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Name

As of 06/01/2026

Price

Aum/Mkt Cap

YIELD

Annualized forward dividend yield. Multiplies the most recent dividend payout amount by its frequency and divides by the previous close price.

Exp Ratio

Expense ratio is the fund’s total annual operating expenses, including management fees, distribution fees, and other expenses, expressed as a percentage of average net assets.

Watchlist

Sparkline Intangible Value ETF

ITAN | Active ETF

$43.00

$78.6 M

0.89%

$0.38

0.50%

Vitals

YTD Return

16.4%

1 yr return

41.1%

3 Yr Avg Return

23.9%

5 Yr Avg Return

N/A

Net Assets

$78.6 M

Holdings in Top 10

20.0%

52 WEEK LOW AND HIGH

$42.7
$31.00
$43.00

Expenses

OPERATING FEES

Expense Ratio 0.50%

SALES FEES

Front Load N/A

Deferred Load N/A

TRADING FEES

Turnover N/A

Redemption Fee N/A

Min Investment

Standard (Taxable)

N/A

IRA

N/A

Fund Classification

Fund Type

Exchange Traded Fund

Name

As of 06/01/2026

Price

Aum/Mkt Cap

YIELD

Annualized forward dividend yield. Multiplies the most recent dividend payout amount by its frequency and divides by the previous close price.

Exp Ratio

Expense ratio is the fund’s total annual operating expenses, including management fees, distribution fees, and other expenses, expressed as a percentage of average net assets.

Watchlist

Sparkline Intangible Value ETF

ITAN | Active ETF

$43.00

$78.6 M

0.89%

$0.38

0.50%

ITAN - Profile

Distributions

  • YTD Total Return 16.4%
  • 3 Yr Annualized Total Return 23.9%
  • 5 Yr Annualized Total Return N/A
  • Capital Gain Distribution Frequency N/A
  • Net Income Ratio N/A
DIVIDENDS
  • Dividend Yield 0.9%
  • Dividend Distribution Frequency Quarterly

Fund Details

  • Legal Name
    Sparkline Intangible Value ETF
  • Fund Family Name
    N/A
  • Inception Date
    Jun 28, 2021
  • Shares Outstanding
    N/A
  • Share Class
    N/A
  • Currency
    USD
  • Domiciled Country
    US
  • Manager
    Christopher Kai Wu

