• The course Innovative Finance: Data Science and Machine Learning 1 will give students the understanding and necessary tools to apply Machine Learning methods to essential research problems in finance.
• Statistical learning (aka Machine Learning or Artificial Intelligence) is the main driver of innovation in the financial industry and can be found almost everywhere: credit decisions, risk management, fraud prevention or (automated) investment processes.
• Therefore, this course will pick up where Quantitative Finance stopped and further explore methods of supervised and unsupervised learning, thereby teaching our computers to learn from the large amounts of data available to us.
• The entire course will be accompanied by (small) real-world-real-data applications making use of Googles’ free and powerful Colab and Kaggle platform.
• For those with a further interest in Innovative Finance: Join Innovative Finance: Data Science and Machine Learning 2 for a real and big-data based machine learning challenge, entirely hosted on www.kaggle.com.

In particular, this course will cover:

• Linear model selection and regularization
• Resampling methods, model assessment and selection
• Tree-based methods
• Neural networks and deep learning
• Unsupervised learning

• Lectures are interactive
• Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course:

• Students understand and can explain the concepts of supervised and unsupervised learning.
• Students are familiar with a variety of topics in finance where machine learning methods can be successfully applied.
• Students are able to apply the most important concepts covered in the course to real datasets in R, making use of powerful online platforms.
• Students are able to effectively communicate about machine learning and artificial intelligence in finance.
• Students are able to critically evaluate situations where machine learning could successfully be applied.

see lecture(s) within the module

• Smart Contracts
• Token Valuation
• Crypto Exchanges
• Tokenization of services and other goods
• Trade Finance with Blockchain
• InsurTech, PropTech and Social Trading

Interactive seminar with guest lecturers.

After successful completion of the course, students…

• Know what smart contracts are and have a basic knowledge of how to code a simple ERC20 Token
• Understand the methods of token valuation and can apply it to simple examples
• Understand how crypto exchanges work and can evaluate them in terms of business model and risk
• Can describe what alternative types of assets can be tokenized and how this is done
• Have basic knowledge of the changes happening in trade finance with respect to Blockchain application
• Know the status quo of developments in the fields of InsurTech, PropTech and Social Trading

• Blockchain Technologies
• Bitcoin and Altcoins
• Tokenization of assets
• Crypto Wealth Manangement
• Crowdfunding
• Robo Advisory

Interactive seminar with guest lecturers.

After successful completion of the course, students…

• know the basic functions of a Blockchain and can explain the most common consensus mechanisms
• can distinguish between different types of cryptocurrencies and can explain their respective field of application
• understand the principles of tokenization and the important factors in token offerings
• know how crypto assets can be integrated within a portfolio
• can distinguish between different types of crowdsourcing and know when to use which
• understand the basic principles of robo advisory

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 one of the three subject areas that constitute the core of the curriculum (i.e., Business Process Management, Data and Application Security, and Data Science).

• The thesis is supervised by a member from the Institute of Information Systems (professor, assistant professor, visiting professor or senior lecturer).
• The master’s thesis is defended in an oral exam.
• The official editing time is defined on the thesis proposal ("exposé") and may not exceed 22 weeks.

After successful submission of the Master's thesis, students will be able to:

• formulate appropriate research questions.
• identify appropriate theories to explain empirical phenomena.
• use appropriate qualitative, quantitative, mixed-methods, and design-oriented research designs.
• identify suitable research methods in order to seek answers to specific research questions.

• A copy of the signed thesis proposal ("exposé") must be submitted until 1 July (for the winter term) and 1 February (for the summer term).
• The master's thesis must be submitted until 30 November (for the winter term) and 30 June (for the summer term) to the the central service desk. (Students are asked to check the opening times of the central service desk, especially during summer months.)
• If any of the dates above falls on a weekend or public holiday, the deadline is automatically extended until the next working day.
• The submission must include: (1) two signed copies in adhesive binding and (2) two signed copies in spiral binding. In addition, students have to upload their thesis to Moodle.

Today, virtually all large organizations have to cope with growing complexity in their enterprise architectures (EA), which often comprise several hundreds or even thousands of IT applications that support an increasing variety of business processes. The underlying software components run on several generations of IT infrastructure, and digitization leads to increased intensity in inter-organizational interfaces and customer-centric solutions. As a consequence, EA comprises not only the fundamental structure and dependencies of business processes, IT applications, software components, IT infrastructure, and data in an enterprise, but also connected components of business ecosystem partners and customers. Changing only one of these EA components can impact a potentially large number of related components. Simultaneously changing several of these components in a number of change projects or transformation programs leads to potentially redundant (i.e. inefficient) and/or inconsistent processes, software systems, and/or IT infrastructure components. The short-term consequence is a waste of resources, and the longer-term consequences are increased effort and difficulty in maintaining existing information systems (because of excessive complexity) and shortage of resources that can be used for innovation.

EA management (EAM) is a management discipline that guides EA’s design and evolution. The goals of EAM are to control complexity, reduce inconsistencies, and leverage synergies in EA. EAM also supports the implementation of business innovation from a holistic perspective. In contrast to other architecture disciplines (such as, e.g., solution architecture or software architecture), EAM covers the entire business-to-IT stack, complete lifecycles of business technology, and all relevant EA components across the enterprise (or even beyond the enterprise, e.g. in business ecosystems).

This course covers EA and EAM, incorporating both research findings and current examples from business practice. The course covers four primary topics:

• Core concepts and the necessity of EAM
• EAM use cases
• EA modelling and analysis
• Continuous improvement and maturity of EAM

• The course involves interactive lectures, class room exercises, and practitioner presentations to integrate theoretical knowledge with practical design and analysis skills.
• Case studies are used to integrate the aspects of EA/EAM covered in the course.
• Students complete homework assignments between lectures.

