Unexpected Risk Meets Unexpected Data

August 21, 2016

Joseph Hooley

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Nassim Nicholas Taleb’s popular black swan theory was named after the shock Europeans felt when they discovered black swans in Australia, despite empirical evidence and their firm belief that all swans were white. He reflected that the experience illustrated “a severe limitation to our learning from observations or experience and the fragility of our knowledge.” In short: The element of surprise and power of the unknown can affect perceptions, decisions and — of course — portfolios.

In this age of volatility, the possibility of black swan events has seemingly become a constant concern, whether they take the shape of an election, natural disaster or regional conflict. Even when we feel confident as investors that the events are themselves predictable, the element of surprise can test what we think we know and leave us exposed to the downside. The U.K.’s public referendum in favor of leaving the European Union took many by surprise, temporarily rattling financial markets around the world, creating political change and economic uncertainty for the U.K. and triggering a host of issues that will take years to resolve.

We can safely assume that future events will continue to jolt global markets. But when even the best of human forecasters struggle to predict with accuracy the outcomes of these events, how can pension plans, for example, effectively make decisions to better weather the volatility that follows?

If surprise events are the question, surprise data may be the answer — as long as we know where to look for them. Emerging technologies can help institutional investors interpret vast amounts of structured and unstructured data to connect the dots between the most unexpected places and events.

Here are a few ways investors can use surprising sources of information to enhance portfolio transparency and identify risk exposure ahead of potential black swan events:

Media outliers. In his 2000 book The Tipping Point, Malcolm Gladwell explores diffusion theory, the spread of ideas after a crescendo event. He writes that “the tipping point is that magic moment when an idea, trend, or social behavior crosses a threshold, tips, and spreads like wildfire,” but he explains that these moments are dependent on “connectors,” “mavens” and “salesmen,” all types of people who help translate ideas and information for a mass audience. Mavens, in particular, are “information brokers, sharing and trading what they know.” In the financial marketplace, the media largely play this role. The 24-hour news cycle, although sometimes overwhelming, can itself help signal the beginning of a tipping point. Using big data to track media sentiment, volume, tone and correlation can help institutional investors understand the diffusion of ideas and outliers that can serve as clues for unexpected risk.

The weather. In October 2015 IBM purchased the Weather Channel’s digital assets for $1 billion to pursue “hyperlocal” analytics. By combining weather information with other massive data sets, IBM expects to help its clients make better decisions to prepare for and offset the impact of weather-related events, which are estimated to cost the U.S. economy up to $500 billion a year across an array of sectors, including agriculture and energy. These analytics, for example, can help retailers better prepare for weather events that could impact their operations and profitability. But the value in these data doesn’t end at the brick-and-mortar shop. They can help institutional investors better manage their holdings in real assets like timber or investments in insurers. Being able to manage the risk of specific holdings in environmental investments is crucial, especially as ESG reporting becomes a new investment standard.

Online retail. When consumers order products, they may be helping investors better track inflation trends to help recalibrate investment strategies before — and after — an event. PriceStats, an inflation series built by State Street Global Markets on online data, uses technology to monitor price fluctuations on roughly 5 million items and tends to identify price shocks faster than similar measures of offline prices, helping investors quickly understand potential shifts in inflation in more than 70 countries.

To extend Taleb’s theory: Risks can come from anywhere, but so can solutions. Regardless of what comes next, investors can better protect their plans from the black swans of the world by using unexplored and diverse sources of information to make connections previously not seen. Or, as Gladwell puts it, “There is a simple way to package information that, under the right circumstances, can make it irresistible. All you have to do is find it.”

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