This portfolio has only about 11 months of historical data, based on the youngest asset in the portfolio. Some metrics, projections, and AI insights may be less reliable and should be interpreted with caution.
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Speculative tech tilted portfolio with large cash buffer and unusually strong short term performance

Report created on Aug 7, 2026

Risk profile Info

7/7
Speculative
Less risk More risk

Diversification profile Info

4/5
Broadly Diversified
Less diversification More diversification
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Positions

This portfolio mixes a sizable cash-like buffer with a concentrated set of speculative and growth-focused holdings. Roughly a third sits in ultra-short-term Treasuries and a government money market fund, while the rest is spread across individual stocks and thematic or index ETFs. The equity sleeve leans into areas with higher business uncertainty and potential upside, which helps explain the “speculative” risk label. Because the data history is only about 11 months, the picture here is very much a snapshot rather than a long-term pattern. Overall, the structure combines a relatively stable core with a riskier satellite, meaning day‑to‑day moves are likely driven mostly by the stock and ETF portion, not the cash‑like holdings.

Growth Info

Over the short 11‑month window, $1,000 in this portfolio grew to about $1,760, far ahead of both the US and global market benchmarks. The compound annual growth rate (CAGR) of roughly 299% is extremely high and not something to treat as a normal long‑run expectation, especially with so little history. Max drawdown — the largest peak‑to‑trough drop — was about -24%, much deeper than the benchmarks’ mid‑single‑digit declines. Only five days generated 90% of total returns, which shows how dependent results have been on a handful of big moves. With such a short, explosive period, these figures are more like a highlight reel than a reliable guide to future behavior.

Projection Info

The forward projection uses a Monte Carlo simulation, which basically re‑mixes past return patterns thousands of times to create many possible futures. Here, 1,000 simulations of the next 15 years suggest a median outcome of about $2,401 from $1,000, with a wide “likely” range between roughly $1,811 and $3,213. The average simulated annual return is about 6.4%, only modestly above the assumed cash outcome, and there’s about a 74% chance of ending with more than was invested. Because the model feeds on only 11 months of highly unusual returns, its signals are quite shaky. It’s better seen as a way to understand uncertainty and ranges, not as a forecast of what will actually happen.

Asset classes Info

  • Stocks
    60%
  • No data
    31%
  • Cash
    9%

By asset class, about 60% of the portfolio is clearly in stocks, around 9% is in cash, and roughly 31% falls into “no data,” where the system simply can’t identify the asset type. Stocks are the main driver of growth and risk, while the cash portion helps dampen volatility and provides liquidity. The presence of a sizable cash-like allocation is notable for a portfolio flagged as speculative, as it can cushion sharp market moves. The “no data” bucket is just a limitation of the dataset, not necessarily a problem with the holdings themselves. From a high level, this mix means the portfolio is growth‑oriented but with a meaningful stabilizing slice on the side.

Sectors Info

  • Technology
    31%
  • Industrials
    13%
  • Cash
    9%
  • Basic Materials
    5%
  • Telecommunications
    2%
  • Health Care
    2%
  • No data
    2%
  • Energy
    1%
  • Consumer Discretionary
    1%
  • Utilities
    1%
  • Financials
    1%
  • Consumer Staples
    1%

This breakdown covers the equity portion of your portfolio only.

Sector-wise, technology stands out at about 31% of the portfolio, with additional exposure to industrials, basic materials, telecom, health care, and several smaller sectors. Cash again shows up as its own category, underlining that part of the portfolio isn’t tied to any particular industry cycle. A tech‑heavy tilt often brings higher sensitivity to innovation cycles, interest‑rate expectations, and investor sentiment toward high‑growth companies; this can boost returns when conditions are favorable but also lead to sharp swings. The rest of the sector mix broadens things somewhat across the economic landscape. Overall, the sector allocation is more concentrated than broad market benchmarks, which are typically more evenly spread across industries.

Regions Info

  • North America
    51%
  • Cash
    9%
  • Europe Developed
    6%
  • Asia Emerging
    2%
  • Asia Developed
    1%

This breakdown covers the equity portion of your portfolio only.

Geographically, a little over half of the portfolio is linked to North America, with smaller allocations to developed Europe and both developed and emerging Asia. That pattern is reasonably in line with many global equity benchmarks that overweight North America, and this alignment helps keep the portfolio connected to some of the world’s largest and most liquid markets. The presence of Europe and Asia adds some regional diversification, so results are not entirely tied to a single economy or policy regime. Cash again appears separately, reinforcing that a portion of the holdings is insulated from regional equity swings. With only 11 months of data, it’s too early to say how these regional slices behave across full economic cycles.

