Asset Allocation
Find the right asset allocation for Cathie Wood Portfolio
Add portfolio to the optimizer to find optimal allocations for your target — whether that's maximizing returns, minimizing drawdowns, or balancing risk across holdings.
Open Portfolio OptimizerPerformance
Performance Chart
The chart shows the growth of an initial investment of $10,000 in Cathie Wood Portfolio, comparing it to the performance of the S&P 500 index or another benchmark. All prices have been adjusted for splits and dividends. The portfolio is rebalanced Every 3 months.
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Returns By Period
Monthly Returns
Expense Ratio
Below, you can find the expense ratios of the portfolio's funds side by side and easily compare their relative costs.
Return for Risk
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Dividends
Dividend yield
Drawdowns
Drawdowns Chart
The Drawdowns chart displays portfolio losses from any high point along the way. Drawdowns are calculated considering price movements and all distributions paid, if any.
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Worst Drawdowns
The table below displays the maximum drawdowns of the Cathie Wood Portfolio. A maximum drawdown is a measure of risk, indicating the largest reduction in portfolio value due to a series of losing trades.
The portfolio has not yet recovered.
Volatility
Volatility Chart
The chart below shows the rolling one-month volatility.
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Diversification
AI Analysis
Thesis
The portfolio is a concentrated bet on high-beta innovation, with a second, slightly stranger bet on the parts of financials that behave like venture capital with a ticker.
The numbers
- The diversification ratio is 1.74, at the 91.3th percentile on the platform, so there is real offsetting among names even though the theme is obvious.
- Effective asset count is 37.67 out of 168; that is a respectable breadth of names, but the risk is still carried by a much smaller core.
- Mean pairwise correlation is 0.25 and the top overlap pairs are extreme: ARKK with ARKB at 0.99, HOOD with ARKK at 0.84, COIN with HOOD at 0.82.
What works
- The portfolio spans several distinct earnings engines: software, biotech, industrial automation, and speculative trading/crypto, so it is not just one crowded trade in disguise.
- There are enough low-weight satellites that some names genuinely diversify the dominant cluster, which is why the diversification ratio is not mediocre.
- To be fair, the presence of names like DE, INTU, and CAT gives the portfolio a few ballast-like relationships that are not perfectly welded to the growth complex.
What does not
- The largest positions sit in tightly linked clusters, especially ARKK, COIN, HOOD, BMNR, CRCL, and ARKB; the math says those are cousins, not diversifiers.
- Several holdings have high portfolio correlations above 0.70 — HOOD, COIN, TEM, ACHR, RXRX, JOBY — so the portfolio’s effective risk is more concentrated than the name count suggests.
Stress Scenario
- If rates rise, speculative growth reprices, and crypto-linked risk appetite fades at the same time, the portfolio’s internal correlations would likely climb rather than cushion each other.
- If the market stops rewarding “duration plus narrative” names, the software, biotech, and fintech sleeves can start looking less like separate sleeves and more like one very enthusiastic sleeve.
Worth knowing
- The portfolio’s correlation structure fits a view on the same liquidity regime more cleanly than a view on separate businesses.
- Portfolios with this profile often look diversified until the factor tape gets stressed, and then the clustering becomes the main event.
Diversification Metrics
Number of Effective Assets
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Not enough data to calculate this metric.
Diversification Ratio
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Not enough data to calculate this metric.