This portfolio is built around one broad global equity fund at 60%, plus a 25% allocation to a Nasdaq-100 tracker, with the remaining 15% split between short-term euro government bonds and a euro cash ETF. So most of the portfolio is in growth-oriented stocks, with a smaller slice providing stability and liquidity. Because the dataset only covers about 1.7 years, any conclusions about long-term behaviour are tentative. Still, the structure suggests a clear pattern: global stock market exposure, amplified by a tech-heavy growth sleeve, and cushioned slightly by lower-volatility bond and cash positions rather than being a pure equity bet.
Over the 1.7-year window, €1,000 hypothetically grew to about €1,232, implying a compound annual growth rate (CAGR) of 12.88%. CAGR is like average speed on a road trip, smoothing out bumps along the way. The portfolio’s max drawdown was -19.39%, with a drop and recovery that together took around eight months. Over this short period, returns sat between the US market (lower return, deeper drawdown) and the global market (slightly higher return). With only 1.7 years of data, these numbers show how it behaved in this specific stretch, not a reliable template for decades.
The Monte Carlo projection uses the short performance history to simulate many possible 15-year paths, shaking returns and volatility randomly to see a range of outcomes. Here, the median result turns €1,000 into about €2,642, with most scenarios falling between roughly €1,857 and €3,877. A smaller slice of simulations show much lower or much higher outcomes. Monte Carlo is essentially an educated “what if” machine, not a prediction. Because it’s based on only 1.7 years of data, the inputs may not capture full market cycles, so the projected 7.56% annualised return should be read as a rough illustration, not a firm expectation.
The portfolio holds 85% in stocks and 15% in bonds and cash-like holdings. That equity-heavy mix naturally points to growth potential but with noticeable ups and downs. Bonds here are short-term government issues in euros, which tend to swing less than stocks and can help dampen volatility, while the cash ETF behaves very defensively. Compared with many “balanced” blends that often lean closer to a 60/40 split, this setup is more growth-focused. Given the limited 1.7-year history, we only see how this mix handled one recent environment, but structurally it’s tilted toward equity-driven outcomes rather than fixed-income stability.
This breakdown covers the equity portion of your portfolio only.
Sector-wise, about a third of the equity exposure is in technology, with the rest spread across financials, telecoms, consumer areas, industrials, health care, and smaller slices elsewhere. That tech share is higher than in many broad global indices, mainly because of the dedicated Nasdaq-100 allocation layered on top of the global fund. Sector weights matter because different areas of the economy react differently to interest rates, growth expectations, and regulation. A tech-tilted portfolio can benefit when innovative growth companies are in favour but may feel sharper swings during periods of rising rates or when sentiment turns against high-growth names.
This breakdown covers the equity portion of your portfolio only.
Geographically, the portfolio leans strongly toward North America at 63%, with smaller allocations to developed Europe, Japan, developed Asia, and modest exposure to emerging regions. This is broadly in line with many global equity indices, which are also dominated by North American companies, so the geographic mix is reasonably aligned with global market weights. That alignment is often helpful for diversification because it naturally spreads exposure across multiple economies and currencies. The presence of European bonds and cash adds a euro anchor, but the equity side is still mainly driven by North American markets. Again, this pattern is observed over only 1.7 years.
This breakdown covers the equity portion of your portfolio only.
By market capitalization, the portfolio is anchored in larger companies: 42% in mega-caps and 30% in large-caps, with a meaningful 13% slice in mid-caps. Larger firms often have more diversified businesses and more stable earnings, which can translate into slightly smoother share price moves than very small companies. At the same time, the mid-cap element keeps some exposure to potentially faster-growing businesses. Relative to a purely large-cap index, this is still firmly big-company focused. The short performance window limits insight into how these size tilts behave across full economic cycles, but the structure is consistent with a global, large-cap core strategy.
This breakdown covers the equity portion of your portfolio only.
Looking through the ETFs’ top-10 holdings, the largest underlying positions include NVIDIA, Apple, Microsoft, Amazon, Alphabet, Broadcom, Micron, TSMC, and AMD. These are mostly large technology or tech-related names, and several appear across both the global and Nasdaq-100 funds. This overlap creates “hidden” concentration: even without directly buying these stocks, together they make up meaningful portions of the portfolio. Because only top-10 ETF holdings are used, actual overlap is likely higher than shown. This concentration helps explain the strong tech tilt and means portfolio behaviour may track big moves in these headline companies, especially during sharp rallies or selloffs.
Risk contribution shows how much each holding adds to overall ups and downs, which can differ a lot from its weight. Here, the 60% global equity fund contributes about 64% of total risk, while the 25% Nasdaq-100 fund contributes around 36%—much more than its weight would suggest. Meanwhile, the 8% in short-term government bonds and 7% in cash collectively add almost no volatility. This means that, in practice, nearly all portfolio risk comes from the two equity funds, especially the growth-heavy Nasdaq-100 position. The bond and cash sleeves act more like stabilisers than major drivers of performance.
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 places the current portfolio very close to the efficient frontier, which is the curve showing the best possible expected return for each level of risk given these holdings. The current Sharpe ratio, a measure of return per unit of volatility, is 0.67, while the maximum-Sharpe mix using the same funds is 0.89 at slightly higher risk. Being near the frontier suggests the existing weights are already using the chosen building blocks efficiently. The extremely high Sharpe shown for the minimum-variance point reflects ultra-low volatility rather than high returns and is based on this short 1.7-year sample, so it shouldn’t be treated as a long-term pattern.
The weighted ongoing cost (TER) of the portfolio is around 0.17% per year, which is low by most standards for a mix of global equities, growth-oriented indices, and bond/cash ETFs. TER, or Total Expense Ratio, is like a small annual service charge embedded in each fund’s price; lower fees leave more of any future returns in the investor’s hands. Over many years, even modest fee differences can compound into noticeable amounts. In this case, the cost level is a clear strength of the portfolio’s design. As always, the 1.7-year return history is too short to judge how much low costs might matter over full cycles, but structurally they are favourable.
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