that often reinvest earnings rather than paying them out. Over longer horizons, reinvested dividends can be an important part of total return, even if the starting yield looks modest. Because the history is just over a year, the observed yield may not fully reflect how payouts change across market and interest‑rate cycles.
Costs in this portfolio are quite low overall, with a blended total expense ratio (TER) of about 0.08%. TER is an annual fee charged by funds, expressed as a percentage of invested assets, and it quietly subtracts from returns each year. Most of the weight sits in very low‑cost index ETFs from Vanguard and Schwab, while the more specialized Avantis and Invesco funds cost more but form a smaller share. This structure helps keep the average down, which supports better compounding over time compared with higher‑fee approaches tracking similar markets. Even though the performance history here is short, starting from a low‑fee base is generally a solid structural feature, because costs are one of the few variables investors can reliably observe upfront.
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