Highly concentrated high growth thematic equity portfolio with aggressive risk and very limited track record
Report created on May 14, 2026Risk profile
6/7
Diversification profile
3/5
Positions
-
PS Invesco Dynamic Semiconductors ETF
PSI - US46137V6478 15.00% -
AI VistaShares Artificial Intelligence Supercycle ETF
AIS 15.00% -
FM MarketDesk Focused U.S. Momentum ETF
FMTM 15.00% -
PO VistaShares Electrification Supercycle ETF
POW 15.00% -
SG SMART Earnings Growth 30 ETF
SGRT 15.00% -
US ProShares Ultra Semiconductors
USD - US74347R6696 10.00% -
KO KraneShares Global Humanoid and Embodied Intelligence Index ETF
KOID 7.50% -
MA Roundhill Space & Technology ETF
MARS 7.50%
This portfolio is built entirely from eight thematic equity ETFs, with no bonds or cash-alternative funds in the mix. Positions are tightly bunched, with five core ETFs each at 15%, one leveraged semiconductor ETF at 10%, and two smaller thematic funds at 7.5% each. Everything here is focused on narrow themes like semiconductors, artificial intelligence, electrification, momentum, and space-related technology. That kind of focus can create powerful return swings because many holdings are exposed to similar economic forces. With only about two months of history, it’s too early to say how this structure behaves across full market cycles, but the initial data and the “aggressive” risk score both point to a portfolio that can move sharply in both directions.
Growth
Over the short 2‑month window, the hypothetical $1,000 investment grew to about $1,347, far ahead of both US and global equity benchmarks. The reported compound annual growth rate (CAGR) of 430% simply reflects this brief, strong run; CAGR is like averaging the speed of a car over a tiny stretch of road, so it can look extreme over short periods. The max drawdown of about -10% shows the deepest dip from peak to trough so far, noticeably steeper than the benchmarks’ roughly -7% falls. Seven trading days made up 90% of returns, which underlines how spiky and event-driven performance has been. With only two months of data, none of these numbers should be treated as a stable long-term pattern.
Projection
The Monte Carlo projection takes the short available history and simulates 1,000 possible 15‑year paths for a $1,000 investment. It uses past volatility and returns as raw ingredients, then shakes them up randomly to show a range of potential futures rather than a single forecast. The median ending value around $2,640 implies an annualized 7.7% return in the simulations, with a very wide band from roughly $1,000 to over $7,000. That spread reflects how an aggressive, concentrated growth portfolio can lead to very different outcomes depending on market conditions. Because the underlying history is barely two months, these simulations are especially fragile and are better read as a volatility illustration than a reliable long-term roadmap.
Asset classes
- Stocks 92%
- No data 8%
- Other 1%
Asset class data shows about 92% in stocks, 1% in “Other,” and 8% with no classification data. The key takeaway is that this is essentially a pure equity portfolio, with no explicit stabilizers like bonds or cash funds in the mix. Equity-heavy allocations typically have higher return potential over long periods but also sharper drawdowns, especially when underlying themes are cyclical or speculative. The “no data” slice simply means classification is missing for that portion, not that it’s something exotic. Compared with broad benchmarks that mix in more defensive assets, this allocation leans firmly toward growth and risk. With only a couple of months observed, the full impact of this high equity weighting on drawdowns in tougher markets hasn’t really been tested yet.
Sectors
- Technology 58%
- Industrials 22%
- Utilities 3%
- Basic Materials 3%
- Consumer Discretionary 3%
- Energy 1%
- Consumer Staples 1%
- Real Estate 1%
- Financials 1%
Sector exposure is dominated by technology at 58%, with industrials a distant second at 22%, and everything else (utilities, materials, consumer, energy, real estate, financials) in very small single digits. That’s a textbook example of sector concentration: most of the economic story here is tied to tech-driven themes like semiconductors, AI, and advanced hardware. When this sector is in favor, concentrated portfolios can see outsized gains; when sentiment turns or regulation or rates hit growth names, the same concentration can magnify losses. The industrials weight likely reflects companies linked to electrification and advanced manufacturing rather than classic cyclical names. Over a short two‑month window, sector behavior can look flattering, but long-term cycles in a tech-heavy allocation are usually much bumpier than the broad market.
