C15 Pension Finance
C15 Pension Finance
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Finance
Project Description
Life-Cycle View of Personal Finance
Defined Benefit vs. Defined Contribution Plans
Instruments Typically Used in Pension Finance
Models for Integrating Financial Risks with Other Risks like Longevity Risk
Defined Benefit vs. Defined Contribution Plans
Instruments Typically Used in Pension Finance
Models for Integrating Financial Risks with Other Risks like Longevity Risk
Teaching Method
Interactive lecture with exercises
Learning Objectives
After completing this lecture, students have gained a solid background in financial decisions regarding longevity and retirement. They know how to combine valuation techniques from finance with those from actuarial mathematics, and understand the effects of longevity, bequest motive, and risk-return tradeoff on the corresponding choices made by individual investors.
Learning Results
Understand and apply life-cycle models of individual investors
Distinguish between DB and DC pension plans and understand their respective implications
Combine financial models with models for longevity risk
Select appropriate financial instruments for individual pension planning and justify this selection economically
Distinguish between DB and DC pension plans and understand their respective implications
Combine financial models with models for longevity risk
Select appropriate financial instruments for individual pension planning and justify this selection economically
Assessment Methods
See lecture within the module.
Examination
Grading will be based entirely on the final exam.
C15 Workshop Investment Banking
C15 Workshop Investment Banking
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Finance
Project Description
- Definition of Investment Banking
- Going Public, IPO`s
- The Cost of Being Public and Going Public
- Underpricing and Underperformance
- Managing the Process of Underwriting
- Merger and Acquisitions (Merger Motives, Categories of M&A, Due Diligence)
- Post-Merger Integration
- Defence Measures
- The Role of Investment Funds in Investment Banking (Hedge Funds and Private Equity Funds, ETFs)
- Stock Repurchase Programmes
Teaching Method
Interactive lecture with cases, business-scene simulations.
Learning Results
After successful completion of this module, students
- are able to evaluate initial public offerings;
- know how to illustrate responsibilities for capital limited by shares;
- can evaluate motives for mergers and acquisitions;
- know how to classify procedures of mergers and acquisitions;
- are able to determine defence measures for undertakings;
- can describe and comment the role of investment funds (incl. Hedge Funds, Private Equity Funds, ETFs) in investment banking;
- are able to evaluate reasons for share buyback.
Assessment Methods
See lecture within the module.
C15 Corporate Governance and Ethics
C15 Corporate Governance and Ethics
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Finance
Project Description
Ethics Essentials:
The course in Corporate Governance covers:
- Theory, Principles, and World-Views
- The Ethical Leader: Self-Mastery and Ethics, Mind-Sets
- Corporate Ethics: Shared Values, Professionalism (as part of Standards of Professional Conduct)
- CFA Institute Code of Ethics and Standards of Professional Conduct
- GARP Ethics and Code of Conduct
The course in Corporate Governance covers:
- Role and Responsibility of Owners
- Practice of Right of Control for Various Actors
- The Problem of Free Riding
- Major Investors
- Conflicts of Interest Between Financiers
- Conflicts of Interest Between Stakeholders
- Financial Remuneration of Managers
- Alternative Designs for Option-Based Remuneration Structures for Managers
- Role and Responsibility of Institutions (Accounting, State)
- Corporate Governance in Financial Services Undertakings
- Optimal Design for Corporate Governance
- Empirical Investigations of Corporate Governance
- The role of Risk Management in Corporate Governance
Teaching Method
Interactive lecture with discussion, cases.
Learning Results
Ethics:
Corporate Governance:
Students:
- Understanding the origins, discipline and business case of ethics
- Discussing defined moments such as ethical dilemma in the corporate context
- Familiarizing with the standards of professionalism in particular
Corporate Governance:
Students:
- illustrate the role and responsibility of corporate owners
- explain the right of corporate control
- describe the problem of free riding
- identify the special characteristic of major investors
- describe conflicts of interest
- scrutinise methods of remuneration of managers
- describe and critically evaluate differing designs for option-based remuneration structures regarding their effect
- demonstrate the role and responsibility of the board of directors, a company’s audit committee and the state
- explain the special characteristics of Corporate Governance in the undertakings of financial services
- develop optimal Corporate Governance structures
- contrast empirical investigations of Corporate Governance
- compare and contrast best practices in corporate governance with those of risk management
- evaluate the relationship between a firm’s risk appetite and its business strategy
- distinguish the different mechanisms for transmitting risk governance throughout an organization
- illustrate the interdependence of functional units within a firm as it relates to risk management
Course Materials
Lecture slides will be available on Moodle
Assessment Methods
See lectures within the module.