Market capitalization Info

  • Mega-cap
    21%
  • Large-cap
    18%
  • Mid-cap
    10%
  • Small-cap
    9%
  • No data
    2%
  • Micro-cap
    1%

This breakdown covers the equity portion of your portfolio only.

Looking at market capitalization, the portfolio spans the spectrum: exposure to mega‑caps and large‑caps is complemented by meaningful mid‑cap and small‑cap positions, plus a small micro‑cap slice. Mega‑ and large‑cap companies often bring more established businesses and deeper trading liquidity, while smaller firms can exhibit higher volatility and more company‑specific risk. This spread can be helpful for diversification across different business sizes and stages. It also means that part of the strong short‑term performance may have been driven by smaller, more volatile names catching a favorable run. The “no data” category here is again a data gap rather than a structural issue in the portfolio.

True holdings Info

  • Rocket Lab USA Inc.
    5.07%
  • Amprius Technologies Inc.
    4.16%
  • Applied Materials Inc
    4.14%
    Part of fund(s):
    • VanEck Semiconductor ETF
    Direct holding 3.93%
  • NVIDIA Corporation
    3.98%
    Part of fund(s):
    • Invesco NASDAQ 100 ETF
    • VanEck Semiconductor ETF
    • Vanguard S&P 500 ETF
    Direct holding 2.54%
  • Micron Technology Inc
    3.57%
    Part of fund(s):
    • Invesco NASDAQ 100 ETF
    • VanEck Semiconductor ETF
    • Vanguard S&P 500 ETF
    Direct holding 3.16%
  • ASML Holding NV
    3.16%
  • Western Digital Corporation
    2.35%
  • TTM Technologies Inc
    2.01%
  • Taiwan Semiconductor Manufacturing
    1.92%
    Part of fund(s):
    • VanEck Semiconductor ETF
    Direct holding 1.53%
  • SAGMF
    1.65%
  • Top 10 total 32.00%

This breakdown covers the equity portion of your portfolio only.

The look‑through view shows that the biggest underlying exposures are concentrated in a cluster of individual names, mostly in technology and related areas. Rocket Lab and Amprius appear only as direct holdings, but companies like NVIDIA, Applied Materials, Micron, and TSMC show up both directly and inside ETFs. That overlap creates “hidden” concentration: for example, NVIDIA’s true exposure is higher than the direct position alone suggests because it’s also held via index and thematic funds. It’s worth noting that ETF look‑through is based only on top‑10 holdings, so real overlap is likely understated. This structure means the portfolio may respond strongly to news affecting a relatively small group of core companies.

Factors Info

Value
Preference for undervalued stocks
Low
Data availability: 43%
Size
Exposure to smaller companies
Low
Data availability: 59%
Momentum
Exposure to recently outperforming stocks
Very high
Data availability: 44%
Quality
Preference for financially healthy companies
Neutral
Data availability: 37%
Yield
Preference for dividend-paying stocks
Neutral
Data availability: 81%
Low Volatility
Preference for stable, lower-risk stocks
Low
Data availability: 59%

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 shows an exceptionally high tilt to momentum at about 93%, while value, size, and low volatility all register as low. Momentum exposure means the portfolio is heavily tilted toward assets that have performed strongly in the recent past. Historically, momentum can do well in clearly trending markets, but it can be vulnerable when trends abruptly reverse, often leading to sharp drawdowns. Low scores on value and low volatility suggest less emphasis on cheaper or more stable companies, which can sometimes cushion declines in choppy periods. With only 11 months of history feeding the model — a period already marked by outsized gains — these factor readings are informative but may overstate how persistent the current style tilts will be over longer horizons.

Risk contribution Info

  • Micron Technology Inc
    Weight: 3.16%
    15.6%
  • Amprius Technologies Inc.
    Weight: 4.16%
    12.6%
  • Western Digital Corporation
    Weight: 2.35%
    10.8%
  • Applied Materials Inc
    Weight: 3.93%
    10.2%
  • Rocket Lab USA Inc.
    Weight: 5.07%
    6.8%
  • Top 5 risk contribution 56.0%

Risk contribution breaks down how much each holding drives overall ups and downs, which can be very different from weight alone. Here, Micron, Amprius, and Western Digital together make up a relatively modest slice of the portfolio by weight but contribute nearly 39% of its total risk. Each of these has a risk‑to‑weight ratio several times higher than one, meaning they punch far above their size in terms of volatility. Rocket Lab and Applied Materials also add noticeably more risk than their allocations might suggest. This kind of concentration is common in speculative portfolios where a few volatile names dominate behavior, and it helps explain why the portfolio has both very strong recent gains and a relatively deep short‑term drawdown.

