See Risk in a Very Different Way

I was thinking about commodities the other day. Well, it was actually sausages and I probably was not alone. These are strange times but who would have thought that a dispute over a breakfast food item would threaten to derail the entire agenda planned for the G7 summit in Cornwall? Thankfully, UK-EU trade tempers cooled and consensus was achieved to tackle much bigger global risks like pandemic vaccine supply and climate change. Of course, sausages were never really the risk catalyst but geopolitics loves an attention grabbing image irrespective of accuracy. Financial markets, on the other hand, don’t do image. In fact, disguise might be closer to the mark.

Michael Lewis of Liar’s Poker fame put it rather well – “Risk, I had learned, was a commodity in itself. Risk could be canned and sold like tomatoes.” The concept of investment banks packaging and transferring similar-looking product between buyers and sellers on a mass scale was great for the bankers pocketing a fee on every trade. However, investors would discover over time the tomatoes in the cans had very different futures. The challenge for investors was to look through the packaging and avoid the poorer quality produce. Welcome to the world of financial analysis and human beings’ misguided confidence in forecasting the future.

Historically, the analysis of a financial asset’s pricing behaviour, market sentiment, macro influences, quality and financial performance have been the fundamental building blocks to pricing/valuing risk. However, this approach tended to present a static risk picture at a particular point in time. The critical miss was change. While we are useless at forecasting the future our best weapon in identifying a risk shift is spotting change. That’s not a job for the human eye or brain alone. Now thanks to super-fast computing power in the cloud, enormous alternative data pools and AI machine-learning those investors with the best digital “eyes” have been able to identify change early and add/offload risk accordingly.  Note the reference to the best eyes. The battle for trading supremacy has swiftly moved on from data capture to AI-assisted analytics.

The best “eyes” need the best analytics as financial markets confront a new risk monster. Pandemic panic has pushed climate change/catastrophe to the forefront of investor consciousness. Indeed, it is not an exaggeration to state that climate change is front and centre of every investment process these days. And, the following recent headlines starkly illustrate the increasing risks:

‘Mega Heat Wave’ is peaking in West, breaking records and intensifying drought – Washington Post

Lake Mead at Hoover Dam to reach lowest water level in decades – CNN

No Water, No Microchips: What is Happening in Taiwan – Forbes

Global food prices post biggest jump in decade – Financial Times

How ‘Chaos’ In The Shipping Industry is Choking The Economy – NPR

PG&E Warns of More Blackouts During California’s Wildfire Season – Wall Street Journal

The challenge for financial market participants is that older risk models need serious upgrades to monitor the impact of climate change. Look no further than the Agri-Food sector and its long list of risk stakeholders – farmers, hedge funds, commodities trading houses, food processors and insurers are all hungry for superior visibility and analytics.  However, it would be a mistake to presume satellite data and images are the one-stop solution. Just like traditional financial analysis, a static risk picture is a poor way to monitor and spot change. Fortunately, technology has developed to meet that challenge with similar solutions to those used by Wall Street trading desks. Think of satellite images as a relatively unsophisticated source of data. The real opportunity is in a superior “picture” boosted by the following:

• Multi-spectral, hyperspectral and radar imagery, of varied spatial, spectral and temporal resolutions plus ground sensor technologies(IoT)

• Machine learning approaches including: Principal Component Analysis, Feature Selection, Random Forest, Neural Networks, Support Vector Machines and Deep Learning.

• Cloud service processing platforms(AWS)

• Integration into existing risk trading models via API

Financial market participants who are seeking an ‘edge’ in identifying change early and pricing risk  will want the best ‘eyes’ and the best insights powered by the latest technologies. The risk calculation is stark. If you can see change early and in a very different way, you will price risk in a very different way. More than food for thought me thinks…..