C15 Investment Strategies and Asset Management
C15 Investment Strategies and Asset Management
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Finance
Project Description
Investment Strategies by Asset Class: Equitiy, Fixed Income, and Derivatives Strategies
Investment Strategies for Different Economic Environments
Asset Management in Practice
Investment Strategies for Different Economic Environments
Asset Management in Practice
Teaching Method
Paper-based preparation of topics, strategy implementation and testing, presentation and discussion
Learning Objectives
After completion of this module, students know about practical aspects of asset management, in particular various investement strategies. These strategies can be categorized by asset class and/or particular (macro-)economic environments for which they are particularly suitable.
Learning Results
Understand exactly how investors try to achieve profits using different asset classes
Know the state of the art regarding risk/profit drivers in financial markets
Devise, justify, and test investment strategies for different economic environments (inflationary/deflationary, expansion/recession,...)
Know the state of the art regarding risk/profit drivers in financial markets
Devise, justify, and test investment strategies for different economic environments (inflationary/deflationary, expansion/recession,...)
Assessment Methods
See lecture within the module.
C15 Educational Journey 2017 to Hong Kong and Singapore - The Future of global Wealth Management: Connections between Europe and Asia
C15 Educational Journey 2017 to Hong Kong and Singapore - The Future of global Wealth Management: Connections between Europe and Asia
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Finance
Project Description
Master students have the opportunity to take part in educational journeys to the world’s most important financial centres. Taking place
Teaching Method
Excursion
Learning Objectives
After successfull completion of this module, students
- know the role of international enterprises and organizations including banks, asset or hedge fund management services, portfolio managers, insurance companies, chambers of foreign trade, chambers of commerce, supranational organisations, ambassadors, politicians or universities in structuring the competitiveness of a financial market;
- know the specifities of the visited financial market;
- established an international and intercultural network.
Assessment Methods
See lecture within the module.
C15 Master's thesis
C15 Master's thesis
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project Description
Short description
In their Master’s thesis, students use scientific methods and work in accordance with standards of scientific writing. The Master’s thesis is typically related to the major (BPM or Data Science) chosen by the student.
Learning objectives
Methods
Entry requirements
Recommended previous knowledge
Colloquium
Submissions and deadlines
Compulsory reading
In their Master’s thesis, students use scientific methods and work in accordance with standards of scientific writing. The Master’s thesis is typically related to the major (BPM or Data Science) chosen by the student.
Learning objectives
- Students will formulate appropriate research questions.
- Students will identify appropriate theories to explain empirical phenomena.
- Students will identify suitable research methods in order to seek answers to specific research questions.
- Students will use appropriate qualitative, quantitative, and design-oriented approaches to seek answers to their research question/questions. Mere conceptual works are also possible.
Methods
- The thesis is supervised by a supervisor and a co-supervisor, both of whom should be members of the Institute of Information Systems.
- The Master’s thesis is defended in an oral exam, where students may be asked questions related to their studies that may go beyond the content of their Master’s thesis.
- The official editing time is defined on the thesis proposal and may not exceed 22 weeks. A shorter editing time is possible.
Entry requirements
- A minimum of 60 ECTS must be achieved before registration.
- The modules Business Statistics I and Research Methods must be passed successfully.
- A research proposal (exposé) signed by the first supervisor and the academic director must be submitted to the study administration in parallel to module registration.
Recommended previous knowledge
- It is highly recommended that the research proposal (exposé) is developed within the module "Research Seminar"
Colloquium
- Colloquium (mid-term presentation) is usually held about two months prior to the submission of the final master's thesis.
- In the colloquium, students are expected to report on their progress and experience in writing their master's thesis.
- The outcome of the colloquium is graded "passed" or "failed".
- The colloquia for the summer term in 2017 will be held on: April 6 - April 7, 2017, starting from 09.00. A detailed schedule will be communicated two weeks prior to these dates.
Submissions and deadlines
- A copy of signed thesis proposal (Exposé) must be submitted until July 1st. (for the winter term) and February 1st (for the summer term) to: Exposé Submission link
- The master's thesis must be submitted until November 30th (for the winter term) and June 30th (for the summer term) to the the central service desk.
- The submission of master's thesis must include: (1) a CD ROM containing thesis' digital copy (at the central service desk) and (2) direct submission of thesis' digital copy to the supervisor and co-supervisor (via e-mail).
- If any of the dates above falls on a weekend or public holiday, the deadline is automatically extended until the next working day. Please also check the opening times of the central service desk, especially during summer months.
Compulsory reading
- Creswell, J.W. (2008) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 3rd Edition, Sage Publications
- Saunders, M.N.K.; Thornhill, A.; Lewis, P.; Leedy P.D.; Ormrod, J.E. (2007) Research Methods for Business Students: AND "Practical Research, Planning and Design", Financial Times Prentice Hall
C15 Research Seminar Data Science
C15 Research Seminar Data Science
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project Description
Short description
The course focuses on developing research proposals in the field of data science.
Topics
Learning objectives
Methods
Recommended previous knowledge
Compulsory reading
The course focuses on developing research proposals in the field of data science.
Topics
- Conducting literature reviews
- Developing research questions
- Designing qualitative, quantitative, and design oriented research
- Writing research proposals
- Ethical issues in data science
Learning objectives
- Students will know the professional code of conduct of the academic IS discipline.
