0001839882-21-018412.txt : 20220210 0001839882-21-018412.hdr.sgml : 20220210 20211203170420 ACCESSION NUMBER: 0001839882-21-018412 CONFORMED SUBMISSION TYPE: CORRESP PUBLIC DOCUMENT COUNT: 3 FILED AS OF DATE: 20211203 FILER: COMPANY DATA: COMPANY CONFORMED NAME: ETF Opportunities Trust CENTRAL INDEX KEY: 0001771146 IRS NUMBER: 000000000 STATE OF INCORPORATION: DE FISCAL YEAR END: 0731 FILING VALUES: FORM TYPE: CORRESP BUSINESS ADDRESS: STREET 1: 8370 STONY POINT PARKWAY, SUITE 205 CITY: RICHMOND STATE: VA ZIP: 23235 BUSINESS PHONE: 804-267-7400 MAIL ADDRESS: STREET 1: 8370 STONY POINT PARKWAY, SUITE 205 CITY: RICHMOND STATE: VA ZIP: 23235 CORRESP 1 filename1.htm

 

December 3, 2021

 

 

Ms. Karen Rossotto

U.S. Securities and Exchange Commission

100 F Street, N.E.

Washington, D.C. 20549

 

Re:ETF Opportunities Trust (File Nos. 333-234544 and 811-23439)
UBC Algorithmic Fundamentals ETF

 

 

Dear Ms. Rossotto:

 

This letter provides the responses of ETF Opportunities Trust (the “Trust”) to the comments of the staff of the U.S. Securities and Exchange Commission (the “Commission”) that you recently provided. The comments addressed Post-Effective Amendment (“PEA”) No. 18 to the registration statement of the Trust, which was filed on October 8, 2021 under Rule 485(a) of the Securities Act of 1933, as amended. The PEA was filed to register a new series of the Trust, the UBC Algorithmic Fundamentals ETF (the “Fund”). For your convenience, I have summarized the comments in this letter and provided the Trust’s response below each comment.

 

PROSPECTUS

 

Fees and Expenses

 

1.Comment: Footnote 2 of the fee table references acquired fund fees and expenses (“AFFE”), but the fee table does not list AFFE as a separate line item. If AFFE is less than 1 basis point, remove the reference to AFFE in Footnote 2 or if AFFE is greater than 1 basis point disclose AFFE in the fee table as required by Form N-1A.

 

 Response: The Fund has removed the reference to AFFE in Footnote 2. See below for the revised fee table.

 

Fees and Expenses of the Fund

This table describes the fees and expenses that you may pay if you buy, hold and sell shares of the Fund. Investors purchasing shares on a national securities exchange, national securities association, or over-the-counter trading system where shares may trade from time to time (each, a “Secondary Market”) may be subject to customary brokerage commissions charged by their broker that are not reflected in the table and example set forth below.

Annual Fund Operating Expenses

(expenses that you pay each year as a percentage of the value of your investment)

Management Fee(1)  0.75%
Other Expenses(2) 0.00%
Total Annual Fund Operating Expenses 0.75%
(1)Under the Investment Advisory Agreement, Ultra Blue Capital, LLC (the “Adviser”), at its own expense and without reimbursement from the Fund, pays all of the expenses of the Fund, excluding the advisory fees, interest expenses, taxes, acquired fund fees and expenses, brokerage commissions and any other portfolio transaction related expenses and fees arising out of transactions effected on behalf of the Fund, credit facility fees and expenses, including interest expenses, and litigation and indemnification expenses and other extraordinary expenses not incurred in the ordinary course of the Fund’s business.
(2)Other Expenses are estimated for the Fund’s initial fiscal year.

 

 

 

 

 

JOHN H. LIVELY ● MANAGING PARTNER

11300 Tomahawk Creek Pkwy ● Ste. 310 ● Leawood, KS 66211 ● p: 913.660.0778 ● c: 913.523.6112

Practus, LLP ● John.Lively@Practus.com ● Practus.com

 

 

 

Principal Investment Strategies

 

2.Comment: Your responses to the following questions should be in correspondence only. Regarding the Fund’s AI algorithms, please elaborate on how the proprietary investment model works. For example, please tell us:

 

What types and quantities of data was used to train the model?

