High growth focused portfolio with strong US technology tilt and concentrated risk in a few names
Report created on Apr 28, 2026Risk profile
5/7
Diversification profile
2/5
Positions
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SC Schwab U.S. Large-Cap Growth ETF
SCHG - US8085243009 30.91% -
AI Global X Artificial Intelligence & Technology ETF
AIQ - US37954Y6326 20.75% -
SC Schwab U.S. Large-Cap ETF
SCHX - US8085242019 19.81% -
SP Invesco S&P 500® Momentum ETF
SPMO - US46138E3392 18.93% -
PO POET Technologies Inc
POET - CA73044W3021 4.91% -
SO SoFi Technologies Inc.
SOFI - US83406F1021 4.69%
This portfolio is fully invested in equities, mostly through four US-listed ETFs and two individual stocks. Around 90% sits in diversified funds tilted toward large-cap growth and momentum, while roughly 10% is in two single, higher-risk companies. This structure leans heavily into growth-oriented themes rather than broad, balanced exposure. A fully equity, growth-tilted portfolio tends to focus on long-term capital appreciation rather than steady income or capital preservation. The combination of ETFs and individual names means some risks are diversified across many companies, while others are very concentrated in just one stock. Overall, the setup reflects a clear preference for higher potential returns with meaningfully higher volatility along the way.
Growth
From late 2020 to April 2026, $1,000 in this portfolio grew to about $2,237, a compound annual growth rate (CAGR) of 16.15%. CAGR is like average speed on a road trip: it smooths out all the ups and downs into one yearly number. Over the same period, the US market returned 14.91% and the global market 12.46%, so this portfolio outpaced both. The trade-off is a deeper max drawdown of -37.24%, compared with around -25% for the benchmarks. A drawdown measures how far an investment falls from a previous peak, showing the “worst-case” drop. The long recovery time from 2022 to early 2024 underlines that strong growth came with sharp, prolonged setbacks.
Projection
The Monte Carlo projection uses 1,000 simulations based on historical volatility and returns to explore many possible 15-year futures. Think of it as re-running history with slight variations to see a range of plausible outcomes rather than a single forecast. The median result grows $1,000 to about $2,777, implying an annualized 8.15% across all paths. But the likely middle range is wide: roughly $1,758 to $4,247, and extreme cases span from around $918 to $7,832. This shows how uncertain long-term equity outcomes can be. Importantly, these simulations rely on past patterns continuing; they cannot predict regime shifts, policy changes, or structural breaks in markets.
Asset classes
- Stocks 100%
All of this portfolio is in stocks, with no allocation to bonds, cash-like assets, or alternatives. That all-equity stance maximizes exposure to market growth but also leaves the portfolio fully exposed to equity market downturns without built-in cushioning from steadier asset classes. Compared with broad market portfolios that usually include some defensive assets, this structure naturally scores higher on risk and potential volatility. A 100% stock mix can experience larger and more frequent swings in value, especially around economic slowdowns or market shocks. The benefit is straightforward participation in equity upside, while the cost is living through deeper drawdowns and longer recovery periods when markets turn.
Sectors
- Technology 50%
- Telecommunications 12%
- Financials 11%
- Consumer Discretionary 8%
- Industrials 7%
- Health Care 6%
- Consumer Staples 2%
- Energy 2%
- Basic Materials 1%
- Utilities 1%
- Real Estate 1%
Sector-wise, technology dominates at about 50%, with the rest spread across telecommunications, financials, consumer, industrials, health care, and smaller allocations elsewhere. This is a clear tilt compared with broad benchmarks, where tech is large but not usually half the portfolio. Heavy tech exposure often means higher sensitivity to innovation cycles, interest rate expectations, and shifts in investor appetite for growth stories. For instance, when rates rise or investors rotate into more defensive areas, tech-heavy portfolios can see sharper pullbacks. On the positive side, this alignment with innovation-oriented sectors has historically supported strong performance during periods when growth and digital transformation themes lead the market.
