The portfolio looks like someone started with a clean target-date solution and then panic-added every “balanced” fund they could find, plus a big vanity stake in Salesforce. Nearly half the money lives inside overlapping target-date and allocation funds trying to do the same job, often with different opinions. That sprawling setup makes it hard to know what’s actually driving risk or growth; it’s like having five chauffeurs all yanking the same steering wheel. On paper it scores as “broadly diversified,” but structurally it’s a cluttered nesting doll of funds inside funds. With only ~1.8 years of history, it’s especially hard to tell whether this mashup behaves gracefully or just hasn’t hit real stress yet.
Over this short 1.8-year window, the results look suspiciously heroic: $1,000 turning into $1,489, a 24.25% CAGR versus 17.71% for the US market and 19.33% globally. That’s great, but past 1.8 years of data is like bragging after one lucky season. The max drawdown of -16.53% wasn’t trivial, but it actually lost slightly less than the US market’s -18.76% in the same stretch. The kicker: 90% of returns came from just 9 days, which is classic “you better have been fully invested on the right mornings” territory. This outperformance says more about recent conditions than any proven, repeatable edge.
The Monte Carlo projection is basically a fancy “what if” machine run on a wobbly 1.8-year history, so take it with a large grain of salt. It spits out a median path of $1,000 growing to $2,335 over 15 years, with a wide $1,109–$4,922 possible range. Annualized, the simulations land around 6.31%, which is far less glamorous than the recent 24% CAGR party. That spread shows how sensitive this portfolio is to future conditions, especially with a risk engine that’s barely met a full market cycle. Think of this as a weather forecast made after watching only one weird season.
At the asset-class level this thing tries to be a “growth” portfolio without fully committing. About 61% is in stocks, 15% in bonds, and a chunky 23% is in the “no data” black box. So even the classification is shrugging. For a supposedly growth-tilted setup, the bond slice and mystery bucket make the message fuzzy: is this trying to be aggressive, balanced, or just indecisive? Asset classes are the main knobs for risk, and when almost a quarter of the portfolio is an unknown category, it’s like driving at night with one headlight out. You’re moving forward, but you don’t really see the road.
This breakdown covers the equity portion of your portfolio only.
Sector-wise, there’s a clear leaning toward technology at 24%, with everything else playing backup singers. Financials, industrials, and health care trail far behind, and the smaller slices in energy, real estate, and utilities look more like accidental exposure than deliberate calls. There’s even a double-listed consumer discretionary line, which nicely matches the portfolio’s “copy-paste more funds” personality. With only a short lookback, it’s impossible to declare this a genius tech tilt or just lucky timing in a growth-friendly phase. But structurally, when one sector towers over the rest like this, the portfolio’s mood will swing with that one corner of the market.
This breakdown covers the equity portion of your portfolio only.
Geographically this portfolio screams “US first, we’ll sprinkle in the rest.” North America sits at 56%, while Europe developed is a modest 8%, Japan 4%, and everything else is tiny single digits, plus 4% “no data” mystery land. It’s not a pure America-or-bust stance, but it’s definitely America-with-tourist-trips. That’s fairly typical, yet it still means the portfolio’s fate is heavily hooked to one region’s economy, politics, and currency. With only 1.8 years of returns, there’s zero evidence this bias is helping or hurting long term; it just means any US wobble will echo loudly in the overall performance.
This breakdown covers the equity portion of your portfolio only.
The market-cap blend is reasonably conventional, but not exactly laser-focused. Mega-cap at 16% plus large-cap at 23% dominate, as usual, with mid-cap at 14% and small/micro at a modest 7% combined. It’s like the portfolio wants a bit of spicy small-cap upside but is too nervous to actually lean into it. For a growth-labeled setup, the tilt is surprisingly middle-of-the-road rather than clearly skewed toward smaller companies. Because the history is so short, there’s no real read on how this mix behaves in a drawn-out bear or recovery; right now it’s just a generic size profile with no strong conviction.