 

Response: The type of data we use are numerical. Data volume/quantity varies based on the source of data deployed. The current size of the Adviser’s databases is around 10 terabytes of which about 50% is currently used for this Fund.

 

What data does the Fund actually use to make decisions now?

 

Response: Examples for type of data used for selection and weighting holdings include historical financial statements (sourced from companies’ 10-Ks, 10-Qs), historical price action, investor sentiments, leading economic indicators, and other alternative data-sets specific to industries of interest.

 

How does the fund determine the data is timely, actionable, and accurate?

 

Response: The Adviser uses mostly factual data in the prediction platform acquired from reliable and high-quality sources, such as Factset. In addition, the Adviser preforms algorithmic, comprehensive, and continuous testing on the ingested data to insure timely updates, data integrity, and consistency. The Adviser has automated processes in place to notify involved personnel when data issues are detected.

 

How does the adviser monitor the model to ensure its continued validity over time?

 

Response: The Adviser continuously runs comparison between the back-tested results and live Fund performance. If the Fund’s performance falls below the Adviser’s expected back-tested performance by a margin of error, then the Adviser’s data scientist team retunes the model to satisfy expectations. In addition, the Adviser’s data science team constantly evolves its algorithms with the goal of improving the Fund’s performance.

 

 

2

 

 

Who pays for the data used by the model and, if the fund, is there a risk that the data necessary to support the strategy may become increasingly expensive or so widely used that it becomes less useful?

 

Response: The Fund does not pay directly for the data. Big Data Federation, Ultra Blue Capital’s parent company, the adviser to the Fund, pays for the data subscriptions. Overall data cost is a small percentage of the Adviser’s total operating cost (currently around 4%). The Adviser has also negotiated a multi-year agreement with the paid data providers (e.g., Factset) in order to better control costs. In addition, the Adviser relies on widely available public data sources which make the risk of data source cost increase relatively insignificant. These public data sources include Federal Reserve Economic Data, United States Census Bureau, and Bureau of Economic Analysis, and Bureau of Labor Statistics, Transportation Security Administration, and United States Department of Treasury.

 

The Adviser believes its key differentiation is not data used, the firm’s value-add is around the mass aggregation of data and the meanings and predictions it extracts from that data using the firm’s proprietary algorithms.

 

3.Comment: In the Principal Risk – Quantitative Security Selection Model Risk, a reference is made to back testing. Explain what back testing is and how it impacts the development of the proprietary strategies.

 

Response: Back testing is the process of assessing how well a trading strategy or analytical method could perform, based on historical data. It is a key component in developing the Fund’s strategy and allows the Adviser to test how a particular strategy has performed in certain market conditions.

 

The Fund has added the following disclosure to the Quantitative Security Selection Model Risk.

 

“Back testing is the process of assessing how well a trading/investment strategy or analytical method could perform, based on historical data. It is a key component of the Fund’s strategy and allows the Adviser to test how a particular strategy has performed in certain market conditions, but the use of back testing does not guarantee that such performance will be repeated.”

 

4.Comment: In the Principal Investment Strategies discussion explain what types or kind of data are used by the model?

 

Response: Examples for types of data used for selection and weighting holdings include historical financial statements (sourced from companies’ 10-Ks, 10-Qs), historical price action, investor sentiments, leading economic indicators, and other alternative data set specific to industries of interest – which are all numerical data.

 

 

 

3

 

The Fund has added the following disclosure to the Principal Investment Strategies discussion.

 

“These fundamental predictions are underpinned by a highly scalable proprietary backend system which processes factual and market datasets available in the public domain. These datasets include historical financial statements, historical price action for companies, investor sentiment, and leading economic indicators, such as gross domestic product, purchasing manager’s index and consumer purchasing index.

 

5.Comment: In the Principal Investment Strategies discussion, consider removing the word “achieve” because it appears to be promissory from the following sentence:

 

“The Fund’s systematic investment process is based on rigorous back-testing of proprietary and evolving data-driven strategies and is designed to allow the Fund to achieve attractive risk-adjusted returns.”

 

Response: The Fund has elected not to make a change because it believes by including “is designed” in the sentence it alerts investors that a possibility exists the design may not work.

