Crowdstrike is down; compiling your own data analysis when the crowd is wrong and groupthink fails
Big thank you to Moomoo Lily. Finally, a brokerage team that understands me.
Is the tendency of the "crowd" to get carried away sometimes, especially with inflationary valuations that... do not really have any logic behind them. Always a good idea to pause and ask yourself "but does it make sense?". Since it is the easiest example to illustrate why "it does not make sense", we'll return to the culprits with the most incorrect accounting who are doing wide-scale damage to the largest ecosystem.
![Crowdstrike is down; compiling your own data analysis when the crowd is wrong and groupthink fails](https://ussnsimg.moomoo.com/sns_client_feed/74563507/20240718/1721313283314-7ef8f3495e.png/big?area=100&is_public=true)
Also: topic goes along with everybody's mooing and buzzing or otherwise alaqsing (best English translation is flying /fluttering) about today -- $CrowdStrike (CRWD.US)$ being "down" and the crutch of needing privately-funded corporate media (X or "Twitter" went private when its CEO wanted secrecy of a private company) to get the word out, as Crowdstrike spokesperson did today, in an attempt to assure everybody that experts have been deployed to remedy the situation.
While they're out there, let's discuss what may be an incorrect petrodollar-based accounting error on a previous iteration of MooMoo's "Investment Themes" algorithm. I noticed this error the other day, and not expecting much, compiled some research from the public Internet. AMAZINGLY, MooMoo listened.
So here are the links and more detail:
Most important is to look at how your algo is handling the LSTM* for "negative externality", such as pollution. Promotion of pollution-oriented businesses should not be happening; theory (my theory) why this error happened is below.
The PETRODOLLAR industry of "cruise ships" has accounting error that likely happened with inflations in increments OVER 1 dollar and deflations broke down into hundred-thousandth or millionths of a penny, or below the 0.01 level of precision. For example: incorrect jump from $43.01 to $178.8765 while debt/equity explodes, due to liabilities going UP for each microgram (μg) of scrubber sludge dumped into the aqua water of the Caribbean. The petrodollar industry's rate of increase was not being matched w/ an appropriate rate of decrease ... and this could be due to missing costs, or costs being lost in algo's incorrect handling of SGD*.
Rate of decrease should be LARGER than rate of increase where negative externalities of polluting environment make big liabilities in health of crew and passengers. The fish, manatees, whales, sharks, corals and crustaceans being slowly boiled to death -- is sad because they did nothing wrong.
One external compiler of data with links (verifiable links) on the information detailed by the illustration I provided is Jim Walker's CruiseLaw news site:
There is definite correlation between SST and climate disaster; `:abbr: SST Sea Surface Temperature` is being adversely affected because, as with SGD* errors, people forget. They are either unaware their vacation monies get converted directly into deadly pollution, or brokers pretend "forget" how gigantic ships are not being assigned proper liabilities (fines) for polluting waterways, air, and marine habitats. Brokers executing on inflation that should have never happened.
Boats dumping sewage, scrubber sludge, unregulated diesel emissions directly into the environment cannot have stock price be positive. Any algorithm that enabled them inflation of their stock price can perhaps be corrected by a better implementation of SGD* on the RNN? Perhaps $Tantech (TANH.US)$ could build a smaller, bamboo-based boat if people want vacay in less pollution.
As for the error of "Infrastructure", skyscraper-sized concrete piles are not good infrastructure to put in future visions of investments.
Acronym Definitions
* Note the following definitions are part of the nGraph core "#AI Topic Library" documentation I worked on at $Intel (INTC.US)$ more than 5+ years ago, and are copyrighted already:
:abbr:`LSTM (Long Short-Term Memory)` is an acronym for "Long Short-Term Memory". LSTMs extend on the traditional RNN by providing a number of ways to "forget" the memory of the previous time step via a set of learnable gates. These gates help avoid the problem of exploding or vanishing gradients that occur in the traditional RNN.
:abbr:`Stochastic Gradient Descent (SGD)`, also known as incremental gradient descent, is an iterative method for optimizing a differentiable objective function.
![All the reasons PETRODOLLAR-dependent "Cruise Ship Industry" should be demoted](https://ussnsimg.moomoo.com/sns_client_feed/74563507/20240719/1721401579375-bc9780a2b0.png/big?area=100&is_public=true)
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104668340 : hi