Fund Description

The Fund is an actively-managed exchange-traded fund (“ETF”). The Fund will invest in U.S.-listed equity securities that Sparkline Capital LP (the “Sub-Adviser”) believes are attractive relative to its proprietary measure of “intangible-augmented intrinsic value.”
Unlike most traditional quantitative value strategies, the Sub-Adviser’s definition of intrinsic value (i.e. intangible-augmented intrinsic value) includes an assessment of both tangible assets and intangible value. Including a measurement of a company’s intangible value is a crucial part of the Sub-Adviser’s investment process. The Sub-Adviser believes intangible value is growing increasingly important as the economy shifts from industrial to information-based. The Sub-Adviser focuses on four pillars of intangible value: (1) human capital, (2) brand equity, (3) intellectual property, and (4) network effects, each of which are described more below.
1.Human capital: Human capital is the value embodied by human beings. In the modern economy, the ability to attract and retain top talent can be an important source of competitive advantage, as are company cultures that motivate and nurture workers.
2.Brand equity: Well-known brand names are often able to generate sales simply due to strong consumer recognition and loyalty. Companies may invest considerable resources in building their brands, which can constitute a large component of their market value.
3.Intellectual property: Intellectual property encompasses creations of the human intellect. It includes both legally-protected patents and proprietary trade secrets. As science and technology plays a larger role in human society, intellectual property has increasingly become the primary source of value for many companies.
4.Network effects: Network effects are a phenomenon by which users of a product or service derive incremental value from the addition of other users to the network. This can make it challenging for new entrants to unseat firms with dominant
market positions. As globalization and the internet increase the potential scale of networks, network effects are becoming an important type of “moat.”
The Sub-Adviser employs a proprietary quantitative methodology to determine an estimated value of the foregoing four pillars for each company as well as to determine an estimated value of each company’s tangible assets – the fifth pillar. The assessment of a company’s tangible and intangible value together determine its intangible-augmented intrinsic value. The Sub-Adviser’s valuation process does not necessarily favor a company’s intangible value over its tangible value but due to four of the five pillars considered for determining a company’s value involving intangible value, it is generally expected that intangible will have a higher weight than tangible value. However, the weighting of individual pillars is expected to fluctuate over time.
The Sub-Adviser uses, among other sources, companies’ public accounting disclosures to analyze tangible assets. However, the Sub-Adviser has concluded that most companies’ accounting disclosures omit or give only cursory mention to their intangible value. The technical accounting definition of “intangible assets” is quite specific and captures only a narrow subset of the Sub-Adviser’s broader concept of intangible value. As a result, a key component of the Sub-Adviser’s process is its use of alternative data to measure intangible value. Alternative data refers to non-traditional data sources beyond conventional financial, accounting and stock price information. Examples of alternative data may include the narratives in corporate reports, patent and trademark grants, employee reviews, and social media. These examples are for illustrative purposes only; the Fund may choose to use some or none of these datasets, as well as other datasets not listed above. In general, such metrics are quite varied because each intangible pillar must be measured differently.
Because alternative data is often unstructured (e.g., text, images, audio) and very large, the Sub-Adviser uses natural language processing (NLP) (a form of machine learning) in addition to traditional quantitative investment techniques to incorporate the data into its investment process. NLP is specifically designed to deal with unstructured text. The Sub-Adviser generally uses a combination of third-party and open-source NLP frameworks, which are widely used and vetted, and adapts them to the unique use case of investing. Open-source NLP frameworks are publicly available code libraries that allow users to freely perform standard NLP tasks, such as named entity recognition, sentiment analysis, and summarization. Third-party NLP frameworks refer to services that, while not fully transparent or free of cost, are accessible to public users to perform NLP tasks such as those mentioned above.
This investment process is applied to a starting investment universe of the approximately largest 1,000 publicly listed U.S. securities (by market capitalization). The Sub-Adviser may remove companies from the universe if the Sub-Adviser determines they do not have a meaningful quantity of intangible value. For each company in the investment universe, the Sub-Adviser considers multiple metrics for the company’s attractiveness according to each of the five pillars, and then averages those metrics to produce a score for each of the five pillars. This is because the Sub-Adviser believes that no one data source or metric is infallible and that by combining many metrics, a better result can be obtained. Finally, the composite score is created by summing across the five pillars. The Fund will then generally seek to hold the securities of the companies with the highest total scores. In determining the weighting of each stock, the Sub-Adviser may take into account various factors, including but not limited to value, market capitalization and liquidity.
The Sub-Adviser is not constrained by the number of portfolio holdings, except that the Fund will generally hold at least 50 securities. The Fund’s investments will primarily include common stocks and may include investments in Real Estate Investment Trusts (REITs). Although the Fund will not concentrate its investments in a particular industry, the Sub-Adviser expects that Fund will have significant exposure to companies in the information technology sector.
The Sub-Adviser will seek to continually improve its valuation models used for the Fund as new datasets, methodologies and research become available. The Sub-Adviser will also employ active risk management techniques. As a result and because the Fund seeks to be fully invested at all times, the Sub-Adviser may recommend changes to the Fund’s individual positions during dynamic market conditions.
Read More

ITAN - Performance

Return Ranking - Trailing

Period ITAN Return Category Return Low Category Return High Rank in Category (%)
YTD 16.4% -51.8% 22.1% 95.14%
1 Yr 41.1% -58.9% 46.9% N/A
3 Yr 23.9%* -25.7% 197.6% N/A
5 Yr N/A* -29.1% 93.8% N/A
10 Yr N/A* -17.2% 37.0% N/A

* Annualized

Return Ranking - Calendar

Period ITAN Return Category Return Low Category Return High Rank in Category (%)
2025 19.2% -69.4% 53.7% N/A
2024 16.4% -94.0% 152.6% N/A
2023 33.1% -13.9% 183.6% N/A
2022 -24.8% -18.2% 8.9% N/A
2021 N/A -80.2% 35.2% N/A

Total Return Ranking - Trailing

Period ITAN Return Category Return Low Category Return High Rank in Category (%)
YTD 16.4% -97.2% 22.1% 98.93%
1 Yr 41.1% -58.9% 67.6% N/A
3 Yr 23.9%* -25.7% 197.6% N/A
5 Yr N/A* -28.1% 93.8% N/A
10 Yr N/A* -11.8% 37.0% N/A

* Annualized

Total Return Ranking - Calendar

Period ITAN Return Category Return Low Category Return High Rank in Category (%)
2025 20.1% -69.4% 53.7% N/A
2024 17.8% -94.0% 152.6% N/A
2023 34.6% -13.9% 183.6% N/A
2022 -24.3% -12.8% 8.9% N/A
2021 N/A -60.0% 35.2% N/A

ITAN - Holdings

Concentration Analysis

ITAN Category Low Category High ITAN % Rank
Net Assets 78.6 M 177 K 1.21 T 98.67%
Number of Holdings 158 2 4154 42.19%
Net Assets in Top 10 14.6 M 1.74 K 270 B 98.34%
Weighting of Top 10 19.97% 1.8% 100.0% 83.69%

