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ANALYTICS
AnalysisScreenerGroups

Selection

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MNRS

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Benchmark

Pick one fund or composite — returns and exposures render in excess of it

Peer Universe

Reference set for percentile ranks and factor bars

Digital Assets Funds · 168 total

ANALYTICS
AnalysisScreenerGroups

MNRS

Grayscale Bitcoin Miners ETF

Expense Ratio
0.59%
AUM
$11M
Inception
1/31/2025
·

The fund invests at least 80% of net assets in the constituents of the Index, which tracks companies engaged in high-performance computing, AI cloud, and accelerated computing activities. The Index includes companies classified as Pure-Play, Quasi-Play, or Marginal-Play based on their revenue exposure to the High Performance Computing theme, with a focus on those in developed and emerging markets. The fund employs a passive management strategy, aiming to replicate the Index while allowing for representative sampling under certain conditions.

Time Series
–

Performance

NameCumulative ReturnAnn. ReturnAnn. VolSharpeMax DD
MNRS
Grayscale Bitcoin Miners ETF
-4.2%-99.5%24.7%-4.03-4.2%
ⓘ Methodology

ETF performance based on market prices. Drag on chart to select period. Sharpe Ratio uses the one-month T-bill as the risk-free rate. From 9/22/2026 to 9/25/2026.

Trailing Returns

Name
YTD1D5D1M3M6M1Y2Y3Y5YMAX
MNRS
Grayscale Bitcoin Miners ETF
-4.2%-2.9%-4.2%
ⓘ Methodology

ETF performance based on market prices. Returns for periods greater than one year are annualized. As of 9/25/2026.

Annual Returns

Name
YTD
MNRS
Grayscale Bitcoin Miners ETF
-4.2%
ⓘ Methodology

ETF performance based on market prices. As of 9/25/2026.

Correlation Matrix

Benchmark

Select a second series, or to correlate with the seven research factors.

Factor Regression

Not enough overlapping history for a regression in this period.
ⓘ Methodology

ETF performance based on market prices. Daily OLS of excess return (fund − T-bill) on factor spread returns over the selected date range, intersected with each series' history. Bold with * |t| ≥ 1.96, ** |t| ≥ 2.58. Cell shading tracks each loading's t-stat. Betas are returns-based — compare with the holdings-based factor scores below. Factor returns are long-short spread returns (© Ken French, Dartmouth).

SnapshotAs ofvsⓘ

Fund Profile

Family
Registrant
Type
ETF
Asset Class
Peer Asset Class
Digital Assets
Region
Equity Sleeve Style
Small Growth
Peer Group
Digital Assets - Equity (27 peers)
Expense Ratio
0.59%
AUM
$11M
Yield
0.50%
Inception
1/31/2025
Holdings (Reported)
25
Turnover
65%
Equity
78%
Derivatives
0%
Net Equity Exposure
99%
Strategy Summary

The fund invests at least 80% of net assets in the constituents of the Index, which tracks companies engaged in high-performance computing, AI cloud, and accelerated computing activities. The Index includes companies classified as Pure-Play, Quasi-Play, or Marginal-Play based on their revenue exposure to the High Performance Computing theme, with a focus on those in developed and emerging markets. The fund employs a passive management strategy, aiming to replicate the Index while allowing for representative sampling under certain conditions.

ⓘ Methodology

Expense ratio and its components from the fund's prospectus fee table. Turnover from the fund's prospectus. Yield as of 10/6/2026, AUM 9/30/2026. Holdings, asset class, style, peer group and exposures as of 6/30/2026. Strategy summary from the fund's prospectus.

Equity Size & Style Map

LargeMidSmall
-
-
2%
-
3%
12%
-
-
83%
Large
ValueCoreGrowth
Size
Large
2%
Mid
14%
Small
83%
Style
Value
0%
Core
3%
Growth
97%
ⓘ Methodology

Equity size and style definitions: Large vs. Small based on market cap, where Large > $70B, Mid $10-70B, and Small < $10B. Value vs. Growth based on percentile ranking on Traditional Value score within the fund's region and size cohort (equal thirds among its true equity-fund peers). This fund is not an equity fund, so the box describes its equity sleeve only (78% of assets): the sleeve's holdings are scored and ranked against equity funds of the same region and size cohort. The rest of the portfolio (bonds, cash, other assets) is not represented here. Cell shading and percentages: share of classified holdings weight per box, from stock-level classification (size on the same $70B/$10B lines; value by Traditional Value terciles across scored stocks). The dot is the average holding ranked against other funds, so dot and weights can differ at the margin. The Size and Style bars are the grid's row and column totals. Holdings as of 6/30/2026. Stock-level data as of 8/31/2026.

