By Mrugank Paranjape
With the development of transparent markets for trading across multiple asset classes, from commodities to bonds to stocks, the data generated in the process in itself has immense value for decision making. It can be used in a variety of activities ranging from mining and production to manufacturing, alas outside of business decisions! Rightly, the British mathematician Clive Humby (2006) projected that “data is the new oil”. The data available from and for commodity markets today continues to grow at an unprecedented rate, thanks to the growing trend towards the digitization of the commodity value chain and the transparency it enables. Most of this data still remains unstructured and untapped. The refinement of this data gathered from the value chain through the use of data science and its use can be a game-changer in today’s competitive advanced commodity markets.
According to a Singapore government estimate, around 10 trillion dollars worth of commodities are produced and consumed annually around the world. Each commodity comes with its own set of challenges related to how it is produced / mined, refined / processed, traded, traded and managed in terms of digitization and value chain transparency. In addition to this, driven by supply and demand fundamentals as well as geopolitics and several other factors, volatility is the only constant in cyclical commodity markets, as are the risks associated with trading decisions of commodities. stakeholders. For commodity players who generally operate on low margins, price instability situations make risk management inevitable. The multi-billion dollar global commodities trading industry negotiates financial contracts with underlying commodities that are essential inputs for much of the manufacturing sector, such as crude oil, copper, cotton, etc. These markets not only provide early signals, but also facilitate risk management by them, and thus play a vital role in greasing the cogs of the economy.
For investment banks that support the healthy existence of these financial commodity markets, back office work is vital for the effective management of their positions and those of their clients in the markets. Gone are the days when back office work for managing positions in investment banks involved large capital expenditures and a long period of time. Position management can now be done transparently throughout the lifecycle of a traded commodity position using agile technologies such as big data, blockchain, machine learning, artificial intelligence (AI), robotics, etc., which allow efficient estimation of demand and price. oscillations. It is therefore imperative that we know these technologies and know how they can impact the world of raw materials.
Simply put, big data is literally a large or massive amount of raw, unstructured and unformatted data that is constantly updating to discover patterns and relationships, thus enabling decision makers to make the right decisions based on real-time analytical information. Big Data can be defined by its volume, variety and the speed of its collection. Its operation is likely to be the next frontier in technology essential for competition and efficiency. Here, “data” can mean anything from structured databases to written data, text, photos and videos, which would require specialized software for conversion into data points that can be used for decision making.
Price movements, changes in market cycles, new regulatory frameworks, etc., create millions of individual data points that can be processed to provide effective information in decision making not only for back managers. -office, but also for markets and can even provide sufficient policy inputs at the level of the commodity economy. In addition, effective use of this data can provide insight into market conduct and help commodity traders make efficient decisions about entry or exit points. The large-scale adoption of big data can provide competitive businesses in the economy and therefore the competitiveness of the economy itself.
An important contribution that comes from the creation of Bitcoin is the distributed ledger known as a ‘blockchain’, which, in simple terms, is a distributed database shared over a defined network. Every computer on this network has a copy of this database and every bit of information is mathematically encrypted and named a “block”, and a chain of these “blocks” validates not only transactions, but also the storage of it. underlying asset. A World Economic Forum survey (2015) suggested that 10% of the global GDP value of economic transactions will be stored using blockchain technology by 2027, including the transaction trail leading to their current ownership.
For commodities having a physical dimension and quality parameters that help to evaluate them, the adoption of blockchain in transactions and storage will improve the efficiency of the execution of commodity transactions and storage with the associated set of information. Blockchain has the ability to bring together all players in the commodities market to prevent fraud, eliminate third parties, accelerate clearing, thereby improving transactional efficiency and financialization while bringing operational efficiency to the value chain. In addition, blockchain-based transactions will improve regulatory filings and reporting by improving market transparency and auditability. The fact that the commodities industry has awakened to this potential has been visible in attempts to deploy blockchain on commodities / verticals such as electricity, diamonds, food and oil. Large trading houses such as Gunvor, Koch, Trafigura and Mercuria have started trials using blockchain technology to settle their back office transactions.
AI and machine learning
Machine learning or AI, by definition, is the implementation of computer software capable of learning independently. Machine learning and AI can offer new opportunities to improve process performance and achieve significant savings for market players. To achieve the end goal, AI / machine learning uses structured big data and learning by linking patterns to fundamentals and price movements with an appropriate level of noise reduction and standardization, thereby improving the process of decision making and thus improving the efficiency of commodity companies that use AI / machine learning. A predetermined logic based on AI / machine learning will allow traders to make instant decisions on commodity curves and settle trades, thus improving transactional efficiency in the markets.
The path to follow
With the help of the emerging trend of digitalization of commodity transactions and storage, the industry can take a big step forward if the same data can be collected and appropriately mined using big data and AI / machine learning. The increasing adoption of blockchain in the storage and transaction of physical products would not only improve transactional efficiency in markets, but also generate adequate data to use for corroboration with other big data to help AI. / machine learning to increase the efficiency of market-based financial transactions. . As each of these technologies are interdependent on each other to bring about a global transformation in the underlying markets, it is essential to have a political regime that not only financially supports the developers and users of the technology, but also provides a supportive policy environment in terms of its implementation. In addition, public institutions in raw material storages would also be actively encouraged to take advantage of digitization and also provide increased transparency for effective decision making in the markets. As in society, harnessing the strengths of the digitization of commodities will help us embark on the path of becoming “price fixers” in global markets.