Top 10 Holdings

  1. Amazon.com Inc 3.94%
  2. Alphabet Inc 2.26%
  3. Alphabet Inc 2.25%
  4. ATT Inc 1.71%
  5. Cisco Systems Inc 1.70%
  6. Intel Corp 1.70%
  7. International Business Machines Corp 1.64%
  8. Merck Co Inc 1.62%
  9. Pfizer Inc 1.59%
  10. Micron Technology Inc 1.56%

Asset Allocation

Weighting Return Low Return High ITAN % Rank
Stocks 		99.45% 0.00% 130.24% 29.11%
Cash 		0.55% -102.29% 100.00% 69.85%
Preferred Stocks 		0.00% 0.00% 2.23% 57.54%
Other 		0.00% -13.91% 134.98% 57.75%
Convertible Bonds 		0.00% 0.00% 5.54% 55.53%
Bonds 		0.00% -0.04% 95.81% 55.53%

Stock Sector Breakdown

Weighting Return Low Return High ITAN % Rank
Utilities 		0.00% 0.00% 25.44% 93.14%
Technology 		0.00% 0.00% 48.94% 0.21%
Real Estate 		0.00% 0.00% 37.52% 81.79%
Industrials 		0.00% 0.00% 29.90% 86.62%
Healthcare 		0.00% 0.00% 60.70% 91.39%
Financial Services 		0.00% 0.00% 55.59% 87.18%
Energy 		0.00% 0.00% 41.64% 80.39%
Communication Services 		0.00% 0.00% 27.94% 6.86%
Consumer Defense 		0.00% 0.00% 49.14% 96.71%
Consumer Cyclical 		0.00% 0.00% 50.47% 64.92%
Basic Materials 		0.00% 0.00% 26.10% 89.85%

Stock Geographic Breakdown

Weighting Return Low Return High ITAN % Rank
US 		99.45% 0.00% 127.77% 31.12%
Non US 		0.00% 0.00% 33.51% 54.56%

ITAN - Expenses

Operational Fees

ITAN Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Expense Ratio 0.50% 0.01% 2.95% 69.30%
Management Fee 0.50% 0.00% 2.00% 48.00%
12b-1 Fee N/A 0.00% 1.00% N/A
Administrative Fee N/A 0.00% 0.85% N/A

Sales Fees

ITAN Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Front Load N/A 0.00% 5.75% N/A
Deferred Load N/A 1.00% 5.00% N/A

Trading Fees

ITAN Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Max Redemption Fee N/A 0.25% 2.00% N/A

Related Fees

Turnover provides investors a proxy for the trading fees incurred by mutual fund managers who frequently adjust position allocations. Higher turnover means higher trading fees.

ITAN Fees (% of AUM) Category Return Low Category Return High Rank in Category (%)
Turnover N/A 0.00% 496.00% N/A

ITAN - Distributions

Dividend Yield Analysis

ITAN Category Low Category High ITAN % Rank
Dividend Yield 0.89% 0.00% 19.15% 35.70%

Dividend Distribution Analysis

ITAN Category Low Category High Category Mod
Dividend Distribution Frequency Quarterly Annually Quarterly Annually

Net Income Ratio Analysis

ITAN Category Low Category High ITAN % Rank
Net Income Ratio N/A -54.00% 6.06% N/A

Capital Gain Distribution Analysis

ITAN Category Low Category High Capital Mode
Capital Gain Distribution Frequency Annually Annually Annually

Distributions History

View More +

ITAN - Fund Manager Analysis

Managers

Christopher Kai Wu

Start Date

Tenure

Tenure Rank

Jun 28, 2021

0.92

0.9%

Mr. Christopher Tsong-Kai (Kai) Wu is the founder and Chief Investment Officer of Sparkline Capital, an SEC-registered investment management firm applying machine learning and computing to seek to uncover alpha (which is excess return above that of a benchmark) in large, unstructured data sets. Prior to Sparkline, Kai co-founded and co-managed Kaleidoscope Capital, a quantitative hedge fund in Boston. With one other partner, he grew Kaleidoscope to $350 million in assets from institutional investors. Kai jointly managed all aspects of the company, including technology, investments, operations, trading, investor relations, and recruiting. Previously, Kai worked at GMO, where he was a member of Jeremy Grantham’s $40 billion asset allocation team. He also worked closely with the firm's equity and macro investment teams in Boston, San Francisco, London, and Sydney. Kai graduated from Harvard College Magna Cum Laude and Phi Beta Kappa.

Brandon Koepke

Start Date

Tenure

Tenure Rank

Jun 28, 2021

0.92

0.9%

Mr. Brandon Koepke serves as Chief Technology Officer & Portfolio Manager. Mr. Koepke has a BSc in Computer Science specializing in Software Engineering at the University of Calgary and a BComm in Finance from the Haskayne School of Business.

Tenure Analysis

Category Low Category High Category Average Category Mode
0.04 39.02 7.17 2.42

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