The Ultimate Guide
The internet provides lots of dubious “magic formulas” (ask yourself this—if it’s really a magically profitable formula, why aren’t hedge funds exploiting it for all the profit it can generate?) There’s also great information, more than ever before, on how to conduct in-depth research on the fundamentals of a stock’s valuation. But, while perfectly sensible, all the online commentary and debate on how to correctly value a stock fails to answer the most strategically urgent question: how are you going to do it faster than big hedge funds equipped with supercomputers and dozens of Ivy League analysts?
Behind this brutal competition is a simple truth: we can’t address a structural disadvantage without a structural solution, a tool that can actually go about cutting into the ridiculous informational advantage enjoyed by hedge funds and other big institutional players.
Our founder, Oliver Schmalholz, decided to confront this problem head on way back in 2005.
At that time, data-driven investing was just coming into view on the horizon. But Oliver was determined to get ahead of the curve: after losing $1 million in just a single day during the 2000 Dotcom stock market crash, he vowed to never let it happen again.
In the years since, data-driven investing has burst into the mainstream. We encourage you to check out some of the articles below. They’re all cataloguing the same fundamental trend: the rise of quantitative analytics in the stock market. Computers can crunch more numbers than ever before, faster than ever before, and it’s hard to find an industry analyst who doesn’t expect that fact to fundamentally shift the competitive contours of the market.
That’s news event can range from the mundane to the headline-grabbing, just so long as it reveals new information about a stock that wasn’t previously publicly available. Earnings reports, M&A announcements, macroeconomic reports, new ratings by prominent stock analysts, research breakthroughs, regulatory approvals or denials—all of these things represent news events in this way of thinking. All these things have the potential to shift the way the market value a stock.
Of course, thousands of non-consequential financial events pass by every day, often with no detectable effect on stock price at all. The key to separating the wheat from the chaff lies in the data. For Oliver, the ingredients of a data-driven system founded on news event analytics came into focus:
After years of experimenting with this data on his own terms, Oliver arrived at a platform that could deliver consistent profits while limiting risk relative to traditional, buy-and-hold investing approaches. What’s more, the news-driven approach fulfilled his vow: he emerged from the 2008 stock market crash unscathed.
Since then, he’s seen his approach validated by academic studies that actually employed NQ’s data. A study conducted by a multi-university team of researchers from NYU, Rutgers, UC Berekley, and Tel Aviv University concluded that “News Quantified data helped researchers determine that news releases can help predict market performance not just in the following main trading session but throughout the subsequent quarter.” (Journal of Investing, Spring 2018 Edition).
When the leader of the study, NYU’s Dr. Joshua Livnat, was asked how long crunching the numbers would have taken without NQ’s data, he projected 1-2 years instead of 1-2 days. A retail investor could certainly develop an analogous system of their own. But it would take, conservatively, several years and several hundred thousand dollars.
We brought NQ to the market to democratize access to this trading toolkit and offer smaller investors a runway to data-driven profits without the expense of building their own trading system from the ground-up. It really is possible to find sustainable stock market profits without a $2000/month Bloomberg terminal.
NQ users do it every day.