Concentrated growth portfolio with aggressive risk profile and strong tilt to value and momentum factors
Report created on May 11, 2026Risk profile
7/7
Diversification profile
3/5
Positions
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QQ QQCMF
QQCMF 38.97% -
VF Vanguard U.S. Value Factor
VFVA - US9219358051 33.49% -
VD Vanguard FTSE Canadian High Dividend Yield Index ETF
VDYIF 17.63% -
FB Fidelity Wise Origin Bitcoin Trust
FBTC - US3159481098 9.91%
This portfolio is built around a single dominant stock position, QQCMF, at roughly 39% of assets, paired with two broad equity ETFs and a near-10% crypto sleeve. That makes it heavily equity-focused with a clear speculative tilt, reflected in the 7/7 risk score. The ETFs add some diversification across companies and styles, while the bitcoin trust introduces a separate, highly volatile return stream. However, the risk contribution numbers show that, despite several holdings, the portfolio effectively behaves as if it were mostly one stock. With only about 1.4 years of history, it is too early to treat this structure as a proven long-term pattern, but the current mix clearly targets return over stability.
Growth
Over the 1.4-year window, a hypothetical $1,000 in this portfolio grew to about $1,685, implying a compound annual growth rate (CAGR) of 48.59%. CAGR is like the average speed on a road trip, showing how fast money grew per year if the path were smooth. This return far exceeded both US and global market benchmarks over the same period, but came with a very deep maximum drawdown of -43.41%, more than double the benchmark drops. That drawdown has not yet been fully recovered, highlighting how sharp the swings can be. Because this period is short and specific, these strong results can’t be taken as a reliable guide to long-term behavior.
Projection
The Monte Carlo projection uses the limited historical data to simulate many possible 15‑year paths, giving a range of potential outcomes for a $1,000 investment. It shows a median end value around $2,876, with a wide “likely” band from roughly $1,781 to $4,389. Monte Carlo works by mixing and remixing past returns and volatility to see how they might play out in different sequences, a bit like shuffling a deck of historical months. The average simulated annual return of 8.47% reflects both strong upside and meaningful downside scenarios. With just 1.4 years of history feeding these simulations, the projections are more fragile than usual and should be seen as rough illustrations, not forecasts.
Asset classes
- Stocks 72%
- No data 18%
- Crypto 10%
By asset class, about 72% of the portfolio is in stocks, 10% in crypto, and 18% classified as “no data,” where the system cannot identify type. This puts the visible portion firmly in the high-risk, growth-oriented camp, since equities and crypto tend to move more than bonds or cash. The crypto slice adds an extra layer of volatility and potential diversification, although it can also amplify drawdowns. Compared with broad market blends that often mix in bonds, this portfolio is structurally more aggressive. The “no data” bucket is simply an information gap, not necessarily a risk problem, but it does mean any analysis of true diversification is incomplete.
Sectors
- No data 39%
- Crypto 10%
- Financials 9%
- Health Care 5%
- Technology 5%
- Consumer Discretionary 4%
- Industrials 3%
- Energy 2%
- Consumer Staples 2%
- Telecommunications 2%
- Basic Materials 1%
This breakdown covers the equity portion of your portfolio only.
Sector data shows exposure spread across financials, health care, technology, consumer areas, industrials, energy, telecom, and materials, but almost 39% of the portfolio sits in the “no data” category and 10% is crypto. That missing chunk likely reflects holdings where sector classification isn’t available, limiting visibility into where business risks really sit. The observed mix suggests some balance rather than an extreme tilt into a single traditional sector. Sector balance matters because different parts of the economy respond differently to interest rates, inflation, and growth cycles. Here, the combination of broad ETF exposure and an unclassified core makes the sector story partially hidden, so apparent diversification may be stronger or weaker than the numbers show.
Regions
- North America 72%
This breakdown covers the equity portion of your portfolio only.
Geographically, the portfolio is reported as 72% North America, with no further breakdown for the rest. This means most identifiable equity risk is tied to North American economic and policy conditions. That can be beneficial when that region performs strongly, and it also aligns with many global benchmarks that are heavily weighted to North American markets. At the same time, it reduces the potential benefits of owning more varied regions that may perform differently at different times. Because part of the portfolio sits in “no data” buckets and crypto is separated out, this geographic view is incomplete, so the true global spread could be broader or more concentrated than stated.
Market capitalization
- No data 39%
- Micro-cap 10%
- Small-cap 8%
- Large-cap 8%
- Mid-cap 7%
This breakdown covers the equity portion of your portfolio only.
Market capitalization data shows exposure across micro‑cap, small‑cap, mid‑cap, and large‑cap stocks, plus a sizable 39% “no data” portion. Smaller companies (micro and small caps) can offer higher growth potential but often come with greater price swings and more idiosyncratic risk, while large caps tend to be more established and somewhat steadier. The presence of all size buckets suggests some natural diversification across company types. However, the large “no data” slice again limits clarity on where the core risk really lives. Given the overall speculative risk score and the concentrated single-stock position, the portfolio’s actual behavior may resemble a more volatile, smaller‑cap tilt than these partial numbers alone suggest.
