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\Mastering Digitized Commodity TradingDigitization is reshaping commodity trading at an accelerating pace, and players are investing substantial resources to develop critical capabilities. Many companies, though, are moving ahead without a clear view of everything it will take to translate this investment into bottom-line results.
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Capturing digitization’s full potential requires more than just a series of isolated pilot projects or the establishment of a dedicated digital division. It demands that companies develop an entirely new business model, one that starts with a strong commitment from the top of the organization and spans governance, organizational structure, information technology, and other considerations.
This is the fourth article in our series on the impact of digitization on commodity trading. The first three articles covered the gathering storm of hyperliquidity, how algorithms are disrupting the value chain, and the $70 billion prize in commodity trading.
DIGITIZED COMMODITY TRADING’S DEVELOPMENT TO DATE
Many players started down the digitization path, albeit unwittingly, in the early 2010s (or before); they launched efforts to streamline back-office operations by standardizing contracts, introducing straight-through processing, and eliminating middle men (mostly brokers and banks) and replacing them with computer screens that allowed traders to interface directly with counterparties. These moves were not digitization of trading per se but rather cost-cutting measures aimed at staying competitive. The initiatives did, however, establish a foundation for a more pronounced digitization of the companies’ trading businesses.
The first commodity traders to experiment with systematic trading across the full commodities chain were a few experiential power and gas utilities exploring parametric trading opportunities in physical intraday markets. These players typically armed mathematicians with Matlab or Python, gave them predefined parameters, and let them trade small positions with limited risk exposure. Most of these efforts ultimately failed, partly because the companies lacked a technical understanding of the algorithms and the underlying technology but mainly because of limited support from company leadership, which often viewed the activities as curiosities rather than the beginnings of a structural shift in trading. Some commodity traders also started using algorithms to optimize the execution of hedging strategies—inspired by the success of investment banks in the equity-trading arena—but not with the primary aim of maximizing profits.
Events really started to unfold in quick succession from 2015 through 2017, with executives at commodity-trading businesses seemingly realizing the immense possibilities of digitization and subsequently pushing it down through their organizations. This led to the launch of numerous pilots across different commodities:
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On the power and gas front, exchanges such as Nord Pool opened their application programming interfaces (APIs) to clients, and several independent service providers registered with exchanges to offer algorithmic trade execution, which spared clients the trouble of building their own algorithms or testing them with the exchanges. Simultaneously, more-advanced players started developing increasingly complex augmentation algorithms—for example, ones that use machine learning to enhance forecasting of price volatility. Many players ultimately let their organizations freely build their own applications, often resulting in an abundance of applications and data
sources but little coordination among them.
On the oil front, things evolved more slowly. This happened partly because of the high trading margins afforded by the “super contango” of 2015 and 2016 (an extreme distortion in the conventional relationship between spot and futures prices, in which the latter were higher than the former), a situation that muted the perceived urgency among traders to invest heavily in digitization. In cases where players did invest, they frequently did so in relatively straightforward applications, such as the execution of hedging strategies. Although many traders also contemplated developing applications that would enable or support fundamental analysis using a data-driven approach, these discussions were often dismissed over worries about excessive complexity. But a number of oil traders ultimately set up smaller teams that were focused on accelerating the realization of digital opportunities.
On the soft-commodities front, the market split in two. Some companies, such as Cargill, made large investments in data and data science. The majority of companies, however, did nothing. As in the oil and power and gas arenas, most players closed the era having launched many pilots, a good number of which had limited success and were ultimately abandoned or largely forgotten. Very few companies developed a systematic approach to the digitization of their business, one that would make a real difference in the company’s performance.
Since the beginning of 2018, however, the evolution of digitization among commodity traders has started to take a different turn. Rather than thinking in terms of pilots, most players are now talking about data and infrastructure. (We hear things like “Data is the new oil.”) Although this zeal and shift in focus will undoubtedly prove valuable, we would argue that, to truly make a difference in their profits, trading companies must first take a few steps back and make sure that they’re covering all their bases.
GOING FORWARD
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From this point forward, the arc that digitized commodity trading takes as it evolves will depend on two factors: traders’ ambition regarding each commodity, combined with the actions of disruptors; and the readiness of the respective trading markets for digitization.
The first factor is strongly influenced by the general profitability of trading the commodity, a consideration that is becoming increasingly important in a trading world characterized by rising competition.The second factor is a reflection largely of the physical and trading characteristics of each commodity. Here, we consider three variables in particular: the heterogeneity of the commodity, meaning the degree to which the commodity’s physical characteristics vary; the quality and sophistication of the infrastructure supporting the trading (including the availability of electronic trading and exchange- and platform-sponsored APIs); and the “fairness” of, or transparency of information about, the market.
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