The risk profile, derived from past market volatility, reflects the level of risk the portfolio is exposed to. This assessment helps align your investments with your financial goals and comfort with market fluctuations.
The diversification assessment evaluates the spread of investments across asset classes, regions, and sectors. This ensures a balanced mix, reducing risk and maximizing returns by not concentrating in any single area.
This portfolio is very focused: three equity ETFs with 70% in a China technology fund, 18% in US growth, and 12% in a broad US market ETF. So, it’s 100% in stocks with a heavy tilt toward tech and growth themes, and a single holding dominates. That kind of structure can create powerful upside in favorable markets but also leads to big swings when sentiment turns. A more balanced mix usually spreads risk across different economic drivers, while this setup leans into a specific story. Anyone using something like this should treat it as a high-risk growth sleeve, not a complete “all‑weather” portfolio on its own.
Historically, $1,000 invested grew to $2,286, which is solid in absolute terms but trails both the US market and global market. The portfolio’s compound annual growth rate (CAGR) of 8.66% lags the US market’s 14.42% and global’s 11.91%. Max drawdown reached about -56%, far deeper than the roughly -34% drawdowns of the benchmarks, and that decline still hasn’t fully recovered. CAGR is like your long‑run average speed; drawdown is how bad the worst crash felt on the way. The lesson: this style has delivered lower returns with higher pain compared with broad markets over this period.
The Monte Carlo projection uses past returns and volatility to simulate many possible 15‑year paths, like running thousands of “what if” scenarios. The median outcome grows $1,000 to around $2,693, with a wide likely range from about $1,798 to $4,047 and extreme outcomes spanning roughly $951 to $7,499. Overall, 73.2% of simulations end positive, with an average annualized return near 7.92%. These numbers show decent long‑term potential but with a lot of uncertainty. It’s crucial to remember simulations rely on historical patterns that may not repeat, especially for regions or sectors sensitive to regulation and policy changes.
Everything here is in stocks, with no bonds, cash, or alternative assets. That pure‑equity structure maximizes growth potential but also leaves the portfolio fully exposed to equity market downturns, without a built‑in stabilizer like high‑quality bonds or cash. For long horizons, high equity exposure can make sense, yet the trade‑off is deeper and more frequent drawdowns. This is especially true when equities are concentrated in more volatile areas. A setup like this generally suits investors who can ride through big drops without needing to sell, and who might hold other safer assets outside this portfolio for balance.
Sector exposure is dominated by technology at 47%, with another 25% in telecommunications and 16% in consumer discretionary. Other sectors like financials, industrials, health care, and staples are each only tiny slices. Compared with broad equity benchmarks, this is a pronounced tilt toward tech‑driven and consumer‑oriented businesses, and a big underweight to more defensive sectors that often hold up better in downturns. Tech‑heavy portfolios tend to be very sensitive to interest rates, regulation, and innovation cycles. This sector mix can deliver strong growth in boom periods but often comes with sharp reversals when sentiment around innovation or policy turns.
Geographically, about 70% of exposure is in emerging Asia and 30% in North America. That’s almost the opposite of global benchmarks, which are usually dominated by developed markets like the US. This creates a big tilt toward one region’s economic, regulatory, and political environment, especially its technology sector. Such a stance can pay off if that region outperforms, but it also increases vulnerability to local policy shifts, capital controls, or growth slowdowns. Currency risk is another layer: returns in dollars depend not just on local stock performance but also on exchange‑rate moves. This is a bold regional allocation, not a global blend.
Some holdings may not have full classification data available. Percentages may not add up to 100%.
Most exposure is in larger companies, with 41% in mega‑caps and 42% in large‑caps, plus about 10% in mid‑caps. That means the portfolio leans on established, often dominant firms rather than smaller, more speculative names. Large and mega‑caps can provide some stability within equity markets because of diversified businesses and stronger balance sheets, even if the overall portfolio is still quite volatile due to sector and regional bets. The smaller mid‑cap slice may add some extra growth potential and risk. Overall, this size mix is reasonably aligned with global norms, which is a helpful counterbalance to the concentrated geographic and sector positions.
