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Critical Facts You Need to Know About Preferred Stocks
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Intelligent Livermore ETF
ETF
LIVR
Payout Change
None
Price as of:
$27.2059
+0.28
+1.03%
primary theme
N/A
LIVR (ETF)
Intelligent Livermore ETF
Payout Change
None
Price as of:
$27.2059
+0.28
+1.03%
primary theme
N/A
LIVR (ETF)
Intelligent Livermore ETF
Payout Change
None
Price as of:
$27.2059
+0.28
+1.03%
primary theme
N/A
Name
As of 10/04/2024Price
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
$13.7 M
Holdings in Top 10
N/A
52 WEEK LOW AND HIGH
$26.9
$25.05
$27.21
Expenses
OPERATING FEES
Expense Ratio 0.69%
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 10/04/2024Price
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
LIVR - 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 NameIntelligent Livermore ETF
-
Fund Family NameN/A
-
Inception DateSep 18, 2024
-
Shares OutstandingN/A
-
Share ClassN/A
-
CurrencyUSD
-
Domiciled CountryUS
Fund Description
The Fund uses Intelligent Alpha, LLC’s (the “Sub-Adviser”) proprietary artificial intelligence-powered stock selection strategy to create an intelligent equal weight portfolio of global large cap stocks with over $1 billion in market capitalization (the “Investment Universe”). The securities selected will be based on the major trading trends inspired by the greatest traders in the world. The portfolio will generally hold between 60-90 stocks with no single position exceeding 10% of the portfolio. The securities held in the portfolio will be a byproduct of the themes identified by artificial intelligence (“AI”), as described in more detail below. This is collectively referred to herein as the “Intended Strategy.”
The Sub-Adviser’s AI-powered stock selection process uses three steps:
Step 1:Human initiation. A human analyst (the “Analyst”) establishes the Intended Strategy for the underlying portfolio. including the target investment universe, the portfolio size, the intended concentration level (e.g., maximum security weighting), and any specific factors or themes to be highlighted in the portfolio (e.g. dividends, quality). The Analyst uses a template to collect historical data and forward estimates of certain data such as revenue, earnings, free cash flow, etc. from a third-party data source (e.g., Factset). The Analyst may add certain additional data that is relevant to the intended strategy (e.g., data on revenue or earning growth for a growth-oriented strategy). This information is to be used in quantitative analysis by the AI. Finally, the Analyst defines a philosophy for the AI to use for qualitative analysis of stocks in the Investment Universe. The Philosophy for the Fund is built on the ideas of the world’s greatest traders with an eye toward momentum in price, strong fundamentals and strong macro trends (the “Philosophy”). The Philosophy defines how the AI selects securities to represent the themes of the major trends, sectors, etc. For example, the AI may identify a trend to invest in European stocks. The Philosophy may instruct the AI to optimize for European stocks that have certain characteristics, such as strong business moats, stock momentum, high free cash flow yields, etc. The Philosophy is part of the instruction set that is provided by Analyst to the AI as described in subsequent steps.
A large language model, which is a type of AI algorithm that uses deep learning techniques and massively large data sets to understand, summarize, generate and predict new content, is consulted to identify 4-6 major trading trends inspired by the greatest traders in the world. The Analyst will define the list of famous traders and investors for the AI by reviewing long-term (5 years or greater) track records of famous investors as compared to broad market benchmarks, which will vary based on the nature of the trading trend(s) observed. A major trading trend is a clearly defined and articulated trading view expressed by the famous trader. The major trading trends identified by the AI define the thematic, sector, and other broad-based exposures that the AI believes the portfolio should represent as inspired by the trading trends identified for the identified greatest traders. The Sub-Adviser instructs the AI to optimize for trading trends that will be in favor for at least 6-24 months.
The AI will analyze numerous information sources (such as 13F filings, public statements, and interviews) relating to the identified traders and trends when conducting its overall analysis. Form 13F is a quarterly report filed by institutional investment managers to disclose their U.S. equity holdings to the SEC. Based on the identified trends (e.g., short U.S. small cap securities) the AI may select, and the Analyst may include, at the Analyst’s discretion, leveraged or inverse leveraged index ETFs to represent the trend as part of the portfolio, such as when the AI recommends a trade that involves a broad basket of equity securities. Leveraged ETFs seek to provide investment results that match a multiple of the performance of an underlying index (e.g., two times the performance). Inverse Leveraged ETFs seek to provide investment results that match a negative multiple of the performance of an underlying index.
