Syllabus

Title
0730 Kurs IV - Business Forecasting Project
Instructors
Ass.Prof. Dr. Otto Janschek, Claudia Marini, MSc (WU)
Contact details
  • Type
    PI
  • Weekly hours
    2
  • Language of instruction
    Englisch
Registration
09/21/21 to 09/21/21
Anmeldung durch das Institut
Notes to the course
Subject(s) Bachelor Programs
Dates
Day Date Time Room
Tuesday 11/23/21 01:00 PM - 05:00 PM Online-Einheit
Tuesday 11/30/21 01:00 PM - 05:00 PM Online-Einheit
Tuesday 12/07/21 01:00 PM - 05:00 PM Online-Einheit
Tuesday 12/14/21 01:00 PM - 05:00 PM Online-Einheit
Tuesday 01/11/22 01:00 PM - 05:00 PM D5.1.002
Tuesday 01/18/22 01:00 PM - 05:00 PM TC.1.01 OeNB
Wednesday 01/26/22 01:00 PM - 05:00 PM TC.1.02

Contents

This class is project-based. Together with a group of valuation professionals, we will explore how the increasing focus on ESG impacts business valuation. The goal of this course is to develop and conduct empirical studies based on the concepts developed in course 3.

Learning outcomes

After completing this class students will be able to:

  • understand how ESG impacts DCF valuation parameters
  • discuss issues in forecasting financials and implementing DCF models
  • identify and use sources for collecting financial and ESG data
  • understand and apply steps in preparing data for statistical analyses
  • understand and apply basic statistical techniques

Apart from that, completing this course will contribute to students' abilities to:

  • be an effective, hard- and soft-skilled team member
  • work effectively in a team-based structure
  • develop a project structure for a loosely defined project and prioritize tasks
  • give presentations on complex topics

Attendance requirements

Attendance: In order to successfully pass this course, your absence is limited to 20% of all our appointments including online sessions.

Teaching/learning method(s)

  • Teaching materials for self-study
  • Input lectures
  • Quizzes
  • Coachings
  • Group discussions
  • Group assignments
  • Individual assignments

The course uses a problem-based learning approach. This implies that students will need to develop solutions to complex questions on an individual and/or group level. To support this process, a mix of instruments will be used: Self-study materials and where required, input lectures will provide basic concepts for the next steps, coaching sessions will provide early feedback, and presentations and reports will demonstrate the ability of students to successfully structure problems and provide meaningful answers. Practitioner input will give students guidance and feedback on their progress.

    Assessment

    The course grading system is based on:

    • Quiz 10% 
    • Interim presentation 20%
    • Final presentation, incl. components of grading carried out by partnering company and peer review 30%
    • Final report 30%
    • Participation 10% 

    Grading scheme

    • excellent/Sehr gut ≥87,5%
    • good/Gut ≥75,0%
    • satisfactory/Befriedigend ≥62,5%
    • sufficient/Genügend ≥50,0%
    • not sufficient/Nicht genügend <50,0%

    Prerequisites for participation and waiting lists

    SBWL-Grundkurs (Kurs 1) und Business Analysis Project Kurs

    Readings

    Please log in with your WU account to use all functionalities of read!t. For off-campus access to our licensed electronic resources, remember to activate your VPN connection connection. In case you encounter any technical problems or have questions regarding read!t, please feel free to contact the library at readinglists@wu.ac.at.

    Recommended previous knowledge and skills

    Apart from financial analysis and basic investment analysis/valuation knowhow (Grundkurs SBWL, Business Analysis Project) your performance and level of effort will depend on your Excel and Powerpoint skills. For those who have not used these tools intensively, we recommend to (re)acquaint yourself with these programs before the start of the course. VBA programming is not required. For data collection, experience with Bloomberg, Eikon, or WRDS will be helpful (https://www.wu.ac.at/en/library/finding-literature/databases). For statistical analyses, experience with R or Stata will help.

    Availability of lecturer(s)

    Otto Janschek & Claudia Marini

    contact hours: Tuesdays at Ifu/Dienstagsbier

    Last edited: 2021-09-07



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