The digitalization of commodity trading desks is rapidly redefining the attributes of fundamental analysts, placing them at the heart of trading and investment strategies. Hedge funds have been leading the digital transformation of trading activities. With other trading businesses keen to retain a competitive edge, the pace is set for this transformation to spread to trading houses and traditional merchant traders. But in the current market environment, the specific requirements from analytics functions still vary depending on the profile of companies as well as the nature of the data that can be quickly leveraged – or not – through advanced tools.
The role of analysts has always been focused on understanding the market of one or several products covered by trading desks, interpreting trends based on data such as ship-tracking or high-frequency demand figures. Other non-quantitative information from experts in geopolitics, economics and even geologists when it comes to oil and gas markets are equally important. Fundamental analysts typically understand the physical characteristics of the oil and gas supply chain, different grades of crude, oil and gas infrastructure of producing and consuming countries, and the difference between shale oil drilling and so-called conventional techniques.
In recent years, the evolution of analysts’ role has been influenced by the increased data availability through digitalization and to a lesser extent, more transparent public data. Historically, analysts would focus on supply and demand statistics while the interpretation would be left to traders.
But increasingly, analysts are getting more involved in connecting the fundamentals to the market’s behaviour thanks to more advanced modelling tools. Through a holistic approach, their role is to build a narrative with the data, explore various scenarios and views, and propose them to traders so they can make efficient trade and investment decisions.
After years of experience, analysts usually grow to become head of analytics, trading strategists or even traders, especially when employers are faced with fierce competition for new traders amid a scarce talent pool. Since the start of 2022 for example, HC Group’s Liquid Fuels practice in the United States has seen several trading strategists or senior analysts moving into junior trading positions.
This is because some crude derivatives traders have had such strong years since the start of the coronavirus pandemic that it has become extremely challenging to entice them out of their current position. As a result, recruiters have turned their attention to the most attainable talent like analysts who are eager to move into a trading seat, often considered a more prestigious role and career move.
With more responsibilities and influence on trading decisions, fundamental analysts are gaining status. Increasingly, they act as the brains behind original trade ideas instead of limiting their work to market analysis. This is already the case in financial institutions and hedge funds, where some equities and bonds traders for instance are only expected to execute trades. In some cases, trading operations are even automated through digital tools, a trend that is expected to spread to commodities.
Exempt from the responsibilities of risk management and of handling a Profits & Losses portfolio, the time of an analyst is dedicated to analysing historical and statistical relationships, building models and identifying hidden patterns, imbalances and correlations between prices, traded products or assets. The purpose is to identify effective opportunities for arbitrage and spread trading.
Data a Game Changer
With more data transparency, demand for quantitative skills is only expected to rise. Since 2021, HC Group has seen unprecedented demand for analytics talent in both crude and refined products, the result of a perfect storm of hedge funds and trading houses ramping up and sophisticating their analytics capabilities at the same time.
Historically, tech companies and hedge funds pioneered the use of systematic and algorithmic trading to convert and integrate more complex and varied digital data into trading strategies. But the availability of Big Data through digitalization has been a game changer, especially for commodities where data transparency from governments and public sources has been scarce and is often released with a delay of up to several months.
For instance, through the availability of high frequency datasets, analysts can apply different machine learning algorithms to translate and structure data into an actual market signal. Mobility data can be gathered via live satellite images to track vessels and ships transporting specific products. Analysts can use designed algorithms to detect the ships on sea, count them and work out the number of floating storages on their own. Other types of mobility data, such as online flight bookings, can help forecast jet fuel demand from a given country instantly through machine learning even before public or national information is released – an exercise described as ‘nowcasting’.
In another example, Russia’s invasion of Ukraine and the resulting sanctions against Russian oil shipments mean traders need to quickly understand the movement of sanctioned ships and the behavior of Russian producers through the processing of key datasets at a granular level.
Skillsets in Demand
Analysts are now expected to come with strong statistical background and Python coding skills in order to extract data and build more complex and complete automated models using artificial intelligence methods.
The growing appetite for digitalization across commodities has led the industry to focus its hiring efforts on technical profiles like quantitative analysts, developers, data scientists and engineers. Many have come from tech companies like Google and Amazon, though with little knowledge of commodities and supply & demand fundamentals. Conversely, those with fundamental profiles must elevate their data skills in order to be competitive.
Both sets of skills are equally needed and valued. Making sense of any hidden data patterns or imbalances can only be done through a fundamental point of view. For instance, identifying sanctioned shipments from Russia requires the knowledge of Russian upstream production, infrastructure including storage and pipelines, etc.
In fact, halfway between traditional fundamental analysts and data experts, the combination of both commodities knowledge and quantitative skills has led to the emergence of a new type of analysts tagged as ‘quant-amental’, a term referring to the investment strategies that combine quantitative approaches using computers, mathematical models, and big data with fundamental analysis.
But these are in short supply. To address the shortage of well-rounded individuals, employers can only invest in internships and trainings in supply & demand knowledge or in coding and other quantitative competencies that they can overlay to their methodologies.
In addition to the ability to formulate trading ideas and strategies, analysts need the softer skillsets to effectively convey these ideas and defend them in an independent manner to the trading bench that they support. These include strong interpersonal, commercial and communication skills as well as taking initiatives proactively to match the growing expectations from analysts as shapers of trading strategies.
Such profiles are challenging to find given the relative inexperience of the current talent pool. “Some trading houses are struggling to find analysts who are self-starters and who can operate in that environment. They can do the analysis. But are they proactive and creative in an environment where no one is going to tell them what to do?” says Jamie Tranter, Portfolio Director at HC Group’s Liquid Fuels practice in the United States.
Despite the growing momentum for digitalization and Big Data, many nuances persist. The proportion of fundamental and quant skillsets still differs from hedge funds to physical players.
Commodities merchant companies with physical positions are following in the footsteps of funds and trading houses in sophisticating their trading tools. Despite some reluctance from traditional oil and gas companies to embrace digitalization and the use of data as an asset, demand for such specialized analysts is expected to keep up. But on the flip side, there are limits to how much advanced data techniques can apply especially when it comes to structuring untidy and disparate sets of data that still need vetting and interpreting. Some still see a big distinction between Big Data and automated tool models and traditional fundamental analysis methodologies to help inform supply and demand balances.
Fundamental market and economic expertise remain central to the role in addition to the ability to promote data-based views into a palatable trading strategy. With markets becoming increasingly complex, providing accurate price forecasting using strictly quantitative tools is insufficient to understand the impact of unprecedented supply and demand drivers such as the global coronavirus pandemic and more recently Russia’s invasion of Ukraine.
With government policies, geopolitics, and global supply chain disruptions expected to impact price movements further across commodities, analysts are required to think broadly and critically across a broad range of events in a shorter amount of time. The quicker pace of market and price movements combined with unparalleled disruptions mean analysts often need strong expertise – including associated specialists' network – to be able to factor in new variables into their models with authority. – FS