Looking through the ETFs, exposure is heavily concentrated in a handful of Chinese tech names like Tencent, Meituan, PDD, and Baidu, plus big US names such as NVIDIA, Microsoft, and Apple. Several of these appear via multiple ETFs, which quietly increases concentration even if their weights look modest individually. Because only top‑10 holdings are captured, overlap is likely understated, meaning real concentration could be higher. Hidden overlap matters because if a single company or theme struggles, it can drag multiple holdings at once. Understanding this helps avoid the illusion of diversification when several funds are effectively betting on the same set of giants.
Factor exposure shows very low tilts to value, momentum, and quality, with yield and low volatility roughly neutral, and size only slightly below market. Factors are like the underlying “traits” that drive returns; value targets cheaper stocks, momentum favors recent winners, and quality focuses on robust earnings and balance sheets. A very low value tilt means this portfolio leans away from cheaper names and more toward higher‑priced growth companies. Very low momentum suggests it is not strongly aligned with recent market leaders. Very low quality exposure implies more sensitivity to business‑specific setbacks. Together, this points to a growth‑heavy, more fragile profile in market stress.
Risk contribution shows how much each holding drives overall ups and downs, which can differ a lot from its weight. Here, the China technology ETF is 70% of the portfolio but contributes about 86% of total risk, meaning it dominates performance. By contrast, the US growth ETF and broad US ETF have risk contributions well below their weights. Think of it like one loud instrument in a band drowning out the others. If Chinese tech has a bad phase, the entire portfolio will likely follow, regardless of how the US funds behave. Adjusting position sizes is the main way to reshape that risk profile.
The two US ETFs — the S&P 500 ETF and the S&P 500 Growth ETF — move almost identically, which is expected because they both track closely related US large‑cap universes. High correlation means they rise and fall together, so holding both doesn’t add much true diversification compared with just one core US exposure. That’s not necessarily a problem, but it does mean the perceived variety isn’t translating into very different behavior. In contrast, the China tech ETF will behave differently from US broad market funds, which is where most of the diversification across regions actually comes from in this mix.
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.
On the risk‑return chart, the current portfolio has a Sharpe ratio of 0.28, with expected return of 11.31% and volatility of 26.59%. The Sharpe ratio measures return per unit of risk; higher is better. Both the optimal portfolio (Sharpe 0.79) and minimum variance portfolio (Sharpe 0.78) on the efficient frontier offer much better risk‑adjusted returns using the same building blocks but different weights. Being about 6 percentage points below the frontier at this risk level means the mix is not using its ingredients efficiently. Reweighting among these three ETFs alone could boost expected return or reduce risk without adding new holdings.
The combined dividend yield is around 1.83%, with the China technology ETF yielding about 2.30%, the broad US market ETF about 1.10%, and the US growth ETF at a lower 0.50%. Dividends are the cash payouts companies distribute, and they can be an important part of long‑term returns, especially when reinvested. For a growth‑oriented, tech‑heavy portfolio, this level of income is modest but not negligible. It suggests that most of the return expectation is from price appreciation rather than steady cash flow. For someone seeking regular income, this would likely be a secondary or satellite holding rather than a primary income source.
The weighted total expense ratio (TER) is about 0.48%, driven mainly by the 0.65% fee on the China technology ETF, while the Vanguard funds are very cheap at 0.03% and 0.10%. TER is the annual fee charged by funds, quietly coming out of returns each year. On the positive side, the US exposure is extremely cost‑efficient and aligns with best‑in‑class index fund pricing, which is great for long‑term compounding. The higher fee on the specialized China tech ETF is typical for niche strategies but still meaningful over decades. Overall, costs are reasonable but could be lower if more assets were in ultra‑low‑cost broad funds.
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