Agricultural Supply and Demand Forecasting

CM Navigator | Historical Commodity Market Data: In the complex world of commodity trading, the saying "history repeats itself" serves not merely as a philosophical thinking but as a foundational principle that supports investment and trading strategies. This is particularly relevant in the context of market cycles, which are known for their tendency to exhibit patterns and behaviours that recur over time. By closely examining past market trends and cycles, traders and investors can accumulate insights into potential future movements, allowing them to make more informed decisions. This article dives into the value of historical data analysis for forecasting market trends. Historical data proves to be an indispensable tool for those who want to stay ahead of the dynamic movements of commodity- and freight markets. The strategic use of historical data in commodity trading is crucial for portfolio diversification, risk management, and the timing of market entries and exits. It enables traders and investors to utilize past market responses to various stimuli—such as economic cycles, policy changes, and supply disruptions— allowing them to effectively understand the correlation to market variables such as commodity futures, cash prices, and freight markets and thus it provides a solid foundation to analyse these market dynamics. The impact of geopolitical events on commodity prices is a key example of historical data's relevance. As an example, oil markets are significantly affected by geopolitical tensions in the Middle East, where production disruptions have historically led to sharp increases in global oil prices. By analysing episodes like the oil shocks of the 1970s or more recent conflicts in oil-producing regions, analysts can build models to better anticipate the effects of similar future incidents on commodity markets (Carter, Rausser & Smith, 2011). Furthermore, the analysis of historical freight rates serves as a critical component of the dry bulk commodity trading equation, providing insights into broader economic and logistical trends. These insights reflect the multifaceted influence of numerous factors on the markets and underscore the importance of historical data in uncovering supply-demand correlations, price elasticity, political impacts, and market sentiment over time (Barkoulas, Hu & Santos, 2008). Predictive modelling in commodity trading exceeds mere speculation, anchoring itself in the systematic interpretation of correlations of historical data. The integration of historical data allows statistical models, machine learning algorithms, and other analytical tools to uncover trends that might otherwise not be immediately apparent. Historical variables might include lagged prices, historical volatility indices, and even sentiment analysis derived from news archives. The latter approach enables the formulation of forecasts hypothesises that predict market behaviour with a higher degree of accuracy than traditional methods, as proved by Pai, Hong, and Lin (2018) in the realm of stock price forecasting. Traditional statistical modelling techniques, such as Linear Regression and Autoregressive Integrated Moving Average (ARIMA) models, have long been central in forecasting commodity prices. Linear Regression offers a straightforward approach by establishing linear relationships between variables, while ARIMA models capture the temporal dependencies and seasonality inherent in time series data. These methods have demonstrated efficacy in predicting price movements across a wide array of commodities, providing valuable insights for traders, investors, and policymakers alike. However, the advent of Artificial Neural Network (ANN) architectures has ushered in a new era of predictive modelling, offering unparalleled capabilities in capturing intricate patterns and nonlinear relationships within data. Architectures such as Long Short-Term Memory (LSTM) networks and Feed Forward Neural Networks (FFNNs), have emerged as powerful tools for forecasting grain yield and prices. (Liakos et al., 2018) However, recognizing that no model can guarantee perfect predictions, traders often employ a combination of methods to enhance forecast reliability. Fundamental analysis, which examines supply and demand factors, economic indicators, and other tangible data, complements these econometric models. By integrating various methodologies, traders can form a more holistic view of the market, preparing for a range of potential outcomes and mitigating the inherent risks of relying on a single forecasting approach. The strategic analysis of historical cash prices and freight rates is indispensable in the quest to forecast future movements in commodity markets. This comprehensive approach not only facilitates a deeper understanding of market dynamics but also equips traders and investors with the insights needed to navigate market volatility successfully and seize emerging opportunities. As commodity markets continue to evolve, the thoughtful use of historical data will remain a cornerstone of adjusting trading strategies, highlighting its value in an ever-dynamic economic and geopolitical landscape. The meticulous analysis of historical data is not just a technical exercise but a strategic imperative that leverages patterns of the past, providing a foundation upon which future decisions of commodity trading strategies are made. Barkoulas, J., Hu, A. & Santos, M.R., 2008. The Link between Commodity Prices and Commodity-Linked-Equity Values during a Geopolitical Event. Academy of Accounting and Financial Studies Journal, 12, p.1. Pai, P.-F., Hong, L.-C. & Lin, K.-P., 2018. Using Internet Search Trends and Historical Trading Data for Predicting Stock Markets by the Least Squares Support Vector Regression Model. Computational Intelligence and Neuroscience, 2018, p.6305246. Bouoiyour, J., Selmi, R., Hammoudeh, S. & Wohar, M., 2019. What are the categories of geopolitical risks that could drive oil prices higher? Acts or threats? Energy economics, Vol.84, pp.1-14 Carter, C., Rausser, G. & Smith, A., 2011. Commodity Booms and Busts. Annual Review of Resource Economics, 3, pp.87-118. Liakos, K.G.; Busato, P.; Moshou, D.; Pearson, S.; Bochtis, 2018 D. Machine Learning in Agriculture A Review. Sensors 2018, 18, 2674.