After identifying the major trading trends, the AI Models (defined below) are asked to weight the trends for use in a portfolio. Next, given the trading trends identified, the Analyst curates a proprietary universe of global equity securities with a market capitalization of over $1 billion at the time of the AI’s review. Global equity securities are common stocks, including American Depositary Receipts (“ADRs”), European Depositary Receipts (“EDRs”) and Global Depositary Receipts (“GDRs”) of companies that are located throughout the world and that are listed on a regulated stock exchange. Depositary Receipts represent shares in a foreign company that are traded on a local stock exchange. Given the Investment Universe, a selection set of companies in the universe with sufficient data about revenue growth, earnings, free cash flow, price to earnings ratio, beta and other financial metrics are collected by the Analyst. Sufficient data is generally considered to be 50% of the data entries in the overall dataset that is obtained by the Analyst as described in Step 1.
Step 2:AI portfolio creation. The Analyst gathers the details, information and Philosophy set forth in Step 1 and translates them into an instruction set to be submitted to three large language models (the “AI Models”) for portfolio creation. Each AI Model is similarly instructed to review the data and instructions to create a portfolio of up to 20-30 stocks each, including weights for each position. The AI Models are instructed to limit the maximum weight of any one holding to 10%. Each AI Model has its own discretion in creating its portfolio, which is reviewed by the Analyst in Step 3. The AI Models represent the Fund’s AI investment committee, whereby the AI Models provide their independently created portfolios based on each AI Model’s independent evaluation of the instructions provided to it.
The submission of the data and Philosophy initiates the process for each AI Model to review the instruction set and create a portfolio. The Analyst works with each AI Model to create their respective portfolios. The portfolios are each adjusted for the relevant thematic weights of the trends established in Step 1.
Step 3:Analyst portfolio review. After the three AI Models create their respective portfolios, the Analyst reviews each portfolio to ensure that it adheres to the Intended Strategy and any applicable regulatory requirements. Any stocks that are included in the AI Models that do not fit the Investment Universe or Intended Strategy are excluded with the weight of any removed stocks redistributed pro rata across the particular AI portfolio. Determination of whether a stock fits the Fund’s Intended Strategy is at the final discretion of the Analyst. Any ETFs that are selected by the AI will be the most liquid representation of the trend identified by the AI Models and will be included in the in the portfolio at the Analyst’s discretion. For example, if a trend suggests a long position in a small cap Index, the Analyst will select the most liquid ETF that meets the criteria. There is no limit on how much of the portfolio can be represented by ETFs.
The Analyst then aggregates the three weighted AI-powered portfolios into a single portfolio where each of the individual portfolios created by the AI Models make up one-third of the Fund’s overall portfolio. Each position in the portfolio is equal weighted.
A portfolio review is initiated, including the list of great investors, by the Analyst via the same process described above. The frequency and timing of portfolio review is at the discretion of the Analyst but generally a portfolio review will be conducted on a quarterly basis. The new resultant portfolio at the end of the process will replace the existing portfolio at the time of the review.
The Fund typically rebalances quarterly in February, May, August, and November; however, the Analyst has discretion to review the portfolio more or less frequently.
The Fund is expected to have high portfolio turnover based on historical testing of the Fund’s investment strategy. During the portfolio review process the AI may remove or add stocks to the portfolio or change the weight of stocks already in the portfolio.
LIVR - Performance
Return Ranking - Trailing
| Period | LIVR 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 | LIVR 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 | LIVR 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 | LIVR 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 |
LIVR - Holdings
Concentration Analysis
| LIVR | Category Low | Category High | LIVR % Rank | |
|---|---|---|---|---|
| Net Assets | 13.7 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 | LIVR % 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 |
LIVR - Expenses
Operational Fees
| LIVR Fees (% of AUM) | Category Return Low | Category Return High | Rank in Category (%) | |
|---|---|---|---|---|
| Expense Ratio | 0.69% | N/A | N/A | N/A |
| Management Fee | 0.69% | 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
| LIVR 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
| LIVR 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.
| LIVR Fees (% of AUM) | Category Return Low | Category Return High | Rank in Category (%) | |
|---|---|---|---|---|
| Turnover | N/A | N/A | N/A | N/A |
LIVR - Distributions
Dividend Yield Analysis
| LIVR | Category Low | Category High | LIVR % Rank | |
|---|---|---|---|---|
| Dividend Yield | 0.00% | N/A | N/A | N/A |
Dividend Distribution Analysis
| LIVR | Category Low | Category High | Category Mod | |
|---|---|---|---|---|
| Dividend Distribution Frequency | None |
Net Income Ratio Analysis
| LIVR | Category Low | Category High | LIVR % Rank | |
|---|---|---|---|---|
| Net Income Ratio | N/A | N/A | N/A | N/A |
Capital Gain Distribution Analysis
| LIVR | Category Low | Category High | Capital Mode | |
|---|---|---|---|---|
| Capital Gain Distribution Frequency |