Regions
- North America 94%
- Asia Developed 3%
- Europe Developed 2%
- Asia Emerging 1%
Geographically, about 94% of the portfolio is in North America, with only small slices in developed Asia, developed Europe, and emerging Asia. That means the portfolio is effectively tied to one main region’s economy, politics, and currency. In contrast, global benchmarks spread more evenly across North America, Europe, and Asia. A strong home-region tilt can work well when that region leads global markets, as US equities have often done in recent years. However, it also means that local shocks—such as policy changes, sector-specific issues, or economic slowdowns—can have an outsized effect. The modest non-US exposure provides only limited diversification against US-specific risks.
Market capitalization
- Mega-cap 46%
- Large-cap 36%
- Mid-cap 11%
- Small-cap 6%
By market capitalization, this portfolio is heavily skewed to larger companies: roughly 46% in mega-caps and 36% in large-caps, with only about 17% combined in mid- and small-caps. Market cap is simply the value of a company’s equity, and bigger firms tend to be more established and widely followed. This pattern is broadly similar to many mainstream equity indices, which are also dominated by large companies. The benefit is exposure to firms with deep resources and global footprints, which can be more resilient than very small businesses. The trade-off is less participation in the higher but more volatile return potential that smaller companies can sometimes offer during certain parts of the cycle.
True holdings
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NV NVIDIA Corporation 7.59%
Part of fund(s):- Global X Artificial Intelligence & Technology ETF
- Invesco S&P 500® Momentum ETF
- Schwab U.S. Large-Cap ETF
- Schwab U.S. Large-Cap Growth ETF
-
AA Apple Inc 4.93%
Part of fund(s):- Global X Artificial Intelligence & Technology ETF
- Schwab U.S. Large-Cap ETF
- Schwab U.S. Large-Cap Growth ETF
-
-
-
AV Broadcom Inc 4.42%
Part of fund(s):- Global X Artificial Intelligence & Technology ETF
- Invesco S&P 500® Momentum ETF
- Schwab U.S. Large-Cap ETF
- Schwab U.S. Large-Cap Growth ETF
-
MS Microsoft Corporation 3.26%
Part of fund(s):- Schwab U.S. Large-Cap ETF
- Schwab U.S. Large-Cap Growth ETF
-
GO Alphabet Inc Class A 3.05%
Part of fund(s):- Invesco S&P 500® Momentum ETF
- Schwab U.S. Large-Cap ETF
- Schwab U.S. Large-Cap Growth ETF
-
AM Amazon.com Inc 2.65%
Part of fund(s):- Schwab U.S. Large-Cap ETF
- Schwab U.S. Large-Cap Growth ETF
-
GO Alphabet Inc Class C 2.44%
Part of fund(s):- Invesco S&P 500® Momentum ETF
- Schwab U.S. Large-Cap ETF
- Schwab U.S. Large-Cap Growth ETF
-
MU Micron Technology Inc 1.86%
Part of fund(s):- Global X Artificial Intelligence & Technology ETF
- Invesco S&P 500® Momentum ETF
- Top 10 total 39.79%
Looking through the ETFs to their top holdings, a lot of exposure clusters in a familiar group of large US tech and tech-adjacent names such as NVIDIA, Apple, Microsoft, Alphabet, Amazon, and Broadcom. NVIDIA alone adds up to about 7.6% of the portfolio, and several of these companies appear in multiple funds. Overlap across ETFs creates hidden concentration: even though the portfolio holds many funds and stocks, some underlying companies drive a significant share of results. Note that this overlap is probably understated because only ETF top-10 holdings are captured here. The presence of two single stocks—POET Technologies and SoFi Technologies—adds further company-specific risk on top of this large-cap concentration.
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 is mostly around neutral levels, meaning the portfolio behaves broadly like the overall market on many academic “style” dimensions. Factors are characteristics—like value, size, or momentum—that research links to differences in long-term returns and risk. Here, value and yield both show mildly low exposure, which is typical for a growth-oriented mix: it favors companies priced for future growth over those that look cheap or pay high dividends. Momentum, quality, size, and low volatility sit near the middle, so there is no strong tilt toward smaller firms, defensive names, or high-momentum leaders. Overall, this is a classic growth-leaning profile without extreme style bets in other directions.
