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This page explains how Prism.AI computes True Exposure and the Diversification Score: what the number means, what it does not measure, and how the look-through calculation works under the hood.
The Diversification Score is a single number from 0 to 100 that summarises how evenly your capital is spread across individual companies and sectors, after looking through your ETFs, funds, and other holdings.
The score is not a recommendation and it is not a verdict. If you hold a concentrated portfolio on purpose (a high-conviction bet on a single sector, a barbell strategy, a single-stock position), a low score is telling you something you already know. If you thought you were diversified, a low score shows you where you actually stand.
The score is composed of two components:
Descriptive band labels:
| Range | Label |
|---|---|
| 0–25 | Highly concentrated |
| 26–50 | Concentrated |
| 51–75 | Moderately spread |
| 76–100 | Broadly spread |
The score measures only spread. It says nothing about:
The score is a description of your portfolio's structure as it stands today. It is not a grade and it is not advice.
When you hold an ETF or fund, you do not own it outright; you own a proportional share of every company inside it. If you hold NDQ and also hold NVDA directly, your true NVDA exposure is higher than your allocation chart shows.
For each holding in your portfolio, Prism does the following:
fund_weight × constituent_weight_within_fund. Contributions from multiple sources are summed.The result is a single map of company → true weight across your entire portfolio.
If a portfolio held the NDQ ETF at 40% and also held NVDA directly at 10%, and NDQ's weight in NVDA is approximately 9%:
True NVDA exposure = (40% × 9%) + 10% = 3.6% + 10% = 13.6%
This is higher than the 10% shown in the allocation chart. The overlap callout surfaces this difference. This is an illustration of how the calculation works. It is not a comment on NDQ, NVDA, or either as an investment.
Our data file holds the top 25 constituents per fund by weight. For a broad fund like VTI (approximately 3,700 holdings), the top 25 cover roughly 40% of the fund's weight. The remaining 60% is modelled as equally-weighted pseudo-positions summing to the residual weight.
This residual model is not optional. Without it, a total-market ETF would behave like a 25-stock portfolio and the score would be badly wrong. The residual pseudo-positions carry no ticker, sector, or country identity; they contribute to the position component but not to the sector component.
The underlying measure is the Herfindahl–Hirschman Index, a standard measure of concentration borrowed from competition economics. For a set of weights summing to 1:
HHI ranges from 1/n (perfectly even across n positions) to 1 (fully concentrated in one position). Its inverse, effective N, is the number of equally-weighted positions that would produce the same HHI:
The position score maps effective N to 0–100 on a logarithmic curve, targeting 50 effective positions as the "broadly spread" reference:
The curve is logarithmic because the difference between 3 and 6 effective positions is meaningful; the difference between 40 and 50 is not.
The same HHI maths applied to sector-aggregated look-through weights, targeting 8 effective sectors (an even split across all 11 GICS sectors almost never occurs in practice):
Tickers whose sector is unavailable are excluded and the remaining weights are renormalised. An "unknown" bucket is never created; that would penalise ASX-heavy portfolios for a data gap, not a real property of the portfolio.
If sector data covers less than 5% of total portfolio weight, the sector component is suppressed and the final score equals the position component alone.
If sector data is unavailable: score = round(positionScore)