 

6.Comment: In the Principal Investment Strategies discussion, explain what “risk-adjusted” means.

 

Response: The Fund has revised the disclosure as follows:

 

“The Fund’s systematic investment process is based on rigorous back-testing of proprietary and evolving data-driven strategies and is designed to allow the Fund to achieve attractive risk-adjusted returns (i.e., returns made relative to the amount of risk taken).”

 

7.Comment: In the Principal Investment Strategies discussion, explain what “predicted expanding fundamentals” means.

 

Response: The Fund has revised the disclosure as follows:

 

“In making investment decisions, the Adviser’s algorithms are trained to invest in profitable companies with predicted expanding fundamentals (i.e., companies demonstrating such things as improved cash flows and earnings per share, reasonable price-to-earnings ratio, and improving price-to-earnings growth and dividend yield).”

 

8.Comment: Revise the third paragraph in the Principal Investment Strategies discussion to explain in Plain English how the Fund selects its investments.

 

“In making investment decisions, the Adviser’s algorithms are trained to invest in currently profitable companies with predicted expanding fundamentals. In predicting fundamentals, the Adviser uses its proprietary prediction platform which employs a proprietary machine-learning algorithm that runs multiple independent models in parallel. This proprietary prediction platform generates a wide range of short- to mid-term fundamental predictions for economic metrics (including core inflation, real GDP, and nonfarm payrolls), industries, and large cap companies’ fundamentals (including financials and operating metrics) across multiple sectors. These fundamental predictions are underpinned by a highly scalable proprietary database which processes billions of data points daily from factual and market datasets in the public domain. The prediction platform refreshes daily and selects companies with strong and expanding fundamentals, and actively benchmarks them against industry peers and the broader market. The Adviser believes that by running multiple independent models it serves as a cross-check for the predictions generated by the models.”

 

 

4

 

Response: The Fund has revised the disclosure as follows:

 

In making investment decisions, the Adviser’s algorithms are trained to invest in profitable companies with predicted expanding fundamentals (i.e., companies demonstrating such things as improved cash flows and earnings per share, reasonable price-to-earnings ratio, and improving price-to-earnings growth and dividend yield). In predicting fundamentals, the Adviser uses its proprietary machine-learnings forecasting algorithms. The proprietary forecasting algorithms generate a wide range of short- to mid-term fundamental predictions for economic metrics, industries, and companies’ fundamentals (including financials and operating metrics) across multiple sectors. These fundamental predictions are underpinned by a highly scalable proprietary backend system which processes factual and market datasets available in the public domain. These datasets include historical financial statements, historical price action for companies, investor sentiment, and leading economic indicators, such as gross domestic product, purchasing manager’s index and consumer purchasing index. The Fund’s portfolio will be actively managed and may have exposure to growth and/or value companies. The Adviser believes that by running multiple independent models it serves as a cross-check for the predictions generated by the models.

 

9.Comment: In the Principal Investment Strategies discussion, there is a reference to the prediction platform refreshing daily. Should there be a risk factor added regarding the daily refreshing? If the prediction platform cannot be refreshed on a daily basis will that prevent the Fund from reacting to daily market activity?

 

Response: The Fund has removed the reference to “daily” refreshing of the prediction platform in order to reduce confusion regarding the trading activity in the Fund. The Adviser’s fundamental predictions are updated on regular basis such as when companies publish their 10-Ks, 10-Qs, however, this does not always translate to taking portfolio management actions which are typically performed far less frequent than daily. Based on our revisions noted herein, the Adviser does not believe any additional risk information is needed as it relates to operation prediction platform.

 

10.Comment: Please clarify whether frequent trading will be part of the Fund’s strategy. If it will, consider adding a risk factor for frequent trading. Explain how frequent trading will impact the Fund’s ability to provide daily transparency for the creation and redemption basket process.

 

Response: The Fund included “Portfolio Turnover Rate Risk” in the initial filing of the registration statement in order to alert investors of this possible risk. However, the Adviser’s algorithms will perform rebalancing and investment reselections on at least a quarterly basis, but the algorithms may call for more frequent trading (i.e., weekly, possibly daily in unique situations). In addition, the Fund will be investing in large and mega capitalization equity securities where the Adviser expects low bid/ask spread and high liquidity. The Adviser does not expect the Fund to engage in intra-day trading (i.e., buying and selling the same security on the same trading day) so it does not anticipate any issues with providing daily transparency for the creation and redemption basket process.

