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Sparkline International Intangible Value ETF
ETF
DTAN
Payout Change
None
Price as of:
$24.5421
+0.17
+0.71%
primary theme
N/A
DTAN (ETF)
Sparkline International Intangible Value ETF
Payout Change
None
Price as of:
$24.5421
+0.17
+0.71%
primary theme
N/A
DTAN (ETF)
Sparkline International Intangible Value ETF
Payout Change
None
Price as of:
$24.5421
+0.17
+0.71%
primary theme
N/A
Name
As of 12/20/2024Price
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
Vitals
YTD Return
N/A
1 yr return
N/A
3 Yr Avg Return
N/A
5 Yr Avg Return
N/A
Net Assets
$2.8 M
Holdings in Top 10
N/A
52 WEEK LOW AND HIGH
$24.4
$24.32
$26.33
Expenses
OPERATING FEES
Expense Ratio 0.55%
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 12/20/2024Price
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
DTAN - Profile
Distributions
- YTD Total Return N/A
- 3 Yr Annualized Total Return N/A
- 5 Yr Annualized Total Return N/A
- Capital Gain Distribution Frequency N/A
- Net Income Ratio N/A
- Dividend Yield 0.0%
- Dividend Distribution Frequency None
Fund Details
-
Legal NameSparkline International Intangible Value ETF
-
Fund Family NameN/A
-
Inception DateSep 10, 2024
-
Shares OutstandingN/A
-
Share ClassN/A
-
CurrencyUSD
-
Domiciled CountryUS
Fund Description
The Fund is an actively-managed exchange-traded fund (“ETF”). The Fund will invest in equity securities of publicly listed non-U.S. companies that Sparkline Capital LP (the “Sub-Adviser”) believes are attractive relative to its proprietary measure of “intangible-augmented intrinsic value.” Under normal circumstances, the Fund invests at least 80% of its net assets (plus the amounts of any borrowings for investment purposes) in equity securities of publicly listed non-U.S. companies, including common stocks and depositary receipts evidencing ownership of common stocks, that satisfy the Sub-Adviser’s definition of 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 all publicly listed non-U.S. companies. In determining where a company is located, the Sub-Adviser will consider various factors, including the location of its headquarters, principal operations, revenue sources, principal trading market and legal organization. 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 may include common stocks of small-, mid- and large- capitalization companies, Real Estate Investment Trusts (“REITs”), and depository receipts representing the common stock of non-U.S. companies listed outside their domicile country. Depositary receipts, including ADRs and GDRs are certificates evidencing ownership of securities of a foreign issuer. The certificates are issued by depositary banks and the underlying securities are held in trust by a custodian bank or similar institution. Depositary receipts may be purchased on securities exchanges or directly from dealers. In addition, the Fund may invest in China A-shares (equity securities of companies listed in China). Although the Fund will not concentrate its investments in a particular industry, the Sub-Adviser anticipates that the Fund will hold a meaningful amount of stocks in the technology, industrials, healthcare, and consumer discretionary sectors.
The Fund’s international investments may provide exposure to developed and/or emerging markets. The Sub-Adviser has designated the following countries or regions as developed market: Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Hong Kong, Ireland, Israel, Italy, Japan, the Netherlands, New Zealand, Norway, Portugal, Singapore, Spain,
Sweden, Switzerland and the United Kingdom (the “U.K.”). The Sub-Adviser has designated the following countries as emerging: Brazil, Chile, China, Colombia, the Czech Republic, Egypt, Greece, Hungary, India, Indonesia, Kuwait, Malaysia, Mexico, Peru, the Philippines, Poland, Qatar, Saudi Arabia, South Africa, South Korea, Taiwan, Thailand, Turkey, and the United Arab Emirates. In determining a country to be a developed or emerging country, the Sub-Adviser may consider various factors, including but not limited to its economic development, its integration into the global financial system, and the classifications of independent organizations, such as the International Monetary Fund. To determine if a company is related to a developed or emerging market country, the Sub-Adviser will consider various factors, including the location of its headquarters, principal operations, revenue sources, principal trading market, and legal organization. The countries designated as developed or emerging markets will change from time to time. In addition, the countries in which the Fund actually holds investments will change from time to time.
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.
