0932 Business Analytics II
Phillipp Gnan, MSc (WU)
Contact details
Weekly hours
Language of instruction
09/26/22 to 10/27/22
Registration via LPIS
Notes to the course
Subject(s) Bachelor Programs
Day Date Time Room
Wednesday 11/02/22 08:00 AM - 11:00 AM TC.3.06
Wednesday 11/09/22 08:00 AM - 11:00 AM TC.3.06
Wednesday 11/16/22 08:00 AM - 11:00 AM TC.3.06
Wednesday 11/30/22 08:00 AM - 11:00 AM TC.3.06
Wednesday 12/07/22 08:00 AM - 11:00 AM TC.3.06
Wednesday 12/14/22 08:00 AM - 11:00 AM TC.3.06
Wednesday 12/21/22 08:00 AM - 11:00 AM TC.3.06
Wednesday 01/11/23 08:00 AM - 11:00 AM TC.-1.61

In this course, students will learn to apply the tools introduced in Business Analytics I in the context of Finance. In order to acquire the skills necessary to make complex data-based financial decisions, all lecture units consist of a theoretical Finance part followed by practical applications. In particular, the following topics will be covered:

  1. Basic Data Handling and Summary Statistics
    • Students will learn how to handle a firm-level dataset of financial characteristics and time-series of prices
  2. Data Visualization and Summary Statistics
    • Students will learn how to compute measures of financial performance and risk and how to adequately present them
  3. Hypothesis Testing
    • Students will compare firm performance in the cross-section based on standard firm-level and/or stock characteristics
  4. The Simple Linear Regression Model
    • Students will learn how to evaluate the exposure of a single firm’s stock price to the market’s risk
  5. The Multiple Linear Regression Model
    • Students will explore exposures of single firms’ stock prices to other risk factors
  6. Explanatory Factor Analysis
    • Students will learn how to distill information from multiple financial time-series into a single explanatory factor
  7. Optimization
    • Students will form minimum variance portfolios
Learning outcomes

After completion of the course, students will be able to understand and apply the principles, methods and tools of business analytics to problems in the field of Finance. This includes knowledge on:

  • Handling, visualizing and summarizing big data files in R
  • Formulating and testing hypothesis, and interpreting their results in a business context
  • Applying and interpreting linear regression methods to cross-sectional financial data
  • Formation of stock prices and measurement of financial price performance
  • Stylized facts on stocks, bonds and interest rates
Attendance requirements

Attendance requirement is met if a student is present for at least 80% of the lectures.

Teaching/learning method(s)

The course is taught using a combination of lectures, class discussions, assignments and practical applications of the tools and methods introduced in Business Analytics I.

  • Home assignments 30 points
  • In class assignments 30 points
  • Final Exam 40 points


If you fullfill the attendance requirements, the following grading scale will be applied

  • Excellent (1): 87.5% - 100.0%
  • Good (2): 75.0% - <87.5%
  • Satisfactory (3): 62.5% - <75.0%
  • Sufficient (4): 50.0% - <62.5%
  • Fail (5): <50.0%
1 Author: Berk, J. B. & DeMarzo, P. M.

Corporate finance.

Publisher: Pearson Education.
Remarks: purchase not necessary
Year: 2007
Recommendation: Reference literature
Type: Book

Lecture slides and notes.

Content relevant for class examination: Yes
Recommendation: Essential reading for all students
3 Author: Institute for Interactive Marketing and Social Media (WU Wien)

Business Analytics 2019 – integrated script for all topics (

Type: Script
Last edited: 2022-05-04