 

 

5

 

 

11.Comment: Certain risk disclosures in the principal risk discussion refer to the Adviser having discretion to override the models/proprietary trading algorithms. Add disclosure to the Principal Investment Strategies discussion that states the Adviser has full discretion over investment decisions for the Fund and can override the proprietary trading algorithms.

 

Response: The Fund has added the following disclosure to the Principal Investment Strategies section of the prospectus.

 

“The Adviser has full discretion to override the machine-learning algorithms’ at any time, but it is unlikely the Adviser will do so on a regular basis. This would generally occur when portfolio security weightings and/or the portfolio turnover exceed expected thresholds.”

 

12.Comment: Clarify what “portfolio optimization techniques” are and explain what “portfolio optimization techniques” will be used by the Fund.

 

Response: The risk/performance metrics the Adviser regularly calculates, and uses include Sharpe ratio, Sortino ratio, maximum drawdown, beta, alpha, standard deviation, portfolio turnover, and trading costs. The Adviser will modify its models if needed to optimize these metrics. The model parameters that the Adviser may optimize include security selection criteria, weighting, diversification, rebalancing frequency, and cash allocation.

In order to include more information on this process, the Fund has revised the disclosure in the Principal Investment Strategies discussion to the following.

 

“The Adviser’s AI-driven algorithms actively identify opportunities and automatically invest, divest, and rebalance the Fund’s portfolio allocation, as well as provide real-time performance monitoring and systematic risk management through both rule-based and machine-learning algorithms to optimize portfolio performance. The risk/performance metrics the Adviser regularly calculates, and uses include Sharpe ratio, Sortino ratio, maximum drawdown, beta, alpha, standard deviation, portfolio turnover, and trading costs. In addition, the Adviser utilizes portfolio optimization techniques to determine trading activity, taking into account anticipated transaction costs associated with trading a particular security. The model parameters the Adviser may optimize include security selection criteria, weighting, diversification, rebalancing frequency, and cash allocation. The Adviser has full discretion to override the machine-learning algorithms at any time, but it is unlikely the Adviser will do so on a regular basis. This would generally occur when portfolio security weightings and/or the portfolio turnover exceed expected thresholds.”

 

 

6

 

 

13.Comment: In the Principal Investment Strategies discussion, explain what factors the Adviser considers when deciding whether to employ an overlay strategy. When would the Fund employ an overlay strategy? In addition, explain how the overlay strategy works in tandem with the AI algorithms.

 

Response: The Fund may deploy two types of overlay schemes based on continuous learning AI algorithms by analyzing historical market data and patterns:

 

1)Overnight macro shorting to hedge and neutralize the portfolio when our algorithms predict potential major market downturns.
2)The Fund may also sell options covered call using index options and/or stock holdings when algorithms predict low impact to the portfolio return to generate income for investors.

In order to include more information on the overlay process, the Fund has revised the disclosure in the Principal Investment Strategies discussion to the following.

 

“The Adviser may employ various overlay strategies for the Fund that are designed to increase return and/or hedge against market risks and/or generate income. One strategy that may be employed by the Adviser involves writing covered calls on the broad market using ETF(s) based options and/or on stock holdings in the Fund. The Fund’s use of covered calls will provide the Fund with income, but it will limit the Fund’s opportunity to profit from an increase in the market value of the underlying security to the exercise price (plus the premium received). The Adviser may write call options on securities indices when the Adviser believes the underlying index is going to be flat or down. Both of these overlay strategies are designed to generate income for the Fund during periods when market conditions are expected to be flat to neutral. Another overlay strategy the Adviser may employ involves the use of short selling. Short selling involves investing in such a way that the Fund will benefit from a decline in value of an asset. The Adviser’s use of short selling will be primarily used to hedge/protect against a perceived risk such as a major market downturn. It is anticipated that the Fund’s use of short selling will typically involve shorting ETF(s) and/or stock holdings in the Fund. The Adviser may purchase put options on securities indices and/or stock holdings that provide downside protection as it relates to the Fund’s exposure to large cap stocks and/or to a market sector that the Adviser has identified as a risk for the Fund.”

