Commodity trading is the process of buying, selling and trading primary economic sector commodities on exchange platforms. These products, including agricultural goods, metals, energy sources, etc., form the backbone of the global economy. The prices of the major commodities are driven by economic growth, geopolitics, monetary policies, currency, weather and financial investments. In the past few years, the seventh pillar – technology – has significantly contributed to the growth of the global commodities trade. The convergence of commodities and Artificial Intelligence (AI) has resulted in reshaping the global economic landscape as it becomes the driver of efficiency and profitability for traders.

The Indian commodity market has been around for over a hundred years, but it became officially recognised through a legal trading system in 2003. Currently, the total worth of India’s commodity market is an impressive ₹92.23 lakh crore. However, for a long time, Indians have refrained from actively participating in trading due to the risk factor arising from market volatility. In the past few years, with technological advancements, Indian traders have become more confident about their decision-making in trading. In fact, three million new Demat accounts were opened in July 2023, taking the total number to a whopping 123.50 million, indicating a sharp increase in the number of retail investors in the country.

As more people enter the trading and investment landscape, traders expect trading platforms to provide advanced tools and services that amplify their decision-making skills, especially in the commodities market. Commodities markets are the playground for volatility, with price fluctuations which affect trades by the second. This can be overwhelming for the lesser experienced traders who need to make split-second decisions to keep their risk-return fundamentals in place. This is where technological advancements in commodities trading take centre stage. AI-powered advanced tools like predictive analytics enable investors to assess risk and predict future movements based on historical data. A recent example is the vegetable price hike, wherein the markets were extremely volatile, where investors could leverage smart tools to trade in the market.

With the fusion of AI and predictive analytics, traders get lucrative opportunities to overcome the challenges in trading commodities.

The advancements in AI have led to the creation of innovative solutions that address the challenges and complexities of the commodities market. Leveraging natural language processing and machine learning, AI is revolutionising how people approach commodities trading. It has proven instrumental in building risk management models that undertake a variety of factors. Traders can leverage these models to optimise their portfolios and manage their strategies according to the changes in the market. Furthermore, AI-powered algorithms also enable traders to execute trades at lightning-fast speed, as required in the market while trading. It increases the scope of lapping up opportunities with a minimal chance of making an error.

Predictive analytics are driven by AI and have emerged as a powerful tool for forecasting commodity prices. It enables market participants to make informed decisions driven by data-based insights. In predictive analytics, vast amounts of historical data are analysed to identify patterns. Further, machine learning uses the same to predict the movement in future prices.

The nature of commodity markets is such that it experiences significant price fluctuations due to global events, weather patterns and geopolitical tensions. But using pre-deal analytics, traders can assess potential trades before implementing them. It involves creating virtual models of various situations and using intelligent estimates of future movements to understand how a trade might affect profitability. Predictive AI enables traders to simulate different scenarios, providing a detailed forecast for each trade’s possible outcome.

AI can identify irregularities and trends in new trades during the trade validation stage by comparing them to historical trading patterns. This effectively uses AI’s ability to recognise patterns and analyse large amounts of data to identify anomalies. With AI, trade irregularities can be recognised either right after a trade is initiated or during the confirmation period. Importantly, this offers the chance to reverse such trades if necessary.

The amalgamation of AI and predictive analytics had led to the enhancement of the predictive capabilities in the commodity market, like enhanced accuracy and precision in predictions. AI has raised the potential of predictive analytics, where traders leverage the same to maximise returns. AI also mitigates the risk that arises due to human bias or error. This technology has paved the way for faster decision-making by keeping traders updated with the latest information.

The future of AI and predictive analytics promises advanced applications in commodities trading. Even though it is hard to predict the exact market movements, the fusion of AI and predictive analytics would offer enhanced risk management techniques and efficient market predictions by using sophisticated trading algorithms. The progress would disrupt the human-centric approaches in trading. As we anticipate the opening up of more precise trading strategies, it wouldn’t be wrong to say that AI and predictive analytics would become essential tools for commodities trading.

This article is authored by Sarvjeet Virk, co-founder and managing director, Finvasia.

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