Rise of the Machines: Global Macro’s Deep Blue moment

Rise of the Machines: Global Macro’s Deep Blue moment

Global macro investing might be boiled down to investing in assets on the basis of changes in fundamental landscapes:

IA Fintech Member Insights: TOGGLE


Global macro investing, made famous by George Soros, has typically been associated with the swashbuckling style of “anything goes.” Global macro strategy’s broad mandate permits portfolio managers to invest in virtually any instrument, anywhere in the world. Macro investors are also great storytellers – raconteurs – and the elegant narrative surrounding their views sometimes earns them the moniker investor-philosopher. But is there a method to the madness? Or is the lack of performance in recent years proof it was all smoke and mirrors?


At its simplest, global macro investing might be boiled down to investing in assets on the basis of changes in fundamental landscapes: ups and downs in growth and inflation. It’s the cadence of the business cycle buffeted occasionally by changes in monetary policy and fiscal stimuli. To complicate matters, as George Soros pointed out in his book The Alchemy of Finance, there is a self-reflexive relationship between policy and the business cycle. Furthermore, variables like credit, C/A balance, money growth, and housing also matter, though they’re typically a symptom or consequence of growth or inflation.


Most asset prices are driven by both macro (systemic) and asset-specific (idiosyncratic) factors. However, asset price drivers are not fixed in a neat formula: at any moment, they are a subset of a large collection of possible drivers. For example, the price of Apple may be driven day-to-day by news about a new product or incremental demand as reflected in the latest iPhone shipments from Taiwanese manufacturers. However, a dramatic shift in outlook for the Chinese business cycle could lead to a wholesale revision of consumer demand, driving prices up or down irrespective of the company-specific picture.


Against this backdrop, the task of macro investing is to identify an imbalance (peaking growth, rising inflation, unexpected policy change …) that will surprise market consensus and select assets that will be most sensitive to it. Put differently, macro investing is betting on moments when macro (systemic) variables dominate asset price dynamics.


Easier said than done. Macro forecasting may rate second only to palm readers and soothsayers in the degree of skepticism it attracts. This is why macro managers tend to thrive around unusual events. They’re firm believers that betting against “it’s different this time” is the best risk/return bet in the business. The key is to look for dislocations in the business cycle that appear ripe for normalization by finding metrics that appear vastly stretched on historical scale: spending on housing, consumer credit levels, external imbalances…you name it.


The trouble is, this approach is very labor intensive. Even after you’ve identified the high-level drivers, you need to find the right assets amongst thousands that may move in the coming adjustment. When there were only a handful of assets to trade, and little information to analyze, small teams could crunch the numbers and find the right opportunities. Nowadays, large numbers of hedge funds are turning to more and more data in pursuit of this edge. This takes an inordinate amount of time and analysis. The old model – analysts with Excel spreadsheets – doesn’t scale easily.


This is where machines can help. Investors can use raw computing power to scan data for large deviations from historical norms. Marrying these findings with a knowledge graph, an interconnected web of relationships, allows the identification of tradable patterns across every imaginable asset class.


This approach has many advantages, a major one being the ability to sift through a multitude of global  data series – taking full advantage of the Big Data age – picking up symptoms of macroeconomic tension much sooner. For example, much before it’s reflected in consumption numbers, data might show that cross-country shipments of goods by railcar have slowed abruptly ahead of the Christmas shopping season.


Do retail stocks react before or after retail sales slows down? What exactly is the lead/lag? With large scale computing and an AI-enhanced knowledge graph, these questions are answered in a matter of minutes. Computers crunch through all possible leading and lagging scenarios, not only for retail stocks but also in parallel for the entire equity and fixed income universe.


Global macro investing is on the cusp of its own Deep Blue moment. Chess masters no longer fight machines in widely publicized matches: they use them to get better. Soon enough, so will global macro investors. The next George Soros, Stan Druckenmiller, Louis Bacon and Paul Tudor Jones won’t think twice about leveraging machines. Like Google, they’ll become second nature in the research process.


To learn about how TOGGLE harnesses these new technologies to help investors get insights from data, visit www.toggle.global or email [email protected]

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