 

14.Comment: In the Principal Investment Strategies discussion, explain what short selling is. Confirm that the costs associated with Fund’s use of short selling are included in the annual fund operating expense table.

 

Response:The Fund has added the following disclosure to the Principal Investment Strategies section of the prospectus and confirms that its annual fund operating expense table includes all anticipated expenses of the Fund.

 

“Short selling involves investing in such a way that the Fund will benefit from a decline in value of an asset. The Adviser’s use of short selling will be primarily used to hedge/protect against a perceived risk such as a major market downturn. It is anticipated that the Fund’s use of short selling will typically involve shorting ETF(s) and/or stock holdings in the Fund.”

 

 

7

 

 

Principal Risks

 

15.Comment: Consider whether additional risks need to be included as part of the Principal Risk – Quantitative Security Selection Model Risk. Please consider further general risks and vulnerabilities of AI, such as the following.  Please disclose as may be appropriate:

 

a.Data poisoning attacks or manipulation of a model's training data can affect the end performance of a system by leading the model to generate inaccurate outputs or assessments. 
b.Little human involvement in the system to detect anomalous threat activity.
c.On a macro level, motivated actors have incentives to exploit automated trading tools to generate profit, destabilize markets, or weaken foreign currencies.

Response: The Adviser constantly performs the following to protect the Fund from data poisoning attacks or manipulation by:

1.cross checking input data from different sources
2.back-testing and comparing data consistency over time
3.anomaly/outlier detection in input data

The Fund’s investment decisions are based primarily on investment fundamentals and as a result it is rather immune from competition amongst trading algorithms. However, the Adviser has implemented several features to protect the Fund including but not limited to

•       Emergency circuit breakers functions in case of extreme market anomalies; and

•       Validation of market conditions and stability before entering the trades.

The Fund has updated the Quantitative Security Selection Model Risk to include information on how the Adviser protects against data poisoning attacks and model manipulation. The updated risk disclosure is set forth below.

 

“To protect against data poisoning or manipulation of a quantitative model the Adviser constantly cross checks input data from different sources, back tests and compares data consistency over time, and monitors for anomaly/outliers in the data. In addition, the Adviser cross checks its trading algorithms on independent environments, back tests and compares the performance result consistency over time, monitors the security of their systems, and closely controls access to such systems.”

 

 

8

 
16.Comment: Growth Style Risk and Value Style Risk are both listed as principal risks of the Fund but neither investment style is listed in the Principal Investment Strategies discussion. Revise the Principal Investment Strategies discussion to include these two investment styles or remove them as principal risks, if not applicable.

 

Response: The Fund has modified the Principal Investment Strategies section to indicate the Fund may have exposure to growth and/or value companies.

 

17.Comment: Are the proprietary investment models capacity constrained? If yes, explain how the Adviser manages conflicts of interest as it relates to other products/clients that follow the same investment models as the Fund.

 

Response: The Adviser does not anticipate any capacity limitations as it relates to the investment models being used by the Fund. The Fund and all other product currently offered by the Adviser invest and trade in highly liquid mega and large cap stocks. In addition, all trading in the Fund and the other products offered by the Adviser will be synchronized to run in parallel to avoid any conflict of interests.

 

Other General Comments on Prospectus

 

18.Comment: In the section on “Portfolio Managers” in the summary section of the prospectus, please disclose the year in which the portfolio manager began managing the Fund.

 

Response: The Fund has revised the disclosure as requested.

19.Comment: Consider expanding the Item 9 discussion in the prospectus.

 

Response: The Fund has updated its Item 9 discussion and believes it is sufficient to meet the requirements of Form N-1A.

20.Comment: The term “large cap equity securities” has two different definitions in the prospectus. Make the definition consistent throughout the prospectus.

 

Response: The Fund has revised the disclosure to consistently state that the Fund defines large cap companies as companies with market caps of $10 billion or greater at the time of purchase.

