This portfolio is a straightforward global equity mix, with 95% in stocks and 5% in listed real estate. The core is split evenly across US large caps, developed ex-US markets, and emerging markets, each at 25%. Around 20% goes to US mid- and small-cap value stocks, and the remaining 5% to a diversified real estate ETF. This structure makes equities the main driver of returns and risk, while real estate adds a distinct income-oriented slice. A buy-and-hold assumption without rebalancing means that, over time, stronger performers could grow into a bigger share, subtly changing the risk profile compared with today’s starting weights.
From 2016 to 2026, $1,000 in this portfolio grew to about $2,958, a compound annual growth rate (CAGR) of 11.49%. CAGR is like your average speed on a long road trip – it smooths out the bumps. This lagged both the US market (15.40%) and the global market (12.77%) but still represents solid long-term growth. The worst drop, or max drawdown, was -36.32% during early 2020, slightly deeper than the benchmarks’ declines. That illustrates how a value and small-cap tilt can bite harder in sharp stress events. As always, past performance shows how the mix behaved, but it doesn’t guarantee what happens next.
The Monte Carlo projection uses many randomised “what if” paths based on historical patterns to estimate possible futures. Here, 1,000 simulations over 15 years put the median outcome for $1,000 at about $2,871, or an annualised 8.29% across all runs. Think of it as running the same movie with slightly different weather, interest rates, and market swings each time. The 5th–95th percentile range ($1,107–$7,445) shows results can vary a lot even with the same starting portfolio. Importantly, Monte Carlo relies on the past to model the future, so it can’t foresee structural changes, regime shifts, or rare events that haven’t shown up in the data.
Asset class exposure is very clear: almost everything is in equities, with a small 5% slice in real estate. That makes this a growth-oriented structure where portfolio ups and downs are tightly linked to stock markets rather than bonds or cash. The presence of real estate adds a different economic driver, linked to property values and rental income, without changing the fundamental equity character. Compared with many “balanced” mixes that include bonds, this is closer to a pure equity allocation. That helps explain the stronger long-term growth potential alongside the higher volatility visible in the historical drawdown numbers.
This breakdown covers the equity portion of your portfolio only.
Sector allocation is broad and well spread. Technology leads at 20%, followed by financials (18%) and industrials (12%), with consumer, health care, real estate, telecoms, materials, energy, staples, and utilities all meaningfully represented. This balance looks similar to diversified global equity benchmarks rather than a narrow thematic bet. A tech share in the low-20% range is more moderate than many cap-weighted global indices today, which helps avoid over-reliance on a single growth engine. In general, this kind of sector spread means different parts of the economy can offset each other when individual industries hit rough patches or benefit from specific tailwinds.
This breakdown covers the equity portion of your portfolio only.
Geographically, the portfolio is anchored in North America at 53%, with the rest spread across developed Europe, Japan, developed Asia, and a meaningful 18% combined across emerging regions (Asia, Latin America, Africa/Middle East, Europe emerging). This is more globally diversified than a pure US allocation and actually leans a bit more toward emerging markets than many world indices. That alignment with global patterns is a positive sign for diversification, as it reduces dependence on a single economy or currency. At the same time, greater emerging exposure can introduce more volatility and sensitivity to local political and economic developments.
This breakdown covers the equity portion of your portfolio only.
The size breakdown is nicely tiered: 34% mega-cap, 25% large-cap, 17% mid-cap, 15% small-cap, and 6% micro-cap. Compared with a typical global index, this is heavier in mid, small, and micro companies, largely because of the dedicated US value ETFs. Smaller firms tend to be more sensitive to economic cycles and can swing more in price, but they also provide different growth drivers than the largest global giants. This size mix broadens diversification beyond household names, while the still substantial mega- and large-cap share keeps a strong anchor in more established, widely traded companies.
This breakdown covers the equity portion of your portfolio only.
Looking through the ETFs’ top holdings, exposure is spread across major global names like Taiwan Semiconductor, NVIDIA, Apple, Microsoft, Amazon, Tencent, Alphabet, Broadcom, and Alibaba, plus a large internal Vanguard REIT fund. The largest single underlying company, TSMC, is only about 3.2% of the portfolio, and others are under 2%, which limits single-name concentration at the top level. Some overlap clearly exists, with the same tech and consumer giants appearing in multiple funds, but overall weight remains modest. Because only ETF top-10s are included, actual overlap is likely somewhat higher, yet still appears far from extreme concentration.
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 a notable tilt toward value at 61%, while size, momentum, quality, yield, and low volatility are all around the neutral band. Factors are like investing “ingredients” – value, for example, tilts toward cheaper stocks relative to fundamentals. A mild value tilt means the portfolio tends to lean into less expensive companies, which historically has sometimes helped when growth stocks cool off but can lag during strong growth or tech-led rallies. The near-neutral readings on other factors indicate behaviour is broadly market-like in those dimensions, so performance differences versus broad indices are likely driven mainly by that value slant and the size mix.
Risk contribution reveals how much each holding drives overall volatility, which can differ from simple weights. The three 25% core funds together contribute about 72% of total risk, closely matching their combined allocation. The mid- and small-cap value ETFs each weigh 10% but contribute a bit more than their share of risk, with risk/weight ratios above 1. That reflects their greater volatility compared with the large-cap core funds. Real estate is too small to dominate overall risk. This pattern shows risk is mostly proportional and not overly concentrated in a single position, even though some smaller slices punch slightly above their weight.
Correlation measures how closely assets move together – a correlation near 1 means they often rise and fall in sync. Here, the mid-cap and small-cap US value ETFs are flagged as moving almost identically. That suggests that, while they are distinct funds, they behave very similarly in practice and may offer limited diversification from each other during market stress. This is common for funds that target similar segments of the same market. The rest of the portfolio still spans very different regions and styles, so overall diversification is helped more by the global core than by the differences between these two value-focused slices.
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 vs. return chart shows the current portfolio with a Sharpe ratio of 0.49, below both the optimal mix (0.84) and the minimum variance portfolio (0.63). The Sharpe ratio compares excess return to volatility, like judging how much “extra” return you’re getting per unit of bumpiness. Being 2.53 percentage points below the efficient frontier at the same risk level means that, using only these existing holdings, alternative weightings could historically have produced better risk-adjusted outcomes. Importantly, this is a statistical, backward-looking view; it doesn’t say which mix will be best going forward, only that the present allocation hasn’t been the most efficient combination of what’s already in the toolbox.
The portfolio’s overall dividend yield is 2.06%, combining contributions from developed and emerging markets, US large caps, value-tilted small and mid caps, and real estate. Individual yields range from about 1.1% on the S&P 500 ETF up to 3.6% on the real estate ETF. Dividends are cash payments from companies’ profits, and over long periods they can make up a meaningful share of total returns, especially when reinvested. A yield around 2% is consistent with a globally diversified equity portfolio with a modest value tilt, providing a steady income component without turning the portfolio into an income-only strategy.
Total ongoing fund costs (TER) are very low at 0.08%, with individual ETFs ranging from 0.03% to 0.15%. The TER, or Total Expense Ratio, is the annual fee charged by a fund to cover management and operations, taken directly out of returns. For context, fees below about 0.10% are generally considered extremely competitive for broad index exposures. Keeping costs this low is a clear positive: even small fee differences compound significantly over decades. Here, expenses are unlikely to be a major drag on performance, which means more of the portfolio’s gross market return has historically been kept by the investor.
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