- Students will effectively communicate academic research designs.
- Students will produce rigorous research proposals in the area of data science.
- Students will recognize and analyze ethical problems of designing and conducting research in the field of data science.
Methods
- The module integrates theoretical knowledge and practical skills in an interactive seminar.
- The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
Recommended previous knowledge
- Research Methods
Compulsory reading
- Creswell, J. W. (2013). Research design: Qualitative, quantitative, and mixed methods approaches. Sage.
C15 Project Seminar Data Science
C15 Project Seminar Data Science
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project Description
Short description
In this course, students apply acquired data science knowledge and skills to solve a real-world business problem from the area of marketing, finance, or operations.
Topics may include
Learning objectives
Methods
In this course, students apply acquired data science knowledge and skills to solve a real-world business problem from the area of marketing, finance, or operations.
Topics may include
- Supervised learning (regression, classification)
- Unsupervised learning
- Text mining
- Social network analysis
- Assessing model quality
Learning objectives
- Students will analyze a real-world case through the data science lens
- Students will collect and prepare data for later analysis
- Students will build and evaluate statistical models
- Students will translate statistical models into actionable results
Methods
- The module integrates theoretical knowledge and practical skills in a seminar focusing on a real-world case.
- The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
C15 Decision Theory
C15 Decision Theory
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project Description
Short description
The course focuses on judgment and decision-making, with emphasis on how decisions deviate from rational and/or ethical standards, with applications in human-computer interaction.
Topics
Learning objectives
Methods
Entry requirements
Compulsory reading
Further reading
The course focuses on judgment and decision-making, with emphasis on how decisions deviate from rational and/or ethical standards, with applications in human-computer interaction.
Topics
- Introduction to decision making under certainty and risk
- Measuring and modeling individual risk preferences
- Heuristics in decision-making
- Biases in decision making
- Emotions in decision making
- Designing decisions on websites
Learning objectives
- Students will know how decisions can be influenced by various human biases and how to improve individual decisions.
- Students will know basic methods of decision making in order to overcome human biases.
- Students will use methods of decision-making in order to improve business decisions in organizations.
Methods
- The module integrates theoretical knowledge and practical skills in an interactive lecture.
- The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
Entry requirements
- none
Compulsory reading
- Hastie, R. & Dawes R. M. (2010). Rational Choice in an Uncertain World. Sage: London.
Further reading
- Baron, J. (2008). Thinking and Deciding. Cambridge University Press: Cambridge.
- Bazerman, M. H. & Moore, D. A. (2013). Judgment in Managerial Decision Making. John Wiley & Sons, Inc: New York.
- Hammond, J. S., Keeney, R. L., & Raiffa, H. (1999). Smart Choices. A Practical Guide to Making Better Decisions. Havard Business Review Press: Harvard.
- Johnson, J. (2014). Designing with the Mind in Mind. Elsevier: Burlington.
- Kahneman, D. (2011). Thinking, Fast and Slow. Penguin Books: London.
C15 Data Mining & Predictive Analytics
C15 Data Mining & Predictive Analytics
Module Coordinator/Lecturers
Study Programmes
Master's degree programme in Information Systems
Project Description
Short description
The course covers various statistical techniques for making sense of the vast and complex data sets that have emerged in business in the past twenty years. Students will learn to detect patterns in large data sets of various formats (quantitative and qualitative) and translate them into actionable insights.
Topics
Learning objectives
Methods
Recommended previous knowledge
Compulsory reading
Further reading
The course covers various statistical techniques for making sense of the vast and complex data sets that have emerged in business in the past twenty years. Students will learn to detect patterns in large data sets of various formats (quantitative and qualitative) and translate them into actionable insights.
Topics
- Supervised learning techniques for regression (e.g. linear regression, SVM)
- Supervised learning techniques for classification (e.g. logistic regression, KNN)
- Unsupervised learning techniques (e.g. clustering, dimensionality reduction)
- Text mining (e.g. sentiment analysis)
- Hands-on labs with R
Learning objectives
- Students will know and understand the basic concepts and methods of data mining and predictive analytics
- Students will assess the assumptions and quality of statistical models
- Students will select and apply the right statistical models for a given task or data set
- Students will derive actionable insights from statistical results
Methods
- The module integrates theoretical knowledge and practical skills in an interactive lecture.
- The e-learning platform Moodle will be used throughout the course for the dissemination of course material and discussions.
Recommended previous knowledge
- Module “Business Statistics I”
- Module “Business Statistics II”
- Basic knowledge of statistical software R - online course available: tryr.codeschool.com
Compulsory reading
- James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An Introduction to Statistical Learning. With Applications in R. New York: Springer (a free online version is available at http://www-bcf.usc.edu/~gareth/ISL/)
Further reading
- Provost, F. & Fawcett, T. (2013). Data Science for Business. Sebastopol: O'Reilly Media