A couple of weeks ago, I posted about ETFs reporting that they hold GME who don't actually hold any GME at all. I'm not going to link it because I made a boo-boo and the information wasn't correct (sorry!). But I finally got my hands on the thousands of files and have them sorted in at least a preliminary way to where others can play with the information as well. Here are some snapshots of the kind of information I've been able to extract.

SEPTEMBER 2019. Notice how ETFMG Video Game Tech ETF has enough GME to be considered 244% of their entire ETF holdings across all securities... whatever that means!

OCTOBER 2019. Direxion Daily Retail Bull 3x is a repeat offender of having more GME than the value of their entire fund.

NOVEMBER-DECEMBER 2019. Check out all those 100% on loan in Dec.

JANUARY 2021 (of course). Spicy.

Here are a few screen caps of my summary page that I just finished today.

Late 2019-2020

2021-2022

2023-2024

Public Google Drive with raw data and some of my work organizing and processing it.

I have never coded anything in my life but somehow, by the grace of the Lort and SaintGPT I drug my behind through some Python/Anaconda to help me extract relevant bits of data. In addition to running the above process on GME, I was also able to filter out which documents (nport, in this case) also included CHWY, SIRI, and KOSS. Though I have not finished processing them.

My husband and I run a business so we don't have to punch a clock, which is why I've been able to finally dedicate an obscene amount of time on this. I still need to do so many things... pull all the fund value info, all the borrower info, the creation and redemption. There are also 13f filings in here and a few other filing types.

What I did was use the full text search on EDGAR and searched for every document which mentioned the CUSIP for GME. I used an Edgar scraper from Bellingcat on github. It's incredibly well put together (Bellingcat is a coalition of people interested in open source information gathering). Specifically, I pulled the search results with download URLs using their VERY HANDY Google Colab. I can't say enough good things about whoever those folks are.

I am not sure specifically what I'm looking for in all this info. But I have a theory that using this data in addition to historical FTDs, Price, volume, etc that one might be able to find and "follow" around what I think of as "the hole" where GME shares should be but aren't. I think one might be able to figure out precisely why a variety of stocks move together. Also, I'd like to answer a few questions like

If an AP (authorized participant) redeems an ETF share (phantom or not) what does the AP do with the other securities in that ETF share?

Will I be able to find evidence of known purchases in the data? (RC,DFV)

Can different ETF series in the same fund shift/hide positions between them?

... among others. I invite anyone to dig into this information as well. If you know more about processing data using python and want to help, hit me up. I made an anon email account to handle and share all this info. You can find me at [[email protected]](mailto:[email protected]) . I will be continuously updating the Google Drive with my progress and will be happy to upload any relevant info others manage to pull out. Additionally, I saved some information sheets on each of the filing types in the drive so that you can learn a bit about what it is you're looking at. For example, in the Nport documents, they represent 3 months at a time of info. Which would be important to know when looking at this and working with the data.

My wrists are killing me. I totally imagined myself writing a much more elegantly constructed post but my brain is pudding and I don't think I could type another word. If I think of anything else, I'll edit this post and/or put together a README within the Google Drive.

ETA: my specific theory is that DFV figured out how they were, well, shuffling their short position between ETFs and whatever actions he took/whatever he bought was done at such a time that disrupted the expected flow and caught them with their pants down in a game of musical chairs where he pulled two chairs out at a time that left them unable to cover using the technique they’ve been using to do so. Which is why I’m so curious about what happens to redeemed ETF shares once they’ve stripped them of stocks they want, and when they utilize them, where they hold them, etc. I also find it interesting that these reports are every 3 mos and I wouldn’t be surprised if that contributed to the semi-regular cycles of price improvement. Also interesting that not every etf reports at the same time, and there are some waves that are bigger than others.