This portfolio is mostly in broad stock index funds, with a small bond slice and a few focused “satellites.” Around 59% sits in a total US market fund and about 22% in an international equity fund, forming a diversified core. Smaller positions in a semiconductor fund, a memory‑themed ETF, a defense stock, and a small‑cap index add more specific exposures. A tiny bond index holding rounds things out. Structurally, this is a classic “core‑satellite” setup: a broad, diversified base plus a handful of focused positions. That kind of structure can make performance easier to understand, because most behavior comes from the core, while the satellites create targeted tilts without dominating everything.
Over the very short one‑month window available, the portfolio turned $1,000 into about $1,144, which implies a huge annualized CAGR of 310%. That number looks dramatic, but over such a brief period it mostly reflects a strong recent burst rather than a stable long‑term pattern. Max drawdown, the largest peak‑to‑trough drop, was mild at about -1.1%, similar to the referenced markets. The portfolio outpaced both the US and global benchmarks in this snapshot, but with only about a month of data, that outperformance could easily reverse. Past performance over such a short stretch is more like a weather report than climate data: interesting, but not something to rely on for long‑term expectations.
The forward projection uses a Monte Carlo simulation, which basically means running thousands of “what if” scenarios based on the limited history and volatility observed. Here, a $1,000 starting amount ends at a median of about $2,702 after 15 years, with a wide typical range between roughly $1,720 and $4,126. These numbers translate to an average simulated annual return around 7.9%. Because the input history is only about a month, the model is leaning heavily on that short run and statistical assumptions, not on a rich long‑term track record. So the projection is best viewed as a rough, educational illustration of potential variability, not a precise forecast.
By asset class, about 95% of the portfolio is in stocks, around 2% in bonds, and a small slice is tagged as “no data.” This means the ride is overwhelmingly driven by equity markets, with bonds playing only a modest stabilizing role. Equity‑heavy mixes usually swing more with market news but also carry more growth potential over long horizons. The small bond holding and low‑volatility tilt (seen in factors) suggest a slight nod toward smoothing the bumps without meaningfully changing the stock‑driven character. Since the “no data” bucket is tiny, it doesn’t materially change the overall picture: this behaves much more like a stock portfolio than a balanced stock‑bond mix.
This breakdown covers the equity portion of your portfolio only.
Sector exposure shows a clear tilt toward technology at about 30%, with industrials next at 15%, followed by financials, consumer areas, and health care. Compared with many broad global indices, that tech weight is on the high side, and it’s amplified by the dedicated semiconductor and memory‑themed positions. Tech‑heavier portfolios can do very well in periods of innovation optimism but may feel more impact when growth expectations cool or interest rates rise. At the same time, having meaningful exposure to industrials, financials, and other sectors provides some balance. This spread across multiple areas helps avoid everything riding on a single business theme, even if technology is the clear leader.
This breakdown covers the equity portion of your portfolio only.
Geographically, roughly three‑quarters of the portfolio is in North America, with the rest spread across Europe, Japan, other developed Asia, and small slices in emerging regions. That North America tilt lines up fairly closely with many global equity benchmarks, which often give the US a large weight because of its market size. A structure like this tends to anchor results to US market conditions while still drawing on diversification from overseas companies. The modest exposures to Europe and Asia mean foreign markets contribute, but they’re not dominant. This alignment with common global patterns is generally positive for diversification while keeping currency and economic exposure largely US‑centric.
This breakdown covers the equity portion of your portfolio only.
The market capitalization breakdown is dominated by mega‑ and large‑cap companies, together around 72%, with smaller portions in mid‑, small‑, and micro‑caps. That’s broadly in line with how global equity markets are structured: big companies take up most of the total value. Larger firms tend to be more established and, on average, a bit less volatile than very small stocks, which can make returns feel somewhat steadier. The smaller stakes in mid and small caps still add some extra growth and risk flavor without driving the overall experience. This blend supports diversification across company sizes while keeping the portfolio anchored in widely followed, liquid names.
This breakdown covers the equity portion of your portfolio only.
The look‑through view is limited here, covering only about 7.7% of the portfolio, because it relies on ETF top‑10 holdings and not full fund holdings. Even so, it shows Raytheon entirely as a direct 5.28% exposure, without overlapping through funds in the top‑10 data, which keeps that particular position straightforward. Within the memory‑themed ETF slice, small weights appear in names like SK Hynix and Samsung, but each is under 1% of the total portfolio. Because the uncovered portion is over 90%, hidden overlap between broad index funds isn’t fully captured. That means any concentration in large global companies inside those index funds is likely understated in this snapshot.
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 highlights a very high tilt to low volatility at 98%, plus high quality at 67%, while value is neutral and size shows a very low reading. In factor terms, low volatility means the holdings tend, based on recent data, to move less sharply than the broader market; quality points to stronger balance sheets or profitability metrics. A very low size exposure suggests a lean toward larger companies rather than smaller, more speculative names. These characteristics often line up with a smoother ride in typical markets, though they can lag in aggressive risk‑on periods. With only about a month of history, these tilts are still informative but could change as more data accumulates.
Risk contribution shows how much each position drives the portfolio’s overall ups and downs, which can differ from its weight. The US total market fund is almost 59% of the weight but only about 46% of the risk, meaning it’s relatively stable compared with others. The international fund, at 22% weight, adds around 27% of risk, and the semiconductor fund, at 8%, contributes about 11%. The standout is the small 2.9% memory ETF slice, which accounts for nearly 8% of total risk, reflecting its higher volatility. Overall, the top three holdings drive over 84% of risk, showing that despite some satellites, the core still controls most of the portfolio’s behavior.
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 the best possible combinations of the same holdings. The current portfolio has a very high Sharpe ratio of 9.14 in this short window, but it still sits well below the frontier at its risk level, by about 65 percentage points. That means, based on recent behavior, other weightings of the existing funds could have delivered higher return for the same volatility, or similar return with less volatility. The “optimal” portfolio in the model shows a much higher Sharpe of 14.68, while the minimum‑variance mix would sacrifice return for much lower risk. Because all inputs reflect only one month, these efficiency gaps are more of a math curiosity than a stable roadmap.
The portfolio’s total indicated yield is about 2.03%, coming from a mix of equity and bond distributions. The broad US and small‑cap index funds show sub‑1% yields, while the international index and bond fund provide a bit more income, and Raytheon adds a modest 1.5%. One data point stands out: the semiconductor fund’s listed 10.5% yield, which may reflect a recent special distribution or short‑term quirk rather than a steady income stream. Dividends can be an important part of total return over time, but yields also change with market prices and company policies. With limited history, it’s too early to draw strong conclusions about the long‑term income profile.
Total ongoing costs look very low, with a combined TER around 0.05%. Most of the core index funds carry extremely low expense ratios of 0.02%, while the specialized semiconductor fund is higher at 0.60% but only a small piece of the portfolio. In practice, that means the portfolio keeps most of its gross returns after fees, which is a big plus over long periods because costs compound just like returns do. This cost profile aligns well with common best practices of using low‑fee index products as a foundation and only paying higher fees for targeted exposures where desired.
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