21.Comment: The Fund indicates it may invest in foreign securities. The AI models work from data points that are available in the public domain. Do the AI models work only in English? Explain how the Fund will select its investments in foreign securities.

 

Response: The algorithms and models used by the Fund work with numbers. The algorithm and models only invest in large cap and mega cap companies listed on US exchanges and that may include foreign companies listed as American depositary receipts (ADRs).

 

 

9

 

 

22.Comment: In the section on “Portfolio Managers” in the Management section of the prospectus, please disclose whether or not the portfolio manager has experience managing and exchange-traded fund.

 

Response: The Fund has revised the disclosure to indicate that the portfolio manager has no prior experience managing an ETF.

STATEMENT OF ADDITIONAL INFORMATION

 

23.Comment: In the statement of additional information (“SAI”), disclose the other directorships of the Trustees over the past five years.

 

Response: The Fund included the directorship information for each Trustee in the initial 485(a) filing and will include again in the upcoming 485(b) filing.

24.Comment: In the SAI, the Fund’s fundamental investment limitation on concentration refers to the Fund possibly concentrating its investments if the Fund’s principal investment strategy is to target the performance of specific index to approximately the same extent as the specific index. Explain why this is included in the fundamental limitation since the Fund does not have a principal investment strategy to target the performance of a specific index.

 

Response: The Fund has revised the disclosure as follows:

“May not invest more than 25% of the value of its net assets in the securities of one or more issuers conducting their principal business activities in the same industry or group of industries. This limitation against industry concentration does not apply to investments in securities of the U.S. government, its agencies and instrumentalities.”

 

25.Comment: On pages 25-26, regarding the disclosure in Acceptance of Orders for Creation Units, please delete or supplementally explain the legal basis for the disclosure here that the Fund may suspend creations under certain circumstances.  In particular, please explain why the disclosure is consistent with rule 6c-11 (the “ETF rule).  See “Exchange-Traded Funds,” Release No. 33-10695, at p.59 (Sep. 25, 2019).

 

Response: The Fund has modified the disclosure on pages 25-26 of the statement of additional information as set forth below. The Fund believes the changes noted below make this disclosure consistent with Release No. 33-10695 and with recent guidance received from the Staff of the SEC.

 

 

 

10

 

“The Trust reserves the absolute right to reject a creation order transmitted to it by the Distributor if: (1) the order is not in proper form; (2) if the Cash Component paid is incorrect; (3) the investor(s), upon obtaining the Fund Shares ordered, would own 80% or more of the currently outstanding Shares of the Fund; (4) the Deposit Securities delivered are not as disseminated for that date by the Custodian, as described above; (5) acceptance of the Deposit Securities would have certain adverse tax consequences to the Fund; (6) acceptance of the Fund Deposit would, in the opinion of counsel, be unlawful; (7) acceptance of the Fund Deposit would otherwise, in the discretion of the Trust or the Adviser, have an adverse effect on the Trust or the rights of beneficial owners; or (58) there exist circumstances outside the control of the Trust, the Custodian, transfer agent, the Distributor and the Adviser that make it for all practical purposes impossible to process creation orders. Examples of such circumstances include acts of God; public service or utility problems such as fires, floods, extreme weather conditions and power outages resulting in telephone, telecopy and computer failures; market conditions or activities causing trading halts; systems failures involving computer or other information systems affecting the Trust, the Adviser, the Distributor or transfer agent, DTC, NSCC, the Custodian or sub-custodian or any other participant in the creation process and similar extraordinary events. The Distributor shall notify a prospective creator of a Creation Unit and/or the Authorized Participant acting on behalf of such prospective creator of its rejection of the order. The Trust, the Custodian, any sub-custodian, the transfer agent and the Distributor are under no duty, however, to give notification of any defects or irregularities in the delivery of Fund Deposits nor shall any of them incur any liability for the failure to give any such notification. All questions as to the number of shares of each security in the Deposit Securities and the validity, form, eligibility and acceptance for deposit of any securities to be delivered shall be determined by the Trust and the Trust’s determination shall be final and binding.”

 

*               *               *

 

Please contact me at (913) 660-0778 regarding the responses contained in this letter.

Sincerely,

 

/s/ John H. Lively

 

John H. Lively

 

 

11

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