Redundant positions Info

  • ASML Holding NV
    Aura Minerals Inc. Common Shares
    Applied Materials Inc
    Western Digital Corporation
    Micron Technology Inc
    Constellium Nv
    Vanguard 0-3 Month Treasury Bill ETF
    Taseko Mines Ltd
    High correlation

Correlation looks at how holdings move relative to each other, on a scale from -1 (opposite directions) to +1 (almost in lockstep). Several pairs here, especially within the tech and semiconductor cluster, show very high correlations: Applied Materials, ASML, Micron, and Western Digital tend to move together. That means when one of these names is having a good or bad day, the others often move in the same direction, amplifying the impact on the portfolio. Somewhat unusually, certain equities appear highly correlated with the short‑term Treasury ETF, which may be an artifact of the short, volatile sample period rather than a stable relationship. With only 11 months of data, these correlations are best seen as early hints, not fixed long‑term relationships.

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.

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The risk‑return optimization chart compares the current mix with two hypothetical alternatives built from the same holdings: the optimal (highest Sharpe ratio) and the minimum‑variance portfolios. The Sharpe ratio measures risk‑adjusted return — how much extra return is earned per unit of volatility. In this short backtest, the current portfolio shows a high Sharpe but sits well below the efficient frontier, meaning the same holdings could have delivered better risk‑adjusted results with different weights over this period. The optimal mix in the model achieves a higher Sharpe with lower risk, and the minimum‑variance portfolio sharply cuts volatility with still positive returns. Because these are all based on 11 unusual months, they illustrate potential trade‑offs, not a precise blueprint.

Dividends Info

  • Applied Materials Inc 0.40%
  • ASML Holding NV 0.60%
  • Leonardo DRS, Inc. Common Stock 0.80%
  • iShares U.S. Infrastructure ETF 1.60%
  • Micron Technology Inc 0.10%
  • VanEck Uranium+Nuclear Energy ETF 2.80%
  • NVIDIA Corporation 0.10%
  • Invesco NASDAQ Next Gen 100 ETF 0.50%
  • Invesco NASDAQ 100 ETF 0.40%
  • VanEck Semiconductor ETF 0.20%
  • Taiwan Semiconductor Manufacturing 0.70%
  • Vanguard S&P 500 ETF 1.00%
  • Vanguard Total International Stock Index Fund ETF Shares 2.50%
  • Western Digital Corporation 0.10%
  • SPDR® S&P Metals and Mining ETF 0.30%
  • SPDR® S&P Telecom ETF 1.20%
  • Vanguard 0-3 Month Treasury Bill ETF 3.60%
  • Aura Minerals Inc. Common Shares 3.40%
  • Fidelity® Government Money Market Fund 3.10%
  • Weighted yield (per year) 1.67%

The portfolio’s overall dividend yield is about 1.67%, which is modest and consistent with a growth‑ and momentum‑oriented tilt. Most of the individual growth and tech names pay very low dividends, if any, while higher yields come from areas like the short‑term Treasury ETF, the money market fund, certain international and sector ETFs, and a few resource‑linked stocks. Dividends can provide a steady income stream and help smooth total returns when prices are flat, but here they are clearly a secondary driver compared with price movements. Given the limited history, the exact yield may shift as payouts and prices change, yet the basic pattern of income being a smaller part of the total return story is clear.

Ongoing product costs Info

  • iShares U.S. Infrastructure ETF 0.30%
  • VanEck Uranium+Nuclear Energy ETF 0.61%
  • Invesco NASDAQ Next Gen 100 ETF 0.15%
  • Invesco NASDAQ 100 ETF 0.15%
  • VanEck Semiconductor ETF 0.35%
  • Vanguard S&P 500 ETF 0.03%
  • Vanguard Total International Stock Index Fund ETF Shares 0.05%
  • SPDR® S&P Metals and Mining ETF 0.35%
  • SPDR® S&P Telecom ETF 0.35%
  • Vanguard 0-3 Month Treasury Bill ETF 0.06%
  • Fidelity® Government Money Market Fund 0.42%
  • Weighted costs total (per year) 0.11%

On costs, the portfolio is quite efficient overall. The total expense ratio (TER) across the funds and ETFs is about 0.11%, which is low by industry standards and supports better long‑term compounding because less return is lost to fees each year. Core index funds, like the broad US and international equity ETFs, anchor costs at the very low end, while more specialized thematic funds and the money market product sit higher but remain reasonable. Low fees do not guarantee good outcomes, especially in a speculative, volatile portfolio like this, but they remove a common drag that investors often face. Over many years, keeping costs modest can materially boost the net amount that stays invested and growing.

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