Commodity markets are being driven by an increasingly diverse set of structural and short-term forces, putting a premium on strategies that combine systematic models with fundamental and on-the-ground research.
Commodities have been a central theme for markets this year. Geopolitical events and economic disruptions have seen oil become a central driver of portfolio decisions, as Suhail Shaikh, CIO of Fuclrum Asset Management, told us earlier in the year. From the commodity rocketing to over $120 a barrel, funds have had to interpret significant fluctuations in its price over the past several months, as a false ceasefire dawns and strategic stockpiling all contribute to a cauldron of uncertainty. Josh Young, Portfolio Manager at Bison Interests, discusses how these influences plus flow positioning contribute to a distorted trading landscape for oil: “Our estimates put a $15-20 disconnection from fair value when you look at comparative inventory analysis. There is a lot of noise creating an uncertain picture.”
This setup is mirrored elsewhere and demonstrates the multitude of different forces influencing pricing. This is a stark contrast to the pre-2020 environment, where China’s economic stimulus behaviour was the central bearer of how commodities behaved. When the country joined the WTO in 2001 and ramped up infrastructure and manufacturing investment, it consumed raw materials at an astronomical pace and thus dominated pricing influence. Now, the setup is far more diversified, in a more fragmented economic landscape, strategic stockpiling, electricity usage, AI demand, and de-dollarisation all contribute to the assets’ behaviour.
Incorporating these diverging forces into a sound, systematic model requires a substantial operational remit. Moreover, it puts the role of discretionary investing firmly under the spotlight. One fund interpreting these shifts and espousing a unique setup is Frontier Commodities, a long/short commodity trading shop in Switzerland. Under the helm of Aline Carnizelo, the firm proprietates its own ‘quantamental approach’, as she explains, “the reason we put on a trade is often a fundamental one – a supply/demand disruption or a cost increase, but once in the trade we bring a lot of data and CTA models to manage risk and avoid wild fluctuations.”
Despite the advancement of trading models, and continued integration of hedge funds and advancing AI research, there are market shifts that sometimes cannot be interpreted without the presence of human judgement. Carnizelo points to the example of Liberation Day last year, a never-before-seen event where determining commodity behaviour carried a heavy degree of uncertainty and therefore required human intuition to take precedence: “At this time all our systematic models were saying to increase risk, I made the human call to bring the risk in the portfolio right down. And we saw funds that followed their models in a way take a substantial hit.”
This process is mirrored elsewhere. Anant Jatia, Founder of Greenland Investment Management, a commodity arbitrage fund based in India, explains that before data is deconstructed and fed into a model, it first must be posed with a question of what you’re trying to determine and what that means for the specific commodity. “Once you understand what the data means for that commodity, the next question is what the tradable form of it is. Is the level informative, or the change, or the rate of change? There is no general purpose for each commodity; it has to be rebuilt for each market.”
As aforementioned, another key element of Frontier’s quantamental approach is the role of on-the-ground research to inform investment decisions. Understanding commodities at this granular level is increasingly valued, as supply chains are skewed and markets have a traditional non-linear nature. Furthermore, data sets and alternative imagery have natural temporal limitations. This is increasingly prevalent across China, where vital information cannot be obtained without an on-the-ground presence. “We have an analyst on the ground in China, and for us it’s not just about understanding what’s being produced; it’s about getting a gauge of what’s going to happen three years from now. This has helped us pick up commodity demand in aluminium, silver and lithium which we would otherwise have missed.”
Understanding the short-term drivers and long-term structural trends that are influencing prices is central to extracting returns from the commodity opportunity set. This is a similar dynamic to equity investments where firms have to weigh up the sustainability of tangible drivers generating returns, whilst sizing positions that offer strong downside protection. Anders Kring, CEO of Incommodities Asset Management, a technology provider that supports risk management frameworks to invest across Europe, believes that proliferation has meant that firms neglect managing this dynamic: “AI will of course play a role, but understanding how physical markets work and the ecosystem is paramount to making astute decisions.”