CM Navigator | Transparency in Dry Bulk Freight Markets: Traditionally, the shipping markets are characterised by a high degree of opacity and exclusivity, predominantly governed by a limited number of shipowners situated in e.g. Greece, Monaco, or Denmark, distant from the demand centres for freight services. Considering the inelastic nature of vessel supply from an economic perspective, it is advantageous for vessel owners to prefer pricing strategies based on an individual transaction basis. This approach enables shipowners to fully gauge the extent of market demand. Consequently, this method of price discovery has historically been in the favour of the freight sellers. Advancements in technology have facilitated a paradigm shift in the methodology of freight pricing. Increased transparency within the dry bulk freight markets is transforming the dynamics of the physical commodity markets. Innovations such as new freight calculators and comprehensive freight- and CFR matrices, designed specifically for the use of freight buyers, are changing the operational strategies of commodity traders. These tools have been instrumental in reducing the time required for price discovery and thus influencing the pace of the decision-making processes. This blog post explores the implications of this augmented transparency, concentrating on its impact on decision-making, risk management, market efficiency, liquidity, and the technological innovations driving these changes. In the world of commodity trading such as grains, fertilizers, cement, steel etc, the cost of freight constitutes a significant component of the CFR price and is often the deciding element of the total cost which determines the most competitive origin for a given destination. Examples of this include the transportation of corn from Brazil to China, where freight costs are approximately 20% of FOB cost (200 USD/PMT FOB and 40 USD/PMT) freight, or cement clinker from Turkey to West Africa where freight is around 50% of FOB cost (50 USD/PMT FOB and 25 USD/PMT). Historically, acquiring accurate freight rates has been a complex and cumbersome process, characterized by multiple stages, discretionary pricing, and considerable delays. This has typically represented a notable information scarcity for most commodity traders without extensive in-house freight departments. However, the introduction of online platforms providing access via websites or API to real-time data on dry bulk freight rates has started to even out the competitive landscape. These platforms provide commodity traders with comprehensive insights into the constantly moving freight markets, covering all major deep-sea routes on a global scale. A dry bulk freight matrix or calculator empowers traders with the capability to instantly evaluate shipping costs, thereby facilitating the comparison of Cost and Freight (CFR) prices to determine competitiveness at the destination. Consequently, this leads to noticeably faster and more accurate cost calculations. The capacity to programmatically examine hundreds of freight combinations changes the role of the trader, reducing the amount of time devoted to price discovery and increasing the focus on market analysis and the making of strategic decisions. The freight markets are characterized by volatility, a consequence of the inelastic nature of supply. Instances, where an oversupply of ships is observed for one week, can rapidly shift to a shortage, shortly after. An illustrative example of this volatility can be observed in the East Coast South America (ECSA) to the Mediterranean trade routes for handysize grain trades. Taking November 2023 as an example, there was a 100% increase in vessel hire rates from the beginning to the end of the month. In general, freight market volatility has increased in recent years thereby creating substantial trading opportunities. For instance, comparing the standard deviation of freight rates for Handysize vessels in 2019 and 2023, considering these years to approximate 'normal' conditions outside of the COVID-19 pandemic, reveals this increase in volatility; the Handysize Baltic Index recorded a standard deviation of 1873 in 2019 compared to 2261 in 2023, indicating a 20% increase in volatility. During the COVID years, the yearly standard deviation surged to 7355, marking a 392% increase in volatility. There is a tendency that larger vessel types show greater volatility, implying that larger standard deviations are to be expected for these vessel sizes. The incorporation of advanced analytics into an organization's proprietary data models enables commodity traders to refine their strategic approaches and enhance the accuracy of predictions, particularly for short-term forecasts applied in trading within the commodity futures market, where efficiency is critical. McKinsey & Company's report, "The Future of Commodity Trading," posits that the application of granular data in the realm of commodities trading has the potential to amplify revenues and profitability by leveraging short-term market inefficiencies. These technological innovations enable traders to make informed decisions quickly, enhancing their ability to manage risks associated with price volatility and shipping costs. Such technological advancements and increased price transparency can lead to greater liquidity. This is attributed to the lowering of barriers to market entry and the fostering of a more competitive trading landscape, as improvements in price discovery mechanisms and overall market efficiency are achieved. As the commodity trading landscape continues to evolve, the significance of technology in enhancing transparency and operational efficiency is becoming more pronounced. The availability of real-time freight information through advanced online platforms exemplifies the transformative impact of technological innovations on the industry. Looking ahead, it is anticipated that the integration of machine learning and data analytics will intensify capitalizing on the growing volume of data accessible in shipping and commodity markets in the future. The trend towards increased transparency within dry bulk freight markets, driven by technological innovations, is transforming the physical commodity trading industries. Instant access to critical freight rates has not only enhanced market efficiency but also improved risk management and facilitated more informed decision-making among commodity traders. As technology continues to play a central role in the evolution of the industry, the potential for innovation remains extensive, promising a future where market participants can operate with exceptional insight and efficiency. McKinsey & Company. (n.d.). The future of commodity trading McKinsey & Company. (n.d.). Data mining for miners: Using analytics for short-term price movement forecasting