Shipping Economics and Analytics

ENE431 Shipping Economics and Analytics

Autumn 2022

  • Topics

    Shipping has largely benefited from an increasing quantity of data from different sources such as commercial, operational, weather, and others. This has derived in new research fields and new business models that exploit traditional shipping economics and the potential of data feed. Our goal for this course is that students could cover the theoretical aspects of shipping economics while immersed in a practical environment where theory is 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

  • Learning outcome

    Upon successful completion of the course, the candidate

    Knowledge

    • 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

    Skills

    • 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

    General Competence

    • 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

  • Teaching

    In person teaching as a default, unless established otherwise by the health authorities

    About 30% of each lecture will be devoted to a mini case study to be solved in groups

    Hands-on sessions with shipping big data will help in preparing for the group assignment

  • Recommended prerequisites

    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)

    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 with Python, R or Julia. The course includes some walkthrough sessions for basic programming coding in Python for shipping Big Data before the group assignment.

  • Required prerequisites

    None

  • Credit reduction due to overlap

    None

  • Compulsory Activity

    A group-based essay must be submitted and approved (English only).

  • Assessment

    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 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 days, 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.

  • Grading Scale

    A-F

  • Computer tools

    PC: Word, Powerpoint, Excel,

    Programming knowledge is not necesssary. However, walkthroughs Python will be used for the labs. R or Julia are also accepted.

  • Literature

    Selected academic articles to be made available through Canvas/Leganto.

Overview

ECTS Credits
7.5
Teaching language
English
Semester

Autumn. Offered Autumn 2022.

Course responsible

Gabriel Fuentes, Department of Business and Management Science