Strictly for students

Research Question

How can an integrated economic framework be developed to evaluate the supply, demand, financial viability, and risk factors, including geopolitical risks, for newbuildings of Mini-Bulkers (10,000 to 25,000 dwt) across three key shipbuilding markets: India, China, and Japan?

Problem Description

The market for new Mini-Bulkers is influenced by complex supply‐and‐demand dynamics. On the supply side, factors such as existing orderbooks, shipyard capacities, and regional production efficiencies play a critical role. On the demand side, global trade flows drive the need for these vessels. However, significant differences in shipbuilding financials exist across India, China, and Japan, where construction costs, financing structures, and governmental policies vary considerably. Risks, including exchange rate volatility, regulatory changes, and geopolitical tensions, add layers of uncertainty that challenge traditional economic modelling in this sector, this must be analyzed. Furthermore, historical demand and pricing data for Mini-Bulkers is limited hence students must invent a modelling approach that can assist them in answering the research question.

Impact

Developing a comprehensive framework will enable CM Group as shipowners/investors, to make informed decisions on whether to buy, and where to buy. 

Data

CM Group will supply newbuilding pricing, supply, and projected demand data for larger ships, including handysize, supramax, panamax, and capesize vessels, across each shipbuilding market. In addition, historical trade flow data for minibulkers (10,000 to 25,000 dwt) and global macroeconomic data will be provided. Students are expected to source any additional information from the Clarksons Research World Fleet Register.

Research Question

How can a user-facing frontend application be designed and developed to support one of CM Navigator’s core algorithms, enabling real-world decision-making in commodity trading and freight markets? The thesis should explore how user needs, pain points, and behavioral patterns can inform interface design, and result in a deployable solution that fits as a page within CM Navigator.

Problem Description

Copenhagen Merchants Group has developed several powerful algorithms that solve relevant optimization and forecasting problems in agricultural shipping and commodity markets. These include models for freight premium prediction, grain basis premium modelling, grain import/export matchmaking, vessel allocation, and bunker optimization. While the backend logic is validated, these tools currently lack a usable, production-grade interface and the link in between. Without this, internal and external users cannot interact with the models effectively. The challenge is therefore to bridge the gap between algorithm output and business use by creating a frontend solution and the link between that is technically sound, user-friendly, and responsive to real user workflows. The design process should include persona development, workflow mapping, prototyping, and implementation in a modern frontend framework. Technology Stack is React, TypeScript, Tailwind CSS and similar modern web frameworks.

Impact

A successful solution will allow commodity traders, freight analysts, or operations teams to directly access and use complex algorithmic insights in their daily workflows. It will improve transparency, speed of decision-making, and user satisfaction. From a business perspective, the result will strengthen the commercial value of CM Navigator, helping convert backend intelligence into customer-facing functionality. For the student, this is a unique opportunity to build a real product that enters production and directly supports global trade in food commodities. The goal is to deliver a product that can be deployed within CM Navigator.

Research Question

How can the integration of complementary sub-models using statistical and machine learning techniques enhance the accuracy of shipping market price predictions in the face of big data, volatility, and geopolitical uncertainty?

Problem description

The traditional methods of predicting shipping market prices have become obsolete due to big data, market volatility, and geopolitical uncertainty. The aim is to enhance price prediction accuracy by integrating complementary sub-models—such as regional balance sheets, ballast dynamics, destination forecasts, congestion predictions, and dynamic demand data—using advanced statistical and machine learning techniques

Impact

Better models can improve the accuracy of shipping market price predictions. An advancement would address the largest pain point in global markets today, mitigating the limitations of traditional pricing methods that struggle with big data, volatility, and geopolitical uncertainty. Refined models would enable more precise regional balance assessments, better prediction of surplus ship movements, accurate vessel location forecasts, reliable congestion level estimations, and dynamic cargo volume allocations. Such improvements would lead to more efficient shipping operations, better resource allocation, and potentially reduced costs and increased profitability for stakeholders in the shipping industry.

Research Question

How can an integrated prediction model incorporating supply analysis, weather patterns, freight market dynamics, geopolitical events, real-time news feeds, futures market trends, and vessel lineups improve the accuracy of predicting basis premiums in agricultural commodity markets, thereby aiding stakeholders in making more informed hedging and purchasing decisions?

Problem description

Agricultural commodity markets are crucial for managing the supply chain and pricing of essential crops such as wheat, corn, barley, soybeans, and soybean meal. For agricultural end buyers, hedging through futures markets is a primary risk management tool. However, there are critical moments when these buyers must transition from financial hedges to physical purchases. The primary challenge in this transition is accurately predicting the basis premium—the spread between physical market prices and futures market prices.

Current methods for predicting basis premiums are often insufficiently precise, leaving stakeholders exposed to market risks and potentially suboptimal decision-making. This inadequacy arises from the complex and multifaceted nature of agricultural commodity markets, where numerous factors simultaneously influence price movements.

Impact

Developing an accurate prediction model for basis premiums in agricultural commodity markets will enhance decision-making, reduce financial risks, and optimize resource allocation for stakeholders. A good model will leverage advanced statistical and machine learning techniques to integrate various market indicators, providing precise and dynamic predictions. Ultimately, it can contribute to market efficiency, economic benefits, and global food security.

The CM Navigator is a software as a service (SaaS) platform. We see it as an essential tool for professionals working in freight and commodity markets, offering a range of features that streamline risk management, data analysis, and decision-making processes.

As students, you are invited to analyze the unique selling proposition (USP) of this platform, examining what sets it apart from competitors in a highly competitive market. Consider how the platform’s ability to integrate CM Group's complex data sources, provide real-time insights, and offer user-friendly interfaces may appeal to various stakeholders in the commodity industry.

Additionally, assess how CM Navigator enhances operational efficiency and drives smarter decision-making through its advanced analytics and customizable dashboards. Does the solutions on the platform offer unique advantages in terms of product market fit and/or user experience? You are encouraged to explore these dimensions and evaluate whether the platform delivers long-term value for its users. In your analysis, think critically about the problem-solving capabilities of CM Navigator and how it supports its target audience in achieving their business objectives.

(Master Thesis Projects only :-))