True holdings
-
-
EO EOG Resources Inc 0.35%
Part of fund(s):- Vanguard U.S. Value Factor
-
T AT&T Inc. 0.33%
Part of fund(s):- Vanguard U.S. Value Factor
-
BM Bristol-Myers Squibb Company 0.33%
Part of fund(s):- Vanguard U.S. Value Factor
-
VZ Verizon Communications Inc 0.32%
Part of fund(s):- Vanguard U.S. Value Factor
-
CM Comcast Corp 0.32%
Part of fund(s):- Vanguard U.S. Value Factor
-
CI Cigna Corp 0.30%
Part of fund(s):- Vanguard U.S. Value Factor
-
CR Salesforce.com Inc 0.28%
Part of fund(s):- Vanguard U.S. Value Factor
-
GM General Motors Company 0.28%
Part of fund(s):- Vanguard U.S. Value Factor
-
CV CVS Health Corp 0.27%
Part of fund(s):- Vanguard U.S. Value Factor
- Top 10 total 41.75%
This breakdown covers the equity portion of your portfolio only.
Looking through the ETFs, only about 42% of the portfolio is covered by identified underlying holdings, and ETF top‑10 coverage is just 5%. The single stock QQCMF stands out as a 39% direct position with no further look‑through, meaning its specific business and risk drivers dominate overall behavior. The identified ETF holdings show various large, familiar companies, each making up only a fraction of a percent of the portfolio, indicating that the ETFs themselves are quite diversified. Because overlap analysis uses only ETF top‑10 holdings, any repeated names across funds are understated. In practice, hidden concentration is much more about QQCMF’s large weight than overlapping ETF constituents.
Factors
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 very high tilt to value (85%) and a high tilt to momentum (78%), alongside a very low exposure to quality (5%). Factors are like investing “ingredients” such as cheapness (value), recent strong performance (momentum), or financial strength (quality) that research links to long‑term behavior. A strong value and momentum tilt can benefit when cheap, recently winning stocks keep doing well, but it may hurt during sharp style reversals. Very low quality exposure suggests a bias away from companies with historically stable earnings or stronger balance sheets, which can make drawdowns more intense. With only 1.4 years of data, these factor tilts describe the current setup rather than a proven long‑run pattern.
Risk contribution
-
QQCMF
Weight: 38.97% 97.6% -
Vanguard U.S. Value Factor
Weight: 33.49% 1.5% -
Fidelity Wise Origin Bitcoin Trust
Weight: 9.91% 0.9% -
Vanguard FTSE Canadian High Dividend Yield Index ETF
Weight: 17.63% 0.1%
Risk contribution numbers are striking: QQCMF, at about 39% weight, drives roughly 98% of the portfolio’s total volatility. Risk contribution measures how much each holding adds to overall ups and downs, which can differ a lot from its weight. Here, the ETFs and bitcoin trust contribute only tiny slivers of risk despite meaningful allocations. A risk/weight ratio of 2.50 for QQCMF means it is far riskier than a “neutral” position of the same size, effectively turning this into a concentrated single‑stock bet. This is consistent with the very high drawdown experienced. Even if the rest of the holdings are diversified and relatively steady, the portfolio will mostly move as QQCMF moves.
Risk vs. return
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 efficient frontier analysis compares the current mix with alternative weightings of the same holdings. The Sharpe ratio, which measures return per unit of risk above the risk‑free rate, is 1.24 for the current portfolio versus 2.23 for the optimal mix and 1.88 for the minimum variance version. Being about 9 percentage points below the efficient frontier at the current risk level means the portfolio is taking on more volatility than necessary for the return it achieved in this short period. In plain terms, different weights among these same holdings could historically have delivered better risk‑adjusted outcomes. Because the input data cover only 1.4 years, these optimization insights are very tentative, but they do highlight how strong concentration drives efficiency.
Dividends
- Vanguard U.S. Value Factor 2.00%
- Weighted yield (per year) 0.67%
Dividends appear as a modest feature here. The Vanguard U.S. Value Factor ETF yields around 2.00%, while the overall portfolio yield is only 0.67%, diluted by the non‑dividend‑paying assets like crypto and the concentrated stock position. Dividends matter because they can provide a steady stream of cash returns that can be reinvested or used as income, especially during flat markets. In this case, the portfolio’s return profile is driven much more by price changes than by income. Over long periods, reinvested dividends can contribute significantly to total returns, but given the short history and growth‑oriented structure, dividends currently play a supporting rather than central role.
Ongoing product costs
- Fidelity Wise Origin Bitcoin Trust 0.25%
- Vanguard U.S. Value Factor 0.13%
- Weighted costs total (per year) 0.07%
On the cost side, the disclosed total expense ratio (TER) for the portfolio is a low 0.07%, with the main ETFs at 0.13% and the bitcoin trust at 0.25%. TER is the annual fee charged by funds, expressed as a percentage of assets, and it quietly reduces returns year after year. Low ongoing costs are generally helpful because they leave more of any gross return in the investor’s pocket, especially when compounded over long periods. Here, the cost structure is impressively lean and aligns well with best practices for efficiency. Given that risk and performance are dominated by a single stock position, fees are not a major drag in this setup; the bigger drivers are concentration and volatility.
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