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150 Long-Term Predictions From Artificial Intelligence

Over the last couple weeks HADE Technologies has published several stories to showcase its artificial intelligence breaking barriers and rewriting the rules of Wall Street. Most recently, we showed how our robots were able to accurately predict future quarterly revenue with 57.5% more accuracy than the consensus of Wall Street analysts. In an industry where marginal improvements are worth billions of dollars, this degree of separation is absolutely mind boggling. You can read about it below:

These noted predictions above are looking just three months into the future. However, we have another application for our predictive technology, and that's for long-term multi-year outlooks and predictions. We have already published more than 150 different long-term predictions over the last five months using the same technology that yielded a 57.5% improvement to quarterly revenue. 

You can now click the link below, or visit our "outlook & predictions" tab above to browse all of the outlooks. We have outlooks that cover a very wide spectrum, from Apple's total revenue, Netflix's expected international members, or even predicting drug sales for some of the biggest blockbusters in Pharma. If you are an investor, success is determined by your ability to predict the future. Hence, this is one free product you don't want to ignore.

You can access all of our predictions for free, all of which are integrated with our cutting edge visualization and analytic technology. We currently have more than 150, and anticipate more than 500 by the end of next year. Continue to check and explore the link above for updates and new publications. 

Why HADE Technologies is so accurate at predicting performance

It's one thing to talk about your great performance, it's another to give away all your secrets. Of course, that's something we would never do for our patent pending technology. However, with 100s of algorithms and a combination of slow and fast data, I don't think it would hurt to explore the general premise for which such performance is possible. And keep in mind, the following are just two reasons from a list of 100s that build our prediction models. 

First, we have data that no one else has, stored in our database. Most financial databases have one source of company revenue, sometimes two. Meanwhile, we track every segment, category, and sub-category for the companies in our database. Thus, it is easier for our machines to identify trends and find connections when there is so much data to absorb. For a company like Apple Inc (NASDAQ:AAPL), we can look at all its geographical regions and products/services, and really understand how its revenue is created. The same goes for Netflix (NASDAQ:NFLX), MGM casinos, or any other category we are trying to analyze. 

Second, we consider atypical factors such as a company's ability to manipulate Wall Street when making its own predictions. 

A stock is nothing more than a reflection of future expectations. If a company beats its earnings expectations, or raises its financial guidance, the stock goes higher. If investors believe a company will perform better than Wall St expects, the stock goes higher. Some companies are notoriously great at keeping Wall Street in check, by issuing "financial guidance" that they will easily exceed. One such example is Apple. Others such as Fitbit Inc (NYSE:FIT) are equally bad.

Our machines take this into consideration, the degree of historical "beat" or "miss" for each company compared to past expectations. We call it a psychological indicator, of which is very important when trying to make accurate predictions. 

All things considered, these two things illustrate how we use slow data (historical information) and psychological factors to predict future performance. However, there is much more involved in the process. We are working with algorithms that constantly change as fast data (real time) is fed to the system each day, and historical data is updated. The goal is to keep getting better, but with a 57.5% improvement to Wall Street, there is no question that we have set the bar very high.