Digitisation is on the increase in large commodities houses seeking to boost margins through employing algorithms and working with data scientists. The move comes as commodity traders have been seeing a drop in return on equity in recent years, as their information edge is blunted causing pressure on margins.
Digitisation has been used in other markets, such as equities, currencies and interest rates for many years, with traders utilising algorithms, artificial intelligence and machine learning to help create successful trades using data.
Lenn Mayhew Lewis is director of Core Capital Advisors, which uses state-of-the-art algorithms backed by direct market access and a robust trading platform to facilitate trading in exotic currencies. Commodities traders are increasingly finding a need to harness data at the same level as other traders to regain footing over the competition. The PDF attachment looks at what a commodity is and how it is traded.
Data Science for Commodities Trading
One result of this shift is that large commodities houses are increasingly looking to hire data scientists to help them find and exploit the right information to be able to turn data into successful strategies for trading. To increase profitability, data needs to be extracted and used to create algorithms, which requires not just the technology but also the data experts who can utilise that technology to glean the best results.
Companies in general are estimated to be spending 300% more on artificial intelligence than they were in 2016. Quantitative trading is big business for data scientists, some of whom are drawing down six and seven-figure salaries when at the top of their game. Demand for ‘algo-traders’ is high across the finance industry. However, compared with other industrial and financial sectors, the commodities market is coming from behind in terms of digitisation at present.
Return on Equity for Leading Commodities Houses
The world’s leading commodities houses have seen significant decreases in the return on equity for shareholders in the commodities markets in the past few years. Metals and oil traders are looking at returns somewhere in the mid-teens at present, compared to more than 50% in the mid-2000s. Agriculture – which historically has always seen lower returns than other commodities – has also seen a drop, with leading companies recording ROE in the single digits in recent results. The infographic attachment looks at some of the commodities forecasts made by the World Bank for 2018.
Increases in Automation
The US Commodity Futures Trading Commission released a report in 2017 which showed that on CME’s futures exchange, the percentage of crude oils contracts that were automated between 2012 and 2016 had risen from 54% to almost two-thirds. Precious metals contracts saw a climb in automation from 46% to 54%, while automated contracts in wheat and soybeans rose from 39% to almost 50%.
Internal Resistance to Data Sharing
One of the biggest issues currently facing large commodities houses in the shift towards digitisation is internal resistance to the data sharing required to generate successful digital systems. The paradigm in commodities houses involves each individual desk being in charge of its own accounts for profit and loss, which leads to data being jealously guarded even from close colleagues. The move to share data is therefore being strongly resisted by many. Another issue is that many houses use a variety of separate systems and technology platforms, meaning that data scientists are first having to focus on system harmonisation before they can begin using the digital infrastructure to bring data in from different areas.
In the short video attachment, you can learn about using commodities trading for risk management and portfolio diversification.