Composite FactorsRef: Digital Assets Funds

Intangible Value1.8
Traditional Value0.3
Small-Cap81.7
Quality2.1
Momentum96.1
ⓘ Methodology

Hover or click for factor definitions. Weighted average of underlying company-level values with weights equal to position size. Ranked within the clean equity fund universe, one entry per fund series (lowest: 0, highest: 100; the middle of the range is denser than the ends). Blue dots mark the fund; the shaded band spans the Digital Assets Funds class 25th–75th percentile of scores, notched at the median. Holdings as of 6/30/2026. Stock-level data as of 8/31/2026.

Factor DetailsRef: Digital Assets Funds

Valuation
Book-to-Price (NTM)12.9
Sales-to-Price (NTM)15.4
Earnings-to-Price (NTM)-6.6
Innovation/IP
R&D / Mkt Cap3.0
Patents / Mkt Cap0.11
Brand
Sales & Marketing / Mkt Cap0.66
Trademarks / Mkt Cap0.05
Human Capital
PhD Employees / Mkt Cap0.74
Top University Alumni / Mkt Cap3.1
Politics
Lobbying Spend / Mkt Cap37.1
AI
AI Employees / Mkt Cap0.36
ⓘ Methodology

Hover or click for factor definitions. Weighted average of underlying company-level values with weights equal to position size. Solid blue dots mark the fund's position within the clean equity fund universe (5th–95th percentile scale); the shaded band spans the fund's broad class (Digital Assets Funds) 25th–75th percentile, notched at the median. Holdings as of 6/30/2026. Stock-level data as of 8/31/2026. Expense ratio, yield, and turnover as of 10/6/2026. AUM as of 9/30/2026.

Asset Class Exposure

Equity77.5%
Cash & Short-Term22.5%
ⓘ Methodology

Shares of the fund's long book, from the asset category each position is filed under. The long book is 128% of net assets, so shares are of the long book rather than of the fund - it is levered by 28%.

Country Exposure

Level

Based on holdings that carry a country (78% of total assets).

80.9%
80.9%
18.7%
11.3%
6.9%
Other0.5%
Other (1 region)0.4%
ⓘ Methodology

Shares are percentages of the holdings that carry a country, not portfolio weights. Holdings as of 6/30/2026. Stock-level data as of 8/31/2026.

Sector Exposure

Level
64.2%
27.1%
6.2%
2.6%
ⓘ Methodology

Shares are percentages of the fund's classified holdings — every position we can identify, with cash and unidentified positions excluded — not portfolio weights. Holdings as of 6/30/2026. Stock-level data as of 8/31/2026.

Thematic Exposure

AI Composition

Infrastructure2.5%
Early Adopter74.3%
Laggard23.3%

Magnificent 7

Magnificent 72.5%
Non-Magnificent 797.5%

Old vs New Economy

Old Economy63.9%
New Economy36.1%

Globalization

Multinational8.1%
Exporter0.0%
Importer11.7%
Domestic80.2%

China Exposure

China/HK Domiciled0.9%
Revenue & Operations0.0%
Operations Only2.4%
Revenue Only0.0%
No Material Exposure96.7%

Company Size

Mega (>$500B)2.5%
Large ($70–500B)0.0%
Mid ($10–70B)14.4%
Small ($1–10B)71.3%
Micro (<$1B)11.8%
ⓘ Methodology

Shares are percentages of the fund's classified equity book, renormalized so each split sums to 100% (the equity book is 78% of net assets) — they are not portfolio weights. China exposure: domiciled = incorporated in China or Hong Kong, falling back to the filing's country for holdings outside the stock model. Among the rest, revenue = ≥15% of revenue derived from China; operations = ≥3% of LinkedIn-tracked workforce in China, or ≥2 identified Chinese suppliers making up ≥12% of those identified. China revenue is estimated where segment reporting is absent. Company size bands by holding market cap: Mega >$500B, Large $70–500B, Mid $10–70B, Small $1–10B, Micro <$1B. Old vs New Economy partitions Sparkline's 48 industries: New = technology, internet platforms and entertainment, e-commerce, payments, pharma/biotech/medtech, and renewable energy; Old = everything else. Shares are of classified holdings and sum to 100%. China shares are of total equity weight. Size shares are of scored holdings weight. Holdings as of 6/30/2026. Stock-level data as of 8/31/2026.

Holdings

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25 positions (top 10 shown, create for full holdings)

#NameTickerWeight %SectorIndustryCountry
1First American Government Obli-
28.83
--USA
2Hut 8HUT
15.03
FinancialsInvest BankingUSA
3Applied DigitalAPLD
12.30
TechnologyIT ConsultingUSA
4IrenIREN
11.20
FinancialsInvest BankingAUS
5Keel InfrastructureKEEL
7.27
FinancialsInvest BankingUSA
6Riot PlatformsRIOT
5.58
HealthcareBiotech & Life SciUSA
7Bitdeer Technologies GroupBTDR
5.38
TechnologySoftwareSGP
8Hive Digital TechnologiesHIVE
5.07
--CAN
9Cipher DigitalCIFR
4.82
FinancialsInvest BankingUSA
10MARA HoldingsMARA
4.56
FinancialsInvest BankingUSA
ⓘ Methodology

Sector, country and factor detail need a security match; the share of weight that matched is a member figure, so the rows without one appear here with those columns blank. Holdings as of 6/30/2026. Stock-level data as of 8/31/2026.

