Frequently Asked Questions
You’ve got questions about Ridgebase AQ and we’ve got answers.
We occasionally get asked if using data from Ridgebase AQ is akin to insider trading. As you're likely aware, insider trading requires proprietary, non-public information about a specific stock, usually obtained through a senior level source within the company in question. So, based on that criteria, is using Ridgebase AQ's predictive intelligence reporting legal? The short answer: Yes, it's legal. The longer answer is that Ridgebase AQ uses all publicly available information as the basis for its neural network analysis. It is possible to get all of the legislative and regulatory data that is at the core of our analysis, directly from each state or the federal government's relevant online archive. While we apply a proprietary analysis to the data through our AI, that in no way makes the underlying data any less public than say an analyst who reviews a company's 10-Q report before providing a recommendation. We just happen to be really, really good at making the correct recommendations.
A neural network is a type of AI machine learning that mimics the human brain in how it functions. Without getting too technical, the Ridgebase AQ neural network is an unspervised, recurrent neural network that was trained through 10 years worth of regulatory data to learn how to accurately predict regulatory actions and the effects those actions have on markets. Our neural network continues to learn with each prediction, getting more intelligent along the way. For example, the Ridgebase AQ neural network is currently slightly over 97% accurate across 47 states and over 94% accurate in the remaining three states and federally. That means for every 1,000 regulatory initiatives, we accurately predict the outcome of between 940 to 970 of those. Equally important, we are most often able to do so six to nine months before the actual event. Now, figure that at any given time in the United States there are upwards of 30,000 pieces of legislation and regulation pending, and you can begin to understand the significance of this predictive market intelligence. If you want to know more about neural networks, TechRadar has a very easy to digest explanation of the basics located here.
In September 2019, California passed a law known as AB5. The scope of this law was to reclassify most types of independent contractors as W2 employees. The biggest losers from this change were gig economy companies such as Uber, Lyft, Doordash, and the like. This change would potentially cost the industry billions per year. The law went into effect in January 2020 and the gig companies have been fighting it since. The three players mentioned earlier lost $90 million in a lawsuit at the beginning of the year and have since pledged over $185 for Proposition 22, in an attempt to overturn the law. The three biggest institutional holders of these stocks lost $1 billion in the days following the law's passage. Short-sellers actually made $247 million in the two days following the new law passing. So, in response to the question, yes, regulatory events have a huge impact on sectors and companies. Here's the kicker though: we knew AB5 would pass a full six months before it did, before even the California Assembly had moved it out of committee. Just imagine what could have happened, if everyone knew what we did at that time.
From time to time, you might see the name Echo Ridge pop up on the Ridgebase AQ site or if you've seen any media coverage of us. Echo Ridge Corporation is the parent company of Ridgebase AQ LLC and the developer of the Ridgebase AQ neural network. Another way to think of it might be that Echo Ridge is the technology development company, while Ridgebase AQ is the business entity specifically catering to the predictive intelligence needs of investment funds and other institutional investors.
Yes, you can! Aside from the tear sheet reporting products available through Ridgebase AQ, we also offer a raw API data feed of our predictive analytics. This is most useful for larger investment funds that have in-house data capabilities, where the Ridgebase AQ data would supplement existing data sources. A second area for the API data is third-party developers who are creating applications beyond the public equities markets. Examples might include the government relations, commercial lending, or insurance sectors.