This article about a possible correlation between FTDs and GME price movements was posted on Reddit on 31 Jan 2022 by user u/Mother-Ingenuity-442 and appears to be credited to the work of user u/section741. It is being reproduced without editing here because, well, we like to publish great DD and save it to the blockchain, impervious to censorship and there forever (yeah, screw you Kenny, you don’t like that much do you?).

u/section741 analyzed all the FTD data for every ETF containing GME and found a near 100% correlation between the FTDs and GME stock price movements.

I downloaded the publicly available FTD data from the SEC:

https://www.sec.gov/data/foiadocsfailsdatahtm

Here’s a script to do that: https://gist.github.com/allmeasured/11bb2e17646dd89853b07ab691c09696

I looked at past FTD data and saw a very high correlation between FTDs of GME and future price movements. So I decided to continue looking. I decided to dig further into the ETF data for ETFs containing GME. I got that list from here: https://www.etf.com/stock/GME

Correlating the ETF data and GME price movement is pretty hard since there’s over a hundred of them and the ETFs all have different amounts of GME. I decided to do the simplest thing that came to mind (this is where you might have a better way to do this and I’m not even sure if this is correct). What I did was to normalize the FTD data for all of the ETFs containing GME by doing the following:

def normalize_ftd(etf_gme_percentage, etf_gme_market_value, etf_ftd):         max_value = 200000000         normalized_value = etf_gme_market_value / max_value         gme_percentage = normalized_value * etf_gme_percentage         return gme_percentage * etf_ftd  

Basically this should return some number that approximates the weight of GME FTDs, and as long as I use the same formula for every ETF then it doesn’t really matter if this returns the actual share number or not, it’s just a number that is weighted equally across all ETFs so then I can sum it together and look for a pattern.

Next I ran a script for cleaning up the data I downloaded from the SEC so it only contains ETF FTDs from 2020 onward for ETFs that have GME, note that it depends on the previous script having been run so you have a folder with all the SEC FTD data:

https://gist.github.com/allmeasured/cf839f363a75e93dd0f7dd42970e3e46

That generates the following file:

https://gist.github.com/allmeasured/2809fbfe188c7feee5188e00312200fa

Now we have all of the FTD data for GME containing ETFs. Now I ran the following script to generate a CSV file with the sum of normalized FTD data across all GME ETFs:

https://gist.github.com/allmeasured/97a12eb4fbb5cb863019421d5b5a1bd2

That generates the following CSV file where the first value is the date and the second value is the sum of the normalized FTDs:

https://gist.github.com/allmeasured/ee1396a670b0bdce2e0180efcb97651a

Ok so now I put that data into a spreadsheet and this is the result (I’m only showing 2021 because that’s where it gets interesting):

I honestly did not think this would show anything and was extremely surprised to see the extremely high correlation to GME prices. Look at the big (or even small) spikes above and compare them to the spikes in GME prices:

It could be the case that this is just a wild coincidence. Could also be illegal naked shorts.

None of this is financial advice, I’m just trying to understand what is going on.”

Apes Army takes no credit whatsoever for this article, nor do we guarantee the accuracy of any of the information contained within….Mainly because it’s wrinkle brain stuff this, and figuring out whether there is a correlation between the FTDs and GME price is not really what we’re good at here, so take everything always with a massive grain of salt. Naturally this is not financial advice. DYOR.