Regions
- North America 74%
- Asia Developed 8%
- Asia Emerging 4%
- Europe Developed 4%
- Japan 1%
- Africa/Middle East 1%
Geographically, the portfolio is heavily tilted toward North America at 74%, with smaller pockets in developed Asia (8%), emerging Asia (4%), developed Europe (4%), Japan (1%), and Africa/Middle East (1%). That means most of the risk is linked to a single region’s markets, regulation, and currency, even though the themes (AI, chips, space, electrification) are global in nature. Compared with a typical world equity benchmark, this is a pronounced North America overweight. That alignment has helped over the last decade when US growth and tech led, but it also concentrates exposure to one economic bloc. The non‑US slices do add some diversification, yet they are modest in size. Over just two months, it’s hard to see how these regional tilts behave across full global market cycles.
Market capitalization
- Mid-cap 27%
- Large-cap 26%
- Mega-cap 17%
- Small-cap 13%
- Micro-cap 1%
Some holdings may not have full classification data available. Percentages may not add up to 100%.
By market capitalization, the portfolio spreads across the spectrum: 27% mid-cap, 26% large-cap, 17% mega-cap, 13% small-cap, and a small 1% in micro-caps. This mix leans more toward mid- and smaller companies than many broad indexes, which are usually dominated by mega- and large-caps. Smaller and mid-sized firms often have more room to grow but can be more volatile and sensitive to economic surprises. That fits with the overall aggressive profile of the portfolio. The presence of mega-caps still adds some anchoring from well-established companies, especially in areas like semiconductors and AI, but it doesn’t dominate the risk picture. With only two months of returns, it’s too early to see a clear pattern of how each size bucket contributes to gains and drawdowns over time.
True holdings
-
MU Micron Technology Inc 4.12%
Part of fund(s):- Invesco Dynamic Semiconductors ETF
- ProShares Ultra Semiconductors
- SMART Earnings Growth 30 ETF
- VistaShares Artificial Intelligence Supercycle ETF
-
VR Vertiv Holdings Co 2.58%
Part of fund(s):- MarketDesk Focused U.S. Momentum ETF
- SMART Earnings Growth 30 ETF
- VistaShares Artificial Intelligence Supercycle ETF
-
LI Lumentum Holdings Inc 1.87%
Part of fund(s):- SMART Earnings Growth 30 ETF
-
AM Advanced Micro Devices Inc 1.82%
Part of fund(s):- Invesco Dynamic Semiconductors ETF
- ProShares Ultra Semiconductors
- VistaShares Artificial Intelligence Supercycle ETF
-
NV NVIDIA Corporation 1.79%
Part of fund(s):- Invesco Dynamic Semiconductors ETF
- ProShares Ultra Semiconductors
-
GL Corning Incorporated 1.63%
Part of fund(s):- SMART Earnings Growth 30 ETF
-
01 LS Electric 1.52%
Part of fund(s):- VistaShares Electrification Supercycle ETF
-
MX MaxLinear Inc 1.48%
Part of fund(s):- Invesco Dynamic Semiconductors ETF
-
00 SK Hynix Inc 1.48%
Part of fund(s):- VistaShares Artificial Intelligence Supercycle ETF
-
CI Ciena Corp 1.40%
Part of fund(s):- MarketDesk Focused U.S. Momentum ETF
- SMART Earnings Growth 30 ETF
- Top 10 total 19.71%
Looking through the ETFs’ top 10 holdings, about 48% of the portfolio is covered, so this is only a partial view. Within that slice, several names repeat: Micron, Vertiv, Lumentum, AMD, NVIDIA, Corning, LS Electric, SK Hynix, and others show meaningful combined weights. This overlap means some individual companies quietly drive more of the portfolio than any single ETF weight suggests. For example, Micron at around 4% and NVIDIA near 2% across funds create hidden concentration in specific chipmakers. Because only top-10 ETF holdings are used, actual overlap is likely higher. Over longer periods, such clustering can make returns more dependent on a relatively small set of underlying stocks, but the limited history here doesn’t yet reveal how that concentration behaves across full market cycles.