DTAN - Performance
Return Ranking - Trailing
| Period | DTAN Return | Category Return Low | Category Return High | Rank in Category (%) |
|---|---|---|---|---|
| YTD | N/A | N/A | N/A | N/A |
| 1 Yr | N/A | N/A | N/A | N/A |
| 3 Yr | N/A* | N/A | N/A | N/A |
| 5 Yr | N/A* | N/A | N/A | N/A |
| 10 Yr | N/A* | N/A | N/A | N/A |
* Annualized
Return Ranking - Calendar
| Period | DTAN Return | Category Return Low | Category Return High | Rank in Category (%) |
|---|---|---|---|---|
| 2023 | N/A | N/A | N/A | N/A |
| 2022 | N/A | N/A | N/A | N/A |
| 2021 | N/A | N/A | N/A | N/A |
| 2020 | N/A | N/A | N/A | N/A |
| 2019 | N/A | N/A | N/A | N/A |
Total Return Ranking - Trailing
| Period | DTAN Return | Category Return Low | Category Return High | Rank in Category (%) |
|---|---|---|---|---|
| YTD | N/A | N/A | N/A | N/A |
| 1 Yr | N/A | N/A | N/A | N/A |
| 3 Yr | N/A* | N/A | N/A | N/A |
| 5 Yr | N/A* | N/A | N/A | N/A |
| 10 Yr | N/A* | N/A | N/A | N/A |
* Annualized
Total Return Ranking - Calendar
| Period | DTAN Return | Category Return Low | Category Return High | Rank in Category (%) |
|---|---|---|---|---|
| 2023 | N/A | N/A | N/A | N/A |
| 2022 | N/A | N/A | N/A | N/A |
| 2021 | N/A | N/A | N/A | N/A |
| 2020 | N/A | N/A | N/A | N/A |
| 2019 | N/A | N/A | N/A | N/A |
DTAN - Holdings
Concentration Analysis
| DTAN | Category Low | Category High | DTAN % Rank | |
|---|---|---|---|---|
| Net Assets | 2.8 M | N/A | N/A | N/A |
| Number of Holdings | N/A | N/A | N/A | N/A |
| Net Assets in Top 10 | N/A | N/A | N/A | N/A |
| Weighting of Top 10 | N/A | N/A | N/A | N/A |
Top 10 Holdings
Asset Allocation
| Weighting | Return Low | Return High | DTAN % Rank | |
|---|---|---|---|---|
| Stocks | 0.00% | N/A | N/A | N/A |
| Preferred Stocks | 0.00% | N/A | N/A | N/A |
| Other | 0.00% | N/A | N/A | N/A |
| Convertible Bonds | 0.00% | N/A | N/A | N/A |
| Cash | 0.00% | N/A | N/A | N/A |
| Bonds | 0.00% | N/A | N/A | N/A |
DTAN - Expenses
Operational Fees
| DTAN Fees (% of AUM) | Category Return Low | Category Return High | Rank in Category (%) | |
|---|---|---|---|---|
| Expense Ratio | 0.55% | N/A | N/A | N/A |
| Management Fee | 0.55% | N/A | N/A | N/A |
| 12b-1 Fee | N/A | N/A | N/A | N/A |
| Administrative Fee | N/A | N/A | N/A | N/A |
Sales Fees
| DTAN Fees (% of AUM) | Category Return Low | Category Return High | Rank in Category (%) | |
|---|---|---|---|---|
| Front Load | N/A | N/A | N/A | N/A |
| Deferred Load | N/A | N/A | N/A | N/A |
Trading Fees
| DTAN Fees (% of AUM) | Category Return Low | Category Return High | Rank in Category (%) | |
|---|---|---|---|---|
| Max Redemption Fee | N/A | N/A | N/A | 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.
| DTAN Fees (% of AUM) | Category Return Low | Category Return High | Rank in Category (%) | |
|---|---|---|---|---|
| Turnover | N/A | N/A | N/A | N/A |
DTAN - Distributions
Dividend Yield Analysis
| DTAN | Category Low | Category High | DTAN % Rank | |
|---|---|---|---|---|
| Dividend Yield | 0.00% | N/A | N/A | N/A |
Dividend Distribution Analysis
| DTAN | Category Low | Category High | Category Mod | |
|---|---|---|---|---|
| Dividend Distribution Frequency | None |
Net Income Ratio Analysis
| DTAN | Category Low | Category High | DTAN % Rank | |
|---|---|---|---|---|
| Net Income Ratio | N/A | N/A | N/A | N/A |
Capital Gain Distribution Analysis
| DTAN | Category Low | Category High | Capital Mode | |
|---|---|---|---|---|
| Capital Gain Distribution Frequency |