ConcentrationRef: Digital Assets Funds

Total Holdings25
Effective Holdings10.2
Top 10 Weight100.0%
Max Position28.8%
Top Sector (of Net Assets)58%
Top Country (of Net Assets)100%
ⓘ Methodology

Hover or click for definitions. Solid blue dots mark the fund: percentage rows on a 0–100% scale, holdings counts log-scaled across the clean-universe 5th–95th percentile range. The shaded band spans the fund's broad class (Digital Assets Funds) 25th–75th percentile, notched at the median. Holdings as of 6/30/2026.

Peer Group Rank

See All Peer Groups
Metric
MNRS
Grayscale Bitcoin Miners ETF
Peer Group
27 funds
Peer Asset Class
168 funds
YTD Return
---
1Y Return
---
3Y Return (Ann.)
---
5Y Return (Ann.)
---
Expense Ratiohigher = cheaper
0.59%0.75%700.69%61
ⓘ Methodology

Sparkline's own holdings-based peer taxonomy (27 share classes across 27 funds). One share class shown per fund — the exchange-listed class where the SEC series has one, else its longest-history class; percentile ranks and medians are computed against the same deduped set. Quartile chips fill the layer the fund's rank falls in (top layer = best quartile). Funds without a full return history rank only on the horizons they cover.

Similar Funds

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Nearest funds across the whole universe, by holdings and behavior — the peer taxonomy is ignored, so matches can come from any category.

Top Similarity94
Top Overlap
Top Active Tilt
Top Factor Match
FundPeer Group
1WGMI
Valkyrie Bitcoin Miners ETF
Digital Assets - Equity
Digital Assets - Equity
94---
2BKCH
Global X Blockchain ETF
Digital Assets - Equity
Digital Assets - Equity
61---
3NODE
Onchain Economy ETF
US Mid Growth
US Mid Growth
58---
4DAPP
VanEck Digital Transformation ETF
Digital Assets - Equity
Digital Assets - Equity
57---
5IBLC
iShares Blockchain and Tech ETF
Digital Assets - Equity
Digital Assets - Equity
56---
6STCE
Schwab Crypto Thematic ETF
Digital Assets - Equity
Digital Assets - Equity
56---
7DECO
State Street(R) Galaxy Digital Asset Ecosystem ETF
Digital Assets - Equity
Digital Assets - Equity
52---
8HECO
State Street(R) Galaxy Hedged Digital Asset Ecosystem ETF
Digital Assets - Equity
Digital Assets - Equity
52---
9TEKX
State Street(R) Galaxy Transformative Tech Accelerators ETF
US Mid Growth
US Mid Growth
49---
10BLCN
Siren NexGen Economy ETF
Allocation - Aggressive
Allocation - Aggressive
49---

The method columns are members-only — create a free account to see these figures.

ⓘ Methodology

Rows are the consensus top matches (a fund must place in at least two methods' top 250 to qualify, and matches missing more than one of this fund's own methods are not ranked — a row scored on fewer methods is a noisier estimate, not a better match). Click a method column to see that method's own top matches across the whole universe instead. Gray (N) beside each method's metric is that fund's rank in the whole universe on that method. Similarity is a percentile among match-grade fund pairs, 0–100 — 87 means the pair is closer than 87% of the strongest matches in the universe, averaged across the methods — so it compares across funds: index clones tie at the top with thousands of other clone pairs, so scores top out in the high 80s to 90s, while a one-of-a-kind fund's best match can score in the 30s. Rows are ordered by the Consensus Rank — the geometric mean of those universe ranks, shown in each cell's tooltip — and the score is held down the list to the level of the closest match above it, so equal scores mean equally close and the number reads as a floor. A dash means that method has no figure for the pair at all (one of them lacks the holdings or return history it needs). Hover a column header for each method's definition. The “Top…” strips above compare against every fund's own top match across the whole universe (they deliberately don't follow the Peer Universe selector); Top Similarity is simply the best match's score, with no reference band; the Holdings and Active Tilt strips carry no reference band because computing every fund's best book overlap would require a full holdings scan per fund — the dot stands alone on its natural 0–100% scale. One share class shown per fund, and a fund's own sister share classes are excluded from its Top-match strips. Holdings as of 6/30/2026. Consensus Rank is the geometric mean of the match's universe-wide rank across the methods (#1.0 = top match on every one). Holdings Overlap = common-holdings weight (sum of the minimum weight across shared positions, long only). Active Tilt Overlap = the same statistic on active weights (holdings minus the fund's own default benchmark, floored at 0), which strips the benchmark ballast that dominates raw overlap. Factor Similarity = 100 − a signature-weighted RMS gap across the composite factor scores (0–100 ranks within the clean equity fund universe), each axis weighted by how far this fund sits from the universe median of 50.