The Rise Of The Machines Part III: Digitization Disrupting Commodity Markets

The commodity trading landscape is changing at a rapid pace, which can be mainly attributed to the digitisation of the commodity markets, as well as the increasingly enthusiastic adoption of new technologies.

The innovative technologies range from algorithmic trading, “big data”, artificial intelligence, machine learning, Python, R, and Tableau, among many others. Across the market, commodity trading merchants, energy majors, and hedge funds are attempting to strike the right balance between a fundamental and quantitative trading approach. The goal is to generate an edge and increase profits, as traditional trading margins are slimming.

The Digital Landscape Across Commodity Markets
To stay ahead of the game in commodity trading, many players are forcing new trading strategies, as digital capabilities determine trading decisions. The result of deploying digital solutions means that middle and back office functions are bolstering in size as quantitative and technological roles grow to support a smaller number of larger risk-taking traders.

The consultancy group, Oliver Wyman, noted in a study that “even proprietary trading strategies both financial and physical will be semi-automated, with a smaller team of traders, and a larger group of analysts that are supported by data scientists who are managing and programming underlying algorithms.” This will in turn enable those players to spot and predict patterns for trading opportunities – which are predominantly generated through smarter artificial intelligence and machine learning algorithms Additionally, there has been a rise of innovative oil trading analytics, and satellite tracking digital contenders, who are attempting to provide reliable oil supply and production data in this increasingly volatile market. The aim is to complete and generate this faster than the Energy Information Administration (EIA) by combining oil sector experience with data science. The rise of these new nimble small digitally led operators include OilX, Refinitiv, Kayrros and TankerTrackers.

In order to seize opportunities and maintain a competitive advantage, major asset-backed commodity trading organisations and independent merchants are developing new quantitative and data science methods at a faster pace.

Quantitative Trading and Analysis
The rapidly growing prevalence of algorithmic and high-frequency trading, as commodity markets become more volatile, is increasingly driven by quantitative data analysis of highly granular market data. This occurs as a result of the large volumes of data in the modern world that commodity traders have access to. It was reported by the Commodity Futures Trading Commission (CFTC) in their 2019 President’s Budget that “automated trading now constitutes approximately 70 percent of regulated commodities derivatives markets”.

The Short Term Energy Outlook produced by the EIA displays the high level of crude oil implied volatility in the following charts, and how global economic and political events are still major contributors of crude oil formation. The data is more readily available than ever, being used by commodity traders to track ‘real time’ as events occur, in the race to beat or embrace quantitative trading systems that can predict volatile market moves.

The Battle for Talent
The swift development of emerging technology over the past 5 years has created a paradigm shift across universities and colleges. High achieving individuals graduating in today’s world are equipped with coding skills, such as Python and R, as a necessary requirement as part of their courses. Despite many commodity trading firms having campus recruiting, as well as links to universities and colleges, to attract graduates to their organizations, they all seem to be upping the ante in their bid to attract the best of the best.

The wave of graduates with these skills has enabled commodity trading firms to develop stronger sophisticated quantitative trading platforms underpinned by leading technological and data analysis. The biggest challenge observed, however, is how effectively these graduates cab then sift through all of the data to generate commercial trading ideas and suggestions.

Conclusion
The shift and growth in technology usage across the commodity trading industry will only continue to develop new market structures alongside digital entrants seizing data-driven opportunities. The digital value is in creating efficiencies through the commodities supply chain, and generating quicker and better-informed trading indicators using the abundant volume of data now available in today’s world.

Read Part I & II on the Rise of the Machines below:

• Part I: Quantitative and algorithmic trading in commodities
• Part II: Building the talent to support quantitative trading in commodities

References
Reimagining Commodity Trading | Oliver Wyman Fiscal Year 2019: President’s Budget | Commodity Futures Trading Commission