Im Modul Controlling werden die Grundlagen der Kostenrechnung sowie wichtige Inhalte der elementaren Finanzmathematik unterrichtet. Bei der Kostenrechnung geht es um die fixen und variablen Kosten, die Break-even-Analyse, die Divisions- und Zuschlagskalkulation, die Deckungsbeitragsrechnung und die Normalkostenrechnung. Anschliessend werden Grundlagen für die Erstellung von betriebswirtschaftlichen Statistiken, von Vergleichsrechnungen und Planungsrechnungen dargestellt. Zusätzlich wird in die Cash-Flow-Rechnung eingeführt, indem direkte und indirekte Verfahren beleuchtet werden. Im Rahmen der elementaren Finanzmathematik werden insbes. die Zins- und Rentenrechnung sowie die Anwendung einer nachschüssigen Verzinsung behandelt.

Präsentationen, Fallstudien, Diskussionen

Schriftliche Prüfung (90min)

Verlässliche Informationen stellen eine wichtige Basis für alle betriebswirtschaftlichen Entscheidungen dar. Im Rahmen der betrieblichen Rechnungslegung erfolgt eine systematische Erfassung, Überwachung und Verdichtung der der durch den Leistungsprozess entstehenden Geld- und Leistungsströme. Nach einer Einführung in die Technik der Finanzbuchhaltung werden die Erstellung der Gewinn- und Verlustrechnung, der Bilanz und der Kapitalflussrechnung dargestellt, ergänzt um Anhang und Lagebericht. Dabei werden insbesondere die vorhandenen Gestaltungsspielräume genauer behandelt. Die Studierenden lernen dabei auch die verschiedenen Rechnungslegungsstandards und den Trend zur Internationalisierung (IFRS) kennen. Behandelt wird zudem die Bedeutung und die Aufgaben interner Kontrollsysteme (IKS) sowie der externen Revision (Wirtschaftsprüfung).

Präsentationen, Fallstudien, Diskussionen

schriftliche Prüfung

Die Teilnehmenden lernen die grundlegenden logistischen Konzepte, Strategien und Philosophien in der Beschaffung, Produktion, Planung und Distribution, sowie die Umsetzung dieser Konzepte und Strategien kennen. Es geht dabei insbesondere um die Erkenntnis, dass in der Logistik der Systemgedanke und die Vernetzung von Anlagen, Informationen und Materialflüssen einen hohen Stellenwert haben. Die Studierenden verstehen Logistik als Querschnittsfunktion über unterschiedliche Unternehmens- und Wirtschaftsbereiche und erkennen die hohe Vernetzung verschiedener Methoden und Instrumente. Sie werden in die Lage versetzt, verschiedene Logistiksysteme und ihre Komponenten zu identifizieren, zu analysieren und hinsichtlich ihrer Einsatzmöglichkeiten zu bewerten. Sie können die Systembestandteile differenzieren und ansatzweise Stärken und Schwächen in Realsystemen erkennen.

Präsentationen, Fallstudien, Diskussionen

Präsentation, Fallstudienanalyse

In diesem Modul wird eine systematische Einführung in die Denkweise des Marketings, seine Prinzipien, Entscheidungstatbestände, Instrumente und Methoden gegeben. Die Veranstaltung soll den Studierenden ein Grundverständnis über die Aufgaben und Ziele einer marktorientierten Unternehmensführung vermitteln. Die Studierenden sollen anschließend in der Lage sein, Praxisfälle aus einer Marketingperspektive zu analysieren und zu beurteilen, um unter Verwendung der einschlägigen Terminologie Lösungsvorschläge zu entwickeln.

Präsentationen, Fallstudien, Diskussionen

Präsentation, Essay

This year again, CFA Society Switzerland will be hosting the CFA Institute Research Challenge in Switzerland which is the local competition within the global CFA Institute Research Challenge. We also welcome all other universities and especially universities of applied sciences in Switzerland and Liechtenstein to join the Challenge this year.

The Challenge is an annual competition between teams of students in preparing an investment case consisting of a detailed research report and a presentation of a listed Swiss company. For the coming competition Straumann has been selected. The Swiss company is headquartered in Basel and listed at the Swiss Stock exchange since 1998. Straumann is a global leader in implant, restorative and regenerative dentistry which offers products and services to dentists and dental laboratories in more than 70 countries world-wide.

The students will be supported by industry mentors and will have to present their final results in front of a panel of senior investment professionals. The Challenge provides students with a unique opportunity to gain first-hand experience in the investment world and to interact intensively with investment professionals and CFA charterholders.

Guided self-study seminar

• Ability to identify sources of value in a business and transform them in investment thesis
• Write an equity analyst report
• Perfect an equity analyst pitch
• Develop soft skills in handling Q&A sessions

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.

• The thesis is supervised by a supervisor who 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.

• 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.

Compulsory reading

• Bryman, A. & Bell, E. (2015) Business research methods (4th ed.). Oxford, UK: Oxford University Press.
• Creswell, J.W. (2013) Research Design: Qualitative, Quantitative, and Mixed Methods Approaches (3rd ed.). Sage Publications
• Oates, B. J. (2006). Researching information systems and computing. London, UK: Sage Publications.
• Recker, J. (2012). Scientific Research in Information Systems: A Beginner’s Guide. Springer, Heidelberg, Germany.

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).
• 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: Please have a look at the current "Guidelines for Writing Academic Papers in Economics".
• 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.

Security Management covers technical and organisational methods for the definition and implementation of security policies. The course covers five primary topics:

• People, processes, and strategic planning
• Risk management
• Regulatory compliance, aw, and ethics
• Security analysis, safeguards, and frameworks
• Maturity and performance measurement

• The module involves interactive lectures with exercises to integrate theoretical knowledge with practical design and analysis skills.
• Homework assignments are used to deepen students’ understanding of the analytical methods of security management.

After successful completion of the course, students will

Professional competence
• understand the main security objectives and processes
• be able to initiate and lead basic security initiatives in smaller organisations

Methodological competence
• be able to set up and maintain basic information security management systems
• be able to apply correct metrics to measure security related KPIs

Social competence
• understand that security management always has an ethical part

Personal competence
• be able to identify emerging security issues
• be able to find and apply suitable standards, literature and frameworks

Technological competence
• be familiar with the main security related standards, guidelines, and frameworks

• Students are provided with the lecture slides and supplementary material (e.g., selected journal articles).

Written exam (60min)

• This course builds on what you have learnt in Innovative Finance: Data Science and Machine Learning 1.
• Based on a large real-world dataset, we will host our own Kaggle competition, where groups of students will compete against each other in a machine learning contest using financial data.
• The challenge will be different each time, so we might forecast stock returns, classify stocks according to how green they are based on tweets and facebook posts or dynamically put together portfolios of cryptocurrencies that are expected to outperform in subsequent periods.
• The course is structured as a lab, where we tackle all real-world issues related to the current challenge together, but will also run small competitions to get the most out of our data.
• Grading will NOT be based on placement in the contest but focus on contribution to the final output and team work.

• Lectures are interactive “labs” devoted to hands-on programming.
• Moodle is used throughout the course to disseminate course material and for information and discussion.

After successful completion of the course:

• Students understand the practical problems when applying statistical methods to real world financial data.
• Students are familiar with the necessary tools to tackle real-world problems based on large (and possibly unstructured) datasets.
• Students can apply the relevant methods to solve real-world problems with the tools available to them.
• Students are able to effectively communicate the results from their projects to a wider audience.

see lecture(s) within the module

• 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