After successful completion of the course, students will

Professional competence
• understand the necessity, fundamental concepts, methods and theories related to EA and EAM
• be able to identify common patterns and different adaptations in EA/EAM cases in practice
• be able to evaluate the consistency, fit, and effectiveness of EAM initiatives in organisations

Methodological competence
• be able to adapt and apply generic EA/EAM knowledge (frameworks, methods, techniques) to concrete application cases
• be able to understand and interpret EA models/meta models as well as to create simple EA models/meta models for provided case descriptions

Social competence
• understand the importance of communication and an organisation’s social system for designing and institutionalising architectural guidelines
• gain experience in introducing ‘technical’ concepts to their fellow students (in the context of warmup presentations)
• gain experience in focused teamwork in diverse groups (in the context of case-related group projects)
• gain experience in focused discussions with experts from practice (in the context of practitioner guest lectures)

Personal competence
• expand their mindset regarding a ‘customer-oriented’ way to understand and organise IS-related management in organisations
• expand their mindset regarding different types of coordination interventions in organisations

Technological competence
• understand the fundamentals of conceptual modelling and modelling meta levels (in the context of EA modelling)

Assignments, written exam

• Based on Sustainable Finance 1, this second course will concentrate on delivering knowledge on the difference in financial products used in the sustainable investing market, as well as related problems.
• This module tries to deliver know how for a future job related to financial product developing, portfolio management, financial consulting or relationship management.
• Interactive lectures with discussion on comparisons across Investment Strategies, purpose and importance of Green Bonds, particular characteristics and problems of Impact Investing and SDG Investing, sustainable investors’ preferences, sustainable mutual funds, sustainable indices and ETFs
• Students work in groups on a Sustainable Portfolio Project within small groups. Based on their definition of a specific sustainable investor, they develop a portfolio which they present to the investor. They write down their approach, ideas and the methodology in a final report
• The lecturer tries to organize professional partners supporting Sustainable Portfolio Project.

• Dialogical teaching (strong interactive discussion in class on selected in-depth fields of sustainable investing)
• Case Study Learning in groups (-> cooperative and multi-dimensional learning) and across groups

Students

• Improve their knowledge in practical issues of sustainable finance.
• Learn how to solve problems connected with the construction of sustainable portfolios.
• Learn more about specific financial instruments and indices in Sustainable Investing.
• Understand different approaches and processes when constructing a sustainable portfolio.
• Learn about the structure of market participants in sustainable investing.

• Derivatives Markets and Instruments: Forwards, Futures, Options, Swaps
• Pricing of Equity, Fixed Income, and Currency Derivatives
• Hedging Using Derivatives
• Financial Engineering

Lecture

Students …

• know how derivatives and derivatives markets work,
• apply standard models to price financial derivatives,
• use Greek variables in risk management and financial engineering,
• devise and/or analyze derivatives strategies for speculation, hedging and arbitrage,
• combine basic instruments to achieve desired payoff structures/decompose payoff structures into their basic components.

See lecture(s) within the module

> Review of Portfolio Theory and Asset Pricing
> Extensions of the CAPM
> Empirical confirmation/rejection of the CAPM
> Stock Market Anomalies
> Multi-Factor Models
> Investment Strategies
> Performance Evaluation
> Portfolio Execution, Monitoring, Rebalancing and Costs

After completion of the module, the students are able to evaluate assets by means of various
models and to illustrate investment procedure. They thereby draw on current knowledge of
capital market research and can show suggestions for solutions while integrating their
theoretical knowledge. Events on the capital market are critically scrutinised in discussions
with instructors and fellow students.

> Students have read and understand the most important literature in empirical asset pricing.
> Students are able to critically evaluate existing literature in the field of empirical asset pricing
> Students understand, can explain and appropriately apply methods used in empirical asset pricing.
> Students are able to effectively communicate research methods and outcomes to their peers.

See lectures within the module.

This module covers the practical application and implementation of concepts in portfolio management. Using an online tool with real and live market data, student groups manage their own portfolio throughout the semester. This includes the specification of an investment strategy at the beginning of the course, frequent trading and writing a fund brochure and performance report.

After completion of the module, the students are able to:
> manage their own investment portfolio
> translate real time financial information into investment decision
> develop and formulate an investment strategy
> write a fund brochure
> conduct a performance review and reporting

> Students are able to critically evaluate financial information
> Students can appropriately translate financial information into investment decisions
> Students understand and appropriately apply portfolio management methods
> Students are able to effectively communicate performance results to peers

See lectures within the module.

All aspects that have to be considered for writing a successful exposé and master thesis, like e.g. the fulfillment of formal requirements, the development of a research project and the correct choice of methodology for answering the research question.

Student Presentations and discussion
Self-study through reading announced literature at home.

After the completion of this module, the students have successfully handed in their Exposé for the master's thesis and know how to conduct a research project. They have learned how to develop a research question, choose and discuss the methodology for answering it. They know how good research papers and theses look like and are able to transfer this knowledge into their own project.

> Learn how to structure a research project
> Formulate a research idea and develop a research question
> Narrow the research idea to a realizable size
> Choose and justify a research methodology
> Conduct a sound literature review
> Formulate an exposé in which the research project is described and justified
> Know and apply the scientific standards and writing principles of the University
> Discuss their project and integrate peer and professional feedback
> Give feedback on other research projects
> Organize their project in terms of literature, writing and time plan
> Learn how to develop a thesis exposé

Presentation (30%)
Written exposé (70%)
Both parts need to be psitive
Obligatory Class Participation