Risk contribution
-
Schwab U.S. Large-Cap Growth ETF
Weight: 30.91% 29.6% -
Global X Artificial Intelligence & Technology ETF
Weight: 20.75% 22.0% -
Invesco S&P 500® Momentum ETF
Weight: 18.93% 14.4% -
Schwab U.S. Large-Cap ETF
Weight: 19.81% 14.4% -
POET Technologies Inc
Weight: 4.91% 9.8% - Top 5 risk contribution 90.3%
Risk contribution shows how much each holding adds to overall ups and downs, which can differ from its weight. POET Technologies is the standout: at only 4.9% weight, it contributes roughly 9.8% of total portfolio risk, with a risk/weight ratio of about 2. That’s like a small instrument playing very loudly in an orchestra. The AI & Technology ETF also contributes slightly more risk than its weight, while the Schwab Large-Cap and Momentum ETFs contribute less risk relative to their sizes. The top three holdings by weight drive about two-thirds of total risk, underlining how concentrated the portfolio’s behavior is in a handful of positions, even though it holds multiple funds and stocks.
Redundant positions
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High correlationSchwab U.S. Large-Cap ETFSchwab U.S. Large-Cap Growth ETF
Correlation measures how closely different investments move together. When two assets are highly correlated, they tend to go up and down at the same time, limiting diversification benefits. In this portfolio, the Schwab U.S. Large-Cap Growth ETF and the Schwab U.S. Large-Cap ETF move almost identically. That makes sense, as both draw from a similar large-cap US universe, just with a growth tilt in one. Holding closely correlated funds doesn’t add much diversification, even if they look different on the surface. Instead, it can reinforce the same underlying market exposure, making the portfolio more dependent on the fortunes of large US companies as a group.
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 risk vs. return chart compares the current mix to an “efficient frontier,” which represents the best achievable return for each risk level using just these holdings. The current portfolio has a Sharpe ratio of 0.74, while the optimal combination of the same positions reaches 1.09 with only slightly higher volatility. The Sharpe ratio is a simple measure of return per unit of risk, using a risk-free rate as a baseline. Being about 4.6 percentage points below the frontier at the current risk level suggests there is room to improve risk/return trade-offs purely by changing weights, without adding or removing holdings. The minimum-variance mix shows that substantially lower risk is also possible with this same toolkit.
Dividends
- Global X Artificial Intelligence & Technology ETF 0.20%
- Schwab U.S. Large-Cap Growth ETF 0.40%
- Schwab U.S. Large-Cap ETF 1.10%
- Invesco S&P 500® Momentum ETF 0.80%
- Weighted yield (per year) 0.53%
The portfolio’s overall dividend yield is about 0.53%, which is quite low compared with many broad equity indices. Dividend yield is the annual cash payout as a percentage of the current value, and it can provide a steady return component alongside price changes. Here, the growth and tech-heavy tilt naturally leads to lower income, since many such companies reinvest profits instead of paying them out. The Schwab U.S. Large-Cap ETF has the highest yield at 1.10%, but it’s still modest. This structure means most historical and projected returns come from price appreciation, not cash distributions, which fits with the portfolio’s growth orientation rather than an income-focused approach.
Ongoing product costs
- Global X Artificial Intelligence & Technology ETF 0.68%
- Schwab U.S. Large-Cap Growth ETF 0.04%
- Schwab U.S. Large-Cap ETF 0.03%
- Invesco S&P 500® Momentum ETF 0.13%
- Weighted costs total (per year) 0.18%
The portfolio’s blended ongoing fee, or Total Expense Ratio (TER), is about 0.18%, which is impressively low for an actively tilted, theme-heavy mix. TER is the annual cost charged by funds as a percentage of assets, similar to a small yearly service fee. The bulk of the allocation is in very low-cost core ETFs from Schwab and a reasonably priced momentum ETF, with only the AI & Technology fund at a higher 0.68%. Over long periods, lower fees mean more of the portfolio’s gross return stays in the investor’s pocket, and this cost level compares favorably with many actively managed growth strategies and thematic products.
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