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This Entrepreneur Uses Cutting Edge Data Analytics To Predict The Future Of Financial Markets Posted on Feb 12 - 2018

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To some, the work of Meraglim might seem like magic. By using predictive financial data analytics software, this cutting edge company aims to improve investor performance in complex financial markets, which is a quantum leap forward for a field that until recently, was stuck in the dark ages. Meraglim founder and CEO Kevin W. Massengill likens this transformation to what happened with alchemy in the 1500s.

“Five hundred years ago, alchemy was the science of the day,” Massengill said. “There was a vast body of knowledge around this subject, but it was all wrong. The people who devoted their lives to this subject didn’t know that – they thought they were right. When the thermometer was invented, everything began to change. With the ability to take measurements, alchemy began to morph into chemistry and metallurgy. The alchemists weren’t stupid people; their models were just wrong.”

In Massengill’s mind, what happened with alchemy is the perfect metaphor for what’s happening with modern economics. Educated people rely on textbooks loaded with elegant equations, but anybody who knows anything about Austrian economics, for example, knows those equations are often times antiquated and irrelevant.

Massengill’s company, Meraglim, doesn’t rely on textbooks. It obtains data from IBM Watson, which can read 9 million books, articles or transcripts a day. Compare this with the average financial analyst who would most certainly feel overwhelmed with hundreds of headlines on their Bloomberg terminal each morning, and it’s easy to see the advantage of supercomputing when it comes to data analysis.

“We’re not God,” Massengill admitted. “It’s too much depth. How can analysts be expected to open those headlines up, figure out which ones matter, and integrate what happened with everything else that’s occurred and what they already know?”

The future of data analysis

That’s where a system like Meraglim’s RAVEN comes into play. With RAVEN, an analyst can map out the nodes that matter to them and task Watson with evaluating the world’s inputs to each of those nodes and updating them in real time. This makes the financial analyst’s morning routine look far different than it does now.

Let’s say you are an analyst and you worry about a potential Chinese yuan devaluation. When you arrive at the office on a Monday morning, you ask: “RAVEN, what happened over the weekend?” View More


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