This breakdown covers the equity portion of your portfolio only.
The look-through holdings data covers only 14.6% of the portfolio, so we’re basically peeking through a keyhole and calling it interior design. What we can see: Salesforce is a chunky 10.1% held directly, then pops up precisely nowhere else, so at least that obsession is honest. The rest of the visible overlap is the usual mega-cap suspects—Apple, NVIDIA, Microsoft, Amazon, Alphabet—each at sub-0.4% levels via broad ETFs. Hidden overlap is probably much higher inside all the target-date and balanced funds, but the top-10-only data can’t show it. Translation: actual concentration risk is almost certainly being undercounted by this nice-looking but incomplete 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-wise, this portfolio has an almost comical combo: very high quality (80%) with high value (61%), low size (26%), and low momentum (32%). Factor exposure is basically the hidden recipe—why returns and risk behave the way they do. Here the recipe reads “grown-up value with training wheels.” The quality tilt means lots of stronger balance sheets and more stable businesses, while the value tilt leans toward cheaper-looking stocks. But the low size and low momentum exposure say it’s not exactly chasing hot trends or smaller high-flyers. Over a full cycle this could mean smoother relative behavior, but with just 1.8 years of data, that “safety” might be more illusion than proven trait.
The real circus is in the risk contribution: the SP Funds 2040 Target Date Fund is 18.84% of the weight but a ridiculous 60.82% of total portfolio risk, more than triple its share. That single fund is doing the volatility heavy lifting while everything else pretends to diversify. Salesforce at 10.10% weight only contributes 8.57% of risk, which is almost polite by comparison. The top three holdings together are 40% of the portfolio but drive 74.28% of the risk. So on paper it looks like a many-fund buffet; in practice it behaves like one loud target-date bet with a Salesforce side dish.
The correlation list reads like a roll call of funds all secretly doing the same job. Target-date funds move like other target-date funds, balanced funds mirror broad indexes, and several small-cap and mid-cap products are basically twins. Correlation just means how similarly things move; here, many positions are basically synchronized swimmers. That kills the illusion that all these different logos and prospectuses equal genuine diversification. In a real downturn, a lot of this stuff will likely go down together, just in slightly different fonts. The short history hasn’t yet served a major crisis, so this copycat behavior is probably understated rather than exaggerated.
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 optimization section is brutally honest: the current portfolio Sharpe ratio is 1.0, while the optimal mix of the same holdings hits 2.59 with much lower risk (3.18% vs 18.38%). The efficient frontier is basically the map of best possible trade-offs using what’s already in the cart. Being 9.64 percentage points below that frontier at the current risk level is like driving a sports car in first gear—loud, inefficient, and kind of pointless. The minimum variance mix even gets a higher Sharpe than the current version. In short, the recipe isn’t the problem; the proportions are. And that’s before we admit the whole exercise is based on just 1.8 years of noisy data.
Yield-wise, the portfolio isn’t exactly a cash-flow machine: total yield sits at 1.68%, which is “better than nothing” but not remotely income-focused. Some funds throw off chunky distributions—like the American Balanced Fund at 7.70% and the T. Rowe retirement balanced fund at 4.80%—while growthy names like Salesforce barely bother. The mix feels accidental: a handful of big-yield products dropped into an otherwise return-first structure. With such a short history, the sustainability of those higher payouts is a big question mark. Dividends are nice, but here they’re more of a side effect than a clearly designed feature.
Cost-wise, the portfolio somehow manages to be both cheap and weirdly expensive. The overall TER of 0.12% is impressively low—“you clicked the right ETFs” territory. But then there’s the 1.43% 529-C share class and other chunky active funds quietly inflating the bill on pieces that could be much simpler. Paying over 1% for anything in a lineup that already includes ultra-cheap core funds is like tipping 200% on a dinner that came with a free buffet. Over decades, those little pockets of fee bloat would matter a lot more than this 1.8-year snapshot suggests, even if they’re currently masked by strong returns.
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