The Impact of AI on Commodities Trading

The significance of commodities markets cannot be overstated. Despite appearing as a distant realm filled with specialised terminology within the realm of high finance, these markets play a crucial role in establishing prices for the fundamental materials required in the production of nearly everything globally. 

Whether a company specialises in manufacturing automobiles or confectionery, the prices determined in these markets for essential resources such as steel or cocoa exert a substantial influence on their operations.

Thus, if you possess shares in companies engaged in physical production or hold a pension tied to such companies, fluctuations in these markets directly affect you. Moreover, when commodities markets experience price hikes, leading to increased production costs, these expenses are often passed on to consumers. 

Therefore, every time you make a purchase involving goods rather than services, you are impacted by commodities markets. In this article, we’ll be looking at how AI is impacting the commodities trading market, and how it impacts stakeholders.


How Is AI Influencing Commodities Trading Patterns?


The emergence of Artificial Intelligence (AI) in shaping trading activities warrants attention. Although some of the speculative narratives surrounding AI might seem exaggerated, the significant influence of AI on commodities trading poses challenges for the United Kingdom’s corporate sector.

AI’s impact on commodity trading stems from its ability to rapidly process information, analyse extensive datasets to identify familiar patterns, and promptly act upon them. This aligns perfectly with the dynamics of commodities markets, which are influenced by a multitude of factors and the voluminous data describing them.

Hedge funds and banks have long recognised the competitive advantage AI could provide, sparking an ongoing AI arms race as they vie to adopt approaches that keep them ahead. However, the reliance on similar datasets among these entities often leads to parallel conclusions and swift, collective actions, contributing to herd-like behaviour that amplifies market fluctuations.

For instance, consider the significance of a crop report on wheat from the USA, which serves as a pivotal indicator for global wheat prices. Previously, analysts would spend hours or even days scrutinising such reports. Today, machine learning algorithms apply natural language processing to swiftly recognise patterns in these reports, correlate them with historical data and other alternative datasets like satellite imagery and weather reports, and execute trades within fractions of a second.

How Does AI Impact Market Volatility?


This shift from varied response times and thoughtful analyses to instantaneous reactions among market participants contributes to heightened volatility in commodities markets.

The concern isn’t merely that hedge funds are leveraging AI, as they often deliver returns for clients, including pension funds. Rather, the issue lies in the volatility that benefits financial entities while posing challenges for tangible industries.


Are There Any Advantages to AI in Commodities Trading?


Based on the evidence, it does seem as though AI is wholly damaging to the commodities trading market. However, there is a silver lining: AI also helps identify opportunities to capitalise on market movements, facilitating frequent trading that enhances liquidity. This liquidity, in turn, enables companies to procure necessary materials precisely when needed, at lower costs.

Furthermore, as AI technology becomes more accessible, its benefits extend beyond early adopters in financial hubs to encompass the broader economy, presenting untapped potential for optimising procurement practices within UK businesses.


Is AI Entirely To Blame for Market Volatility?


Blaming Artificial Intelligence entirely for historical volatility overlooks the inherent unpredictability of commodities markets, reflecting the volatility of our world. The primary concern with AI lies in failing to harness its potential benefits in the real economy amidst apprehensions. Currently, financiers have embraced AI in commodities trading, whereas the corporate sector lags behind. As the future unfolds, disparities in AI adoption continue to persist.