A recent piece by Barron titled “Here Comes Teenagers: They Can’t Vote, But They’re Old enough to Buy Stocks,” speaks favorably of today’s wealthy, fast-moving young “investors” who analyze ideas on Reddit forums and take risks all in YOLO operations. Companies like Robinhood and public figures like the king of SPACs encourage this behavior by pretending that these idiots have a chance against the world’s most sophisticated institutional algorithms. “At 16, Davé is not your typical trader,” the piece says. Dave sure isn’t.
BSD accessory traders are now gone. Instead there are stock trading algorithms that consume data voraciously and use it to generate alpha. And they have been there for a long time. Today’s teen traders may not know Jim Simmons ’name, but this mathematician’s algorithms are often on the other side of the trade.
From a 2019 Wall Street Journal article entitled “The Making of the World’s Great Investor:”
Today, Mr. Simons is considered the most successful money producer in the history of modern finance. Since 1988, its flagship Medallion fund has generated average annual returns of 66% before charging high commissions to investors, 39% after commissions, accumulating commercial profits of more than $ 100 billion. No one in the investment world is approaching. Warren Buffett, George Soros, Peter Lynch, Steve Cohen and Ray Dalio fall short.
Big data is what drives these returns, something we’ve written about before Eight ways to use alternative data for trading. The same data can also be used by companies to learn about how operations can generate more revenue and profits. The most interesting data sets are those that arise as a result of software consumption in the world. Some refer to this as “data depletion,” others call it “environmental intelligence,” but both terms describe massive data that is constantly produced based on people’s normal daily activities. From this data, a large amount of information can be used.
6 companies that monetize alternative data
AlternativeData.org is run by a group of former financial analysts, data analysts and engineers who help institutional investors make the most of all the alternative data on offer. We examined your database to find some interesting alternative datasets that can be used to make money from teens or improve the performance of your business.
Exhaustion of online advertising data
Exhaustion of online shopping data
Similar to Cyberstream, the Hong Kong Measurable AI startup doesn’t talk much about who they are, but they’re doing something just as extraordinary. Measurable AI collects billions of electronic receipts collected directly from users who choose to share their data through various consumer applications. (All of these “free” apps you use aren’t exactly free.) The firm specializes in emerging markets (ten of them) and the type of data they collect includes spending habits of large firms that are commonly found in emerging markets.
The company’s blog contains interesting information about how their data sets are used, such as to exploit “whales” in the video game community, the small group of people who contribute a large percentage of revenue to successful games. Studies have shown that only 1% of users are responsible for more than 59% of the iPhone market revenue in the United States. Measurable AI can help you find these people.
Data depletion of computer networks
Third on our list of companies with a one-page web presence is Del Mar Networks in Pasadena California, which doesn’t even provide a physical address. All we know is that a guy named Aiden works there and participates in the production of a data set that tracks web traffic at the hardware level. Anyone who has used tools like Google Analytics to measure their web traffic knows how accurate data can be, at best, trivial. We use these tools and often see peaks of mystery or an inexplicable phenomenon. (Good luck asking Google to explain the data. They’re too busy crafting the changing “User Experience” that publishers have to skip.) Del Mar Networks has developed “new techniques for measuring web traffic at minute-level resolution through direct observations of the web infrastructure.” This is almost as close to the truth as it will get.
Exhaustion of data from the courtroom
Most publicly available datasets have already run out until they run out, but they can always be resurrected by doing good old-fashioned data storage. A company called Premonition has assembled the world’s largest litigation database that includes artificial intelligence algorithms that travel the world, adding millions of legal cases every day. (They claim their algae can read 50,000 pages in less than a second.) As a result, they can start allocating success rates for attorneys who are ready to hang on to Salesforce.
An example of how useful this data would be would be AbCellera’s lawsuit against Berkeley Lights. As shareholders of the latter, we were pleased to hear the lawyer defending Berkeley Lights, Morgan Chu, who has been described as “arguably the most gifted trial lawyer in the United States” who “offers amazing results for clients. ”. Wouldn’t it be great to replace these superfluous statements taken from Mr. Chu’s website with his actual courtroom history and do the same for the plaintiff? You could then start assigning probabilities to court results. Or better yet, let an artificial intelligence algorithm do it for you.
Exhaustion of investor data
We monitor hundreds of stocks to keep your finger up to date on the hottest trends today. To do this, we have created portfolios on Investing.com that can then be linked to price alerts. It is a free service that also allows you to upload your actual holdings and track performance, a useful tool for any investor who has multiple brokerage accounts that they would like to add. As the third largest financial website in the world with over 20 million users a month, they are generating a lot of data and a few years ago they started earning money. Available data includes sentiments, price alerts, portfolios, and what your users are looking for. It’s not just about starting revenue with data revenue.
Data depletion for investors
Our own technology investment methodology is to look for financial presentation documents to find red flags, because what a company says to the SEC and what they tell the world can be two dramatically different things. For example, the way a company breaks with its routine corporate telegraphs, what happens inside. A simple inconsistency can mean a lot. For example, we recently noticed these two contradictory phrases in the 10-K company:
Know about the departure of an executive before it is announced (we are not saying that is what happened in the previous example) can be very valuable information. A paper called Lazy Prices talks about how “language changes related to the executive team (CEO and chief financial officer), in terms of litigation or the risk factor section of documents, are especially informative for future returns.” These are the types of reports that a company like Fraud Factors could cause. For example, they provide 56 fields that measure the linguistic characteristics of 10 K files.
We often compare information at annual intervals to see how forecasts change or even didn’t change. If every year a company says revenue is five years away, something is wrong. Finding inconsistencies is just an idea of the many fraud factors it provides by exploring all the financial applications filed with the SEC for red flags, which we would say the SEC should do now.
Some additional comments
Think about how much data a company like Walmart runs out of every day. You can bet that Walmart is monetizing this data, but not selling it. Large companies create internal value with their data. When a company can’t create value using its own data, the next best thing to do is sell it: Investing.com is a good example. Then you have companies that don’t generate the data themselves, but have figured out how to collect other people’s data, structure it, aggregate it, and sell it.
The question we asked ourselves when researching this piece is how are these companies getting what they say they are without taking advantage of some sinister methods? (Enter your own conspiracy theory here.) Alternative data providers don’t talk much about how they manage their operations, and that’s probably because they want to avoid the prying eyes of data privacy advocates.
Technology will advance whether people complain or not. If controversial AI algorithms generate incredible value for a company, they will bury them so deep in their Frankenstack that they will never find them. And all those political activists posing as “ethical AI experts” who call for “transparency in AI” will never know the difference. The point is that we are all being watched very closely in the current time and age, whether we like it or not.
Finally, we cannot offer any guarantee that any of the companies mentioned in this piece will provide accurate and legitimate data. Half of them manage one-page sites with little information other than an address. We can only assume that the people at AlternativeData.org have thoroughly reviewed them before selling their data. Nowadays, it’s easy to create synthetic data, so always make sure you do your own due diligence before buying data from anyone.
There are some lessons to be learned here. The first is that today’s teens are better served by saving their money and allocating it to STEM education so they can add value to society rather than become “creators” in search of brochures. The second is that if you don’t use big data to drive your business, those will let it pass quickly.
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