Disclaimer: All data shown here is publicly available and can be downloaded and evaluated with the scripts I provide. Please read my previous posts for this. I do not claim the correctness of the data and the source code, everyone is invited to review, repeat and improve the analysis.
GME is a highly volatile stock:
As a measure of volatile, the one minute candles between 03/13/2022 and 04/13/2022 were evaluated. For this purpose, the standard deviation of the percentage change per minute was calculated and compared with all Russel 1000 tickers.
Std. of the percentual candle = standard deviation( (close – open)/open )
The following graph shows the results against the market cap.
The volatility is driven by trading volume:
The volatility can be driven by various factors, but if you take a look at the trading volume, it quickly becomes clear that it plays a decisive role.
As a measure of trading volume, the number of shares traded per minute is multiplied by the opening price and normalized to the market cap.
mean percentage volume dollar = mean( volume * open ) / market cap
Can retail generate this volume?
Short answer: No
Long answer: Retail would have to keep selling each other shares to generate 0.42% of the market cap per minute. To generate such a high trading volume per minute you need both the financial resources and the infrastructure. Retail would quickly run out of money just because of the transaction fees and the spread, not to mention that trading algorithms with the necessary performance and infrastructure are needed.
In addition, retail is clearly a value investor at GME, which is clear from the published DRS figures, for example. In addition, the published data from fidelity show that retail buys both dip and rip. As the following graph illustrates. Data from the wayback archive was analyzed. Each point represents a saved status since 01.01.2021.
Conclusion: MM, HF, Banks are driving the volume and thereby the volatility. They manipulate the price.
How is the price manipulated?
It is not possible for Retail to provide direct evidence of manipulation as Retail does not have access to the necessary data. The process is not transparent. Therefore, retail has to rely on indirect indicators. An indirect indicator is the correlation between occurring gaps in one minute candles and the outstanding shares. The following graph illustrates this relationship for all Russel 1000 tickers.
The proportion of gaps in one minute candles is obviously much too small for the outstanding shares. Note the log scale. Here one can conclude that GME is traded as if it had approx. 10x outstanding shares.
Put simply: If we did not know the outstanding shares and someone gave us this graph and the proportion of gaps for GME, where would we place GME?
Challenge for Artificial Intelligence Experts:
load trading data from all Russel 1000 tickers.
train AI on 500 randomly selected tickers (exclusive GME) to estimate the outstanding shares based on the trade data.
calculate the accuracy of the model on the remaining 500 tickers (excl. GME)
determine the outstanding shares for GME
repeat the procedure to test the robustness of the model
Conclusion: MM, HF, banks create artificial liquidity by their market power in a legeal or illegal way (retail has no/little possibility to check it). The statistical analysis of GME clearly shows the deviations compared to other Russel 1000 tickers and proves that only MM, HF, banks have enough financial means and infrastructure to implement this. This manipulation is created by the artificial increase of the outstanding shares by a factor of approx. 10x. SHORTS DID NOT COVER.