Tiêu đề SEC- MODELING THE FRAUD - Final 1-24-23 _Redacted.pdf ✅

CONFIDENTIAL Page 1 of 25 EXPERIMENTAL PROOF aka, IF THERE’S FRAUD, PROVE IT Here, I show the results of my experiment to mimic the returns generated by the fraud to demonstrate its existence. The theory is that if I am correct that inverse/leveraged ETP products are a scam and an intentional fraud, then since exotic ETP products are publicly traded and there is an active market, there should be a means to model and mimic the fraud for extreme profit. If there is fraud, not only should there be an ability to profit, but there should be a methodology that can be used to back test and also make future predictions. The returns should be consistent across 200 products all issuers, and over various time horizons. The returns based on this ‘fraud’ methodology should be outsized and standard deviations outside of any statistical probability. So, to prove the fraud...: Mimic the fraud in public markets >> generate impossible returns = proof of fraud Quick Refresher: I’ve gone through great lengths to provide the details surrounding the fraud, including the methods the issuers of these products use to; create, promote and sustain this fraud at the expense of both sophisticated and unsophisticated investors. As a quick recap those methods are: ● Issuing products in pairs: the paired products provide a perfect hedge for the issuers, insulating their short-term risk. ● Daily Resetting: creating a mathematical certainty that the value will approach zero. ● Reverse Splits: manipulates price higher and issuers re-issue or create additional shares at the artificially elevated prices. ● Allowed to be promoted by or sold by financial advisors or held in advisory accounts in violation of their fiduciary responsibility. Without the above, the fraud would collapse and be unsustainable.
CONFIDENTIAL Mimicking the Fraud In Public Markets: Example 1: The method detailed below is more or less how I was trading these products prior to and during the time of my original Whistleblower Submissions to the SEC - December 2019 and January 2020. Model and Method to test: 1. On day one short ‘X’ value of both the inverse and leveraged product a. Example: short $1,000 of SOXS and $1,000 of SOXL 2. When either product goes down by 10% in value add to the short position by .1X bringing the short position back to the original ‘X’ value a. Example: $1,000 SOXL position declines by 10% to a value of $900. Short an additional $100 of SOXL 3. Repeat process for all leveraged/inverse product pairs for each issuer. a. Examples: YINN/YANG, LABU/LABD, TECL/TECS... 4. Rebalance and compound returns over-time - specifically quarterly, yearly, or bi-yearly. 5. The resulting gains will be far in excess of market returns and reflects the profitability of these products by issuing firms. **Remember the issuing firms are achieving these returns with client money and risk to the client, not their own money or their own risk. Results of Modeling the Fraud: Direxion ETFs (see Direxion spreadsheet) When only products that have gone through one or more reverse split are used the above model works over every medium (3-6 months) and long term (1+ yrs.) period tested. The results often beat the associated benchmarks for each product pair (by a lot). As part of this report and attached spreadsheet I tested every Direxion and BMO Microsectors L/I product from the date December 17, 2019, which was the date of my first SEC Whistleblower submission through December 23, 2022. The result was a return of 93.44% (Direxion) for the 3x/-3x products (non- bond products) which can be compared to the S&P 500 return of 19%. Over a 4.8x out-performance by mimicking the fraud. Page 2 of 25
CONFIDENTIAL Page 3 of 25 Performance between September 7, 2021, and December 23, 2022 is even more dramatic at 39.32% return for mimicking the fraud compared to S&P 500 performance of -15% over the same time period. Table taken from Spreadsheet: Direxion Results of Modeling the Fraud: BMO Microsectors ETNs As BMO products are a newer offering (started in 2018) I tested the above method by looking at performance based on: ● Inception to Present (1/13/2023) ● Full Year 2021 ● Full Year 2022 Below is the table of results (see spreadsheet BMO):