ENE431 Shipping Economics and Analytics
Shipping has seen tremendous advancements thanks to the influx of data from various sources. From operational to weather data, the possibilities are endless. By taking this course, you'll have the opportunity to dive into new research fields and cutting-edge business models that take advantage of this data.
Our goal for this course is to provide a comprehensive understanding of shipping economics, with a focus on practical applications. We'll achieve this by immersing students in a practical environment, where theoretical concepts will be interpreted with real data.
The following topics will help in reaching such goal:
- Introduction to models and data-driven analytics for shipping markets
- Big data for shipping commercial, operational and environmental problems
- Commercial contracts for ships and the functioning of the chartering markets.
- The differing market structure, competition and business strategies in selected shipping sectors
- Business risks and risk management in shipping
- Regulatory and environmental issues in international shipping
- The financing of shipping assets
Upon successful completion of the course, the candidate
- understands the economic mechanisms driving the international shipping markets.
- is familiar with recent development in data-driven analysis applied to the freight markets and ship operation.
- is conversant on technical aspects of shipping digital platforms
- understands how to apply advanced economic models and concepts in international shipping
- finds, synthesizes, and presents information on the international shipping
- can apply economic theory to varied strategic issues and practical problems facing shipping companies
- considers the economic, political and ethical issues relevant to the shipping industries
- can communicate with industry practitioners using correct terminology
- communicates problems, methods and solutions from the analyses both in writing and orally
- translates statistics into managerial insight
- exchanges opinions and experiences with others with a background in the field
About 30% of many of the lectures will be devoted to a mini case study to be solved in groups.
Hands-on sessions and offline tutorials with shipping big data will help in preparing for the group assignment.
Familiarity with a programming language is not necessary but will be helpful that at least one group member for the group assignment has some basic knowledge of Python, R, or Julia. The course includes some walkthrough sessions and offline tutorials for basic programming coding in Python for shipping Big Data before the group assignment.
Background knowledge in finance (discounting and net present value, options), microeconomics (supply and demand functions, elasticities), and statistics (probability distributions, expectation, standard deviation, variance, and regressions). If you need some extra help remembering these concepts or just a basic understanding to get started at the beginning of the semester, the instructor will upload additional materials to assist you.
Credit reduction due to overlap
A group-based essay must be submitted and approved (English only).
The course is assessed in three parts: a group-based assignment with 30% from an essay and 20% from an oral presentation, and a written 4-hour individual home exam (50% of the final grade). The group assignment (groups of a minimum of two students and max four students) includes an essay and a presentation to the class on one of a list of prescribed topics of the shipping markets. The language of the exam is English only.
Students will work on the essay for 4 weeks (submission deadline: October 24). Presentations will be covered in 3 to 4 days (depending on the number of groups), starting from week 43.
If you wish to retake an exam, you have to retake both the group assignment and written exams in the same semester.
PC: Word, Powerpoint, Excel,
Programming knowledge is not necesssary. However, walkthroughs Python will be used for the labs. R or Julia are also accepted.
Selected academic articles to be made available through Canvas/Leganto.
- ECTS Credits
- Teaching language
Autumn. Offered Autumn 2023.
Assistant Professor Gabriel Fuentes, Department of Business and Management Science