This portfolio is highly concentrated, with three holdings making up over 90% of total weight. A growth-focused ETF and an energy mutual fund each sit around one-third, and a single aviation stock is just over 30%. The remaining positions are relatively small and add some income and thematic exposure but don’t change the big picture much. This structure matters because portfolio behavior ends up being driven mostly by those top positions. When a portfolio is built around a few large bets, results can swing more sharply, both up and down, compared with a more spread-out mix of holdings.
Over the period shown, $1,000 grew to about $4,904, with a compound annual growth rate (CAGR) of 60.31%. CAGR is like your average speed on a long road trip, smoothing all the bumps to one yearly number. This massively outpaced both the US market and the global market, which were near 20% and below. The max drawdown of -38.23% shows that the portfolio has also experienced sharp falls, roughly double the market’s worst drop in this period. Only 23 days made up 90% of returns, which underlines how dependent the outcome has been on a few powerful up days. As always, past performance doesn’t guarantee similar future results.
The Monte Carlo projection runs 1,000 simulated futures using past risk and return patterns to estimate a range of possible outcomes. Think of it as rolling the dice many times to see different paths your portfolio might take, not a single forecast. The median result shows $1,000 growing to about $2,769 in 15 years, which corresponds to an annualized 8.23% across all simulations. The wide spread—from roughly $1,021 to $8,271 in the 5th to 95th percentile range—shows how uncertain long-term outcomes can be for a higher-risk portfolio. These projections rely on historical behavior, so real-world results can end up outside even these ranges.
Almost the entire portfolio is in stocks, with 99% classified as equities and 1% unclassified by the data provider. A stock-heavy mix generally means higher potential growth but also greater sensitivity to market swings, since there’s little ballast from bonds or cash-like assets. This aligns with the “aggressive” risk label and the high risk score. Compared with a more mixed asset allocation, this structure typically amplifies both gains and losses. It also means that any shocks to equity markets, like recessions or sharp rate moves, are more likely to be felt strongly here, because there are few assets that traditionally move differently during stress.
Sector-wise, the portfolio leans heavily into industrials at 32%, with technology at 19% and energy at 18%. Utilities, telecoms, and consumer sectors fill in the rest. This is less balanced than a broad market index, which usually spreads more evenly and often has a larger technology and financials footprint. An industrial and energy tilt can benefit from strong economic activity and higher commodity prices but may be more exposed during slowdowns or shifts away from fossil fuels. The sizable utilities and telecom stakes create some ballast, as these areas often have steadier cash flows, but they’re still a minority of the whole sector mix.
Geographically, about 88% of the exposure is in North America, with most of the balance in developed Europe and a small slice in emerging Asia. That’s a clear home bias compared with global benchmarks, where North America is large but not this dominant. A strong tilt to one region can work very well when that area leads global markets, as US stocks often have in recent years. It also means portfolio fortunes are closely linked to that region’s economy, interest rates, and currency. The modest non-US allocation still adds some global flavor, but the bulk of risk and return is tied to North American companies.
By market cap, the portfolio is anchored in larger companies: 36% mega-cap and 49% large-cap, with smaller positions in mid- and small-cap names. Larger firms often have more diversified businesses and deeper capital markets access, which can help them weather shocks better than tiny companies. At the same time, the presence of mid- and small-cap exposure introduces extra punch, as these can be more volatile and more sensitive to specific news. This mix leans closer to a typical large-cap dominated index than the concentrated holding list might suggest, but the single-stock bets mean behavior can still differ a lot from broad benchmarks.
Looking through the funds, the biggest single underlying exposure is FTAI Aviation at just over 30%, held directly. The ETFs add familiar mega-cap names like NVIDIA, Apple, Microsoft, Alphabet, Broadcom, Amazon, Tesla, and Meta, each in low single digits. These overlaps create a modest cluster in large US growth stocks, but the main hidden concentration issue actually comes from the explicit 30% position in FTAI Aviation, not duplication inside funds. Since ETF data only covers top-10 holdings, some overlap further down the lists isn’t captured, so total concentration in popular large caps could be slightly higher than shown.
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.
On factors, this portfolio has high momentum (75%) and high quality (65%) exposure, with low tilts to value, size, yield, and low volatility. Factor exposure is like seeing the “personality traits” of your portfolio—momentum being recent winners, quality being financially strong companies, and so on. A strong momentum tilt can help when markets trend upward and leadership persists, but it can hurt during sharp reversals. The quality tilt often cushions downside somewhat, as profitable, stable firms may hold up better. The low yield and low value readings fit with a growth-oriented style rather than a focus on cheap or high-dividend stocks.
Risk contribution highlights how much each holding drives the portfolio’s ups and downs, which can differ from simple weights. FTAI Aviation is 30.36% of the portfolio but contributes about 72.75% of total risk, more than double its weight. That’s a sign of a very volatile holding dominating day-to-day movements. In contrast, the growth ETF and energy fund together hold over 60% of the capital but contribute only about 25% of risk, showing they’re relatively calmer. When one position accounts for nearly three-quarters of total risk, overall results become heavily tied to that single company’s fortunes, for better or worse.
The correlation analysis shows that the Vanguard Mega Cap Growth ETF and the Unusual Whales Subversive Democratic Trading ETF have moved almost identically over the sample period. Correlation measures how often assets move together, on a scale from -1 to 1, where 1 is lockstep. When two holdings are highly correlated, they don’t add much diversification, even if they look different by name or theme. In practice, these funds behave similarly in market swings, so owning both doesn’t spread risk much in this specific pair. The rest of the portfolio’s diversification mainly comes from sector and single-stock differences, not from this ETF pair.
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 shows the current portfolio sitting on or very close to the efficient frontier. The efficient frontier is the curve of the best possible return for each risk level using your existing holdings in different mixes. The portfolio’s Sharpe ratio of 1.62—Sharpe being return per unit of risk—compares well to the minimum variance option and is reasonably close to the maximum Sharpe portfolio at 1.85. This suggests the current mix is already using these holdings in a risk/return-efficient way. In other words, the structure is efficient given the components, even though it remains high risk because of its underlying choices.
The portfolio’s total dividend yield is about 3.03%, which is a blend of very high and very low payers. The energy fund and the JPMorgan Equity Premium Income ETF stand out with yields above 7% and 8%, while the growth ETF and some smaller positions offer minimal income. Dividends represent cash that companies or funds distribute, which can be taken out or reinvested to compound over time. Here, income is meaningful but not extreme at the portfolio level because some large holdings are low-yield growth names. This creates a mix of current income and growth-oriented components within the same portfolio.
The portfolio’s weighted total expense ratio (TER) is about 0.18%, which is low by active-fund standards and in line with many cost-conscious strategies. TER is the annual fee charged by funds, expressed as a percentage of the money invested. The Vanguard growth ETF is particularly cheap at 0.07%, helping keep overall costs down, while the thematic ETF and energy fund charge more but are smaller weights. Low costs support better long-term outcomes because less return is lost to fees each year, and those small differences compound over time. Overall, the fee structure here is a notable strength of the portfolio.
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