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 data shows a very strong tilt away from size (13%) and low volatility (10%), and a strong tilt toward momentum (75%). Factors are like “personality traits” of investments that academic research has linked to long-term patterns. A low size score means the portfolio leans toward smaller and mid-sized stocks, which often move more dramatically than giants. Very low low‑volatility exposure reinforces that this is built from more jumpy names. High momentum means many holdings have risen strongly recently, which can help in trending markets but often hurts when trends suddenly reverse. Value and yield are both on the lower side, consistent with growth-oriented themes. With only two months of data feeding these estimates, they should be seen as early signals, not precise long-run factor blueprints.
Risk contribution
-
VistaShares Artificial Intelligence Supercycle ETF
Weight: 15.00% 17.5% -
Invesco Dynamic Semiconductors ETF
Weight: 15.00% 16.9% -
ProShares Ultra Semiconductors
Weight: 10.00% 15.7% -
SMART Earnings Growth 30 ETF
Weight: 15.00% 14.5% -
VistaShares Electrification Supercycle ETF
Weight: 15.00% 12.6% - Top 5 risk contribution 77.3%
Risk contribution looks at how much each holding adds to overall portfolio ups and downs, which can differ from its weight. Here, the three biggest risk drivers are the AI Supercycle ETF (17.5% of risk), the semiconductor ETF (16.9%), and the leveraged semiconductor fund (15.7%). Together, these top three make up just 40% of the capital but about 50% of total risk. The leveraged semiconductor ETF is especially notable: at 10% weight it contributes over 15% of risk, showing how leverage amplifies swings. Other 15% positions like SMART Earnings Growth and Electrification add slightly less than their weights in risk terms. With such a concentrated thematic set-up, risk is naturally clustered, and this early data suggests a few holdings dominate the portfolio’s day-to-day movement.
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–return chart compares the current mix with an “efficient frontier” built from the same holdings. The current portfolio has a very high reported Sharpe ratio of 4.45, but the optimal mix achieves 5.42 with slightly higher return and modestly higher risk, while a minimum-variance mix lowers risk but also reduces return. The fact that the current portfolio sits about 31.8 percentage points below the frontier suggests that, based on this short history, there are theoretical weightings that would have delivered better risk-adjusted performance using only these ETFs. However, because the inputs come from just two months of unusual returns, the precise numbers are fragile. The main takeaway is educational: even with the same building blocks, how they’re weighted can meaningfully change the balance between risk and return.
Dividends
- ProShares Ultra Semiconductors 0.20%
- MarketDesk Focused U.S. Momentum ETF 0.20%
- KraneShares Global Humanoid and Embodied Intelligence Index ETF 0.70%
- VistaShares Electrification Supercycle ETF 0.10%
- SMART Earnings Growth 30 ETF 0.10%
- Weighted yield (per year) 0.13%
Dividend yields across the ETFs are very low, with most funds sitting around 0.1–0.2% and the overall portfolio yield at roughly 0.13%. That’s typical for growth- and innovation-focused strategies, where companies often reinvest profits into expansion instead of paying them out as cash. For this portfolio, dividends are essentially a minor side effect rather than a core return driver. In practice, that means returns will mainly come from price movements—whether the underlying companies grow earnings, gain investor enthusiasm, or benefit from theme-related tailwinds. Over long horizons, low-yield portfolios can still do very well if capital gains are strong, but they tend to be more sensitive to swings in market sentiment. With only two months of data, it’s too early to judge the consistency of any income stream here.
Ongoing product costs
- Invesco Dynamic Semiconductors ETF 0.57%
- ProShares Ultra Semiconductors 0.95%
- Weighted costs total (per year) 0.18%
Costs are measured using the total expense ratio (TER), which is the annual fee charged by each ETF. Individually, the Invesco semiconductor ETF sits at 0.57% and the leveraged ProShares fund at 0.95%, which is on the higher side compared with broad market index ETFs but typical for specialized thematic and leveraged strategies. The weighted portfolio TER comes out around 0.18%, which is quite competitive for such a focused, active-tilt line‑up. Lower overall costs mean investors keep more of any returns generated, and over many years the difference can compound meaningfully. With only two months of performance history, fees haven’t had much time to show up in net results yet, but structurally, this cost level is a positive aspect of the portfolio.
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