Over the past 15 years, the global economy has operated under two different growth models. Between 1999 and 2007, the growth model operated through ever larger trade imbalances between emerging market and commodity exporting countries – who ran larger and larger surpluses – and a group of rich countries – first and foremost the U.S. – who ran larger and larger trade deficits. Global imbalances were then seen as a problem by some, but they were really a symptom of the global geographic distribution of aggregate supply and demand, with excess supply in the high-saving emerging market countries and excess demand in some low-saving rich countries (and with energy exporting countries doing quite well as they exported to both). These supply-demand and saving-investment imbalances generated huge international capital flows that were sufficient to bring global demand into line with abundant global supply of goods at something approximating the full employment of global resources. ... That growth model obviously broke down in the global financial crisis years of 2007–2009 as global imbalances shrunk in line with global aggregate demand. From 2009–2014, the global economy has operated under stimulus from “nontraditional” monetary policies that pushed policy rates to zero and ballooned central bank balance sheets through massive “chunks” of quantitative easing. Also, the global policymakers “went Keynesian” for a couple of years during and following the crisis by delivering a large dose of fiscal stimulus. The good news is that, as a result, the global economy avoided depression and deflation. But that’s all they did or could reasonably do. The reality is that now, five years after the global financial crisis, average growth in the global economy is modest and the level of global GDP remains below potential. The global economy has not as of today found a growth model that can generate and distribute global aggregate demand sufficient to absorb bountiful global aggregate supply. Unless and until it does, we will be operating in a multi-speed world with countries converging to historically modest trend rates of potential growth with low inflation. 0% neutral real policy rates for many developed and some developing countries will likely be the investment outcome.
When energy-drink company Red Bull bought Ford’s Formula 1 team Jaguar Racing in 2004, the team was in a shambles. In the five years it was controlled by Ford, its drivers won not a single race. The closest it came to a championship was in 2004 when Jaguar placed seventh in the constructors standings. Out of 11 teams. … Renamed Infiniti Red Bull Racing, the team now dominates Formula 1 racing in much the same way Ferrari did during the glory years of Michael Schumacher in the early 2000s. It has won the double championship—when a team comes first in points both for an individual driver and for the team, or constructor of the car—every year since 2010 and is well on its way to winning its fourth. But advanced engineering accounts only for part of its astonishing success. As important is the way the team uses data.
What economists and marketers are learning from newly accessible consumer data … Around the world, billions of sales transactions every month, down to a can of Coca-Cola from a local store, are recorded in some way by Nielsen, the measurement and information firm that has been gathering data from retailers and consumers for 90 years. For most of its history, Nielsen shared those data primarily with its retail customers and manufacturing customers under strict agreements that protected customer confidentiality. Academic researchers gained access to some data by negotiating directly and often at length with Nielsen, or by partnering with a corporation and promising the data and results would be for internal use only. … Now Nielsen is sharing three datasets through Booth, with a staggering amount of information. One dataset covers purchases by 40,000–60,000 households in the United States. Another contains sales results from 35,000 stores—grocery stores, drugstores, discount chains, and similar outlets—for the years 2006 through 2011. Those records span up to 3 million bar codes, and the data represent about 33% of the volume at mass merchandisers and about 55% of US retail volume from grocery stores and drugstores. … The information now available is a gold mine for researchers, marketers primarily, but also economists who see the potential to explore longstanding questions about consumer behavior.
Early this year, as part of the $92 million “Data to Decisions” program run by the Defense Advanced Research Projects Agency (DARPA), the Office of Naval Research began evaluating computer programs designed to sift through masses of information stored, traded, and trafficked over the Internet that, when put together, might predict social unrest, terrorist attacks, and other events of interest to the military. Blog posts, e-mail, Twitter feeds, weather reports, agricultural trends, photos, economic data, news reports, demographics—each might be a piece of an emergent portrait if only there existed a suitable, algorithmic way to connect them. ... There is no doubt that the Internet—that undistinguished complex of wires and switches—has changed how we think and what we value and how we relate to one another, as it has made the world simultaneously smaller and wider. Online connectivity has spread throughout the world, bringing that world closer together, and with it the promise, if not to level the playing field between rich and poor, corporations and individuals, then to make it less uneven. There is so much that has been good—which is to say useful, entertaining, inspiring, informative, lucrative, fun—about the evolution of the World Wide Web that questions about equity and inequality may seem to be beside the point. ... But while we were having fun, we happily and willingly helped to create the greatest surveillance system ever imagined
Somewhere in your favorite sports franchise’s front office, a team of analysts is teasing the truth out of a mess of misleading statistics. Regardless of the sport or the data source — Corsi, SportVU, or Statcast — the analysts’ goals are the same: to capture contributions that standard statistics omit or misrepresent, and to find the positive indicators buried beneath superficial failures. The shot on goal that goes wide? In a sense, it’s a good sign, since it might mean more shots in the future, some of which will find the net. The line drive caught by a leaping outfielder playing out of position? A double would’ve been better, but even an almost-double tells us that the player who came close to extra bases has the skills to drive the baseball at a speed and trajectory that would typically lead to a hit. Not all outs are created equal. ... Whether they know it or not — and nowadays, most of them don’t — all of these quants are re-proving the principle at the core of a product developed two decades ago by a company called AVM Systems, a small outfit founded by Ken Mauriello and Jack Armbruster, two businessmen based in the Chicago suburb of Wheaton, Illinois. AVM’s central insight sounds hackneyed now, but it was — to borrow a latter-day business buzzword — disruptive at the time: Process is important, because results are sometimes deceiving.
CargoMetrics, a start-up investment firm, is not your typical money manager or hedge fund. It was originally set up to supply information on cargo shipping to commodities traders, among others. Now it links satellite signals, historical shipping data and proprietary analytics for its own trading in commodities, currencies and equity index futures. ... There was an air of excitement in the office that day because the signals were continuing to show a slowdown in shipping that had earlier triggered the firm's automated trading system to short West Texas Intermediate (WTI) oil futures. Two days later the U.S. Department of Energy's official report came out, confirming the firm's hunch, and the oil futures market reacted accordingly. ... in this era of globalization 50,000 ships carry 90 percent of the $18.5 trillion in annual world trade. ... "My vision is to map historically and in real time what's really going on in economic supply and demand across the planet" ... building a "learning machine" that will be able to automatically profit from spotting and publicly traded security that is mispriced, using what he refers to as systematic fundamental macro strategies. ... CargoMetrics was one of the first maritime data analytics companies to seize the potential of the global Automatic Identification System. Ships transmit AIS signals via very high frequency (VHF) radio to receiver devices on other ships or land.
He's not the genius cranking out code, the analyst looking for the next big IPO, the hand-shaking CEO, or the wartime general turning a pile of intel into a plan. He's the guy who can talk to all of those people, understand them, and combine their strengths into a matrix none individually would have imagined. ... He didn't even have a particularly military bearing. While other guys pumped iron, the lithe little yoga dude they called Dr. Spaghetti Man was stretching and breathing on the wrestling mats, an Ivy Leaguer downward-dogging in a world of booyah. ... As a DARPA program manager, White could name his project. And the “thing” he wanted to make was a new breed of search engines, capable of mining the entirety of the Internet. ... White’s Memex project would be a portfolio approach. Some tools would dive into the dark Web and present all the hidden onion sites to be found there as a list, something previously considered too difficult to bother with. Others would index and sort the enormous flows of deep and dark Web online forums (which are otherwise unsearchable). Others would monitor social-media trends, connect photos, read handwritten information, or strip out data from Web pages and cross-index the results into data maps.