Jeroen Koenraadt

This course is compulsory on the MSc in Accounting and Data Analytics. This course is not available as an outside option to students on other programmes. This course uses controlled access as part of the course selection process. For information on controlled access courses, including eligibility, application processes, deadlines, and departmental contact details, please refer to the Controlled Access Courses webpage.

Pre-requisites:

Students must have completed AC416 before taking this course.

Assumed prior knowledge:

Basic knowledge of data management, programming, and applied regression analysis (MY452 or equivalent and one of the following: MY472 or MY470 or ST445).

The course focuses on the use of big data and predictive analytics in the context of financial statement analysis. 

The course introduces and develops a framework for fundamental analysis which consists of four integrated steps: (i) business strategy analysis; (ii) accounting analysis; (iii) financial analysis; and (iv) forecasting and valuation. 

The first step focuses on the understanding of the firm’s operating environment and business risk, with the goal of developing performance expectations. Students will learn how to use data analytics techniques to support industry and competitive strategy analyses and to identify key profit drivers. The second step comprises an analysis of the extent to which accounting captures the underlying economics of the firm, the identification of areas of accounting flexibility, and the recasting of financial statements to undo biases and distortions. The course will use of regression analysis techniques to assess (and undo) financial statement distortions. The third step entails the assessment of a firm’s performance (e.g., through ratio and cash flow analysis based on accounting data). Students will learn how to calculate and analyze ratios for large samples of firms using traditional commercial databases, and how to draw and communicate insights from this analysis. The final step focuses on forecasting and valuation. The course will rely on the use of predictive analytics techniques to forecast earnings. Furthermore, students will learn how to construct investment portfolios based on accounting signals.

30 hours of seminars in the Winter Term.

This course has a reading week in Week 6 of Winter Term.

The course consists of 1.5-hour sessions delivered twice a week in the Winter Term, with a reading week in Week 6.  Students are expected to read and prepare cases in advance and be ready for in-class discussion.

Case analysis / study weekly.

• Students are expected to read the cases in advance and prepare for in-class discussion
• Students are also expected to read academic papers and other relevant material. The lecturer will guide students in the reading of academic papers

There is no required textbook for the course. Illustrative textbooks covering specific parts of the course include:

• Resutek, R. and V. Richardson (2024). Financial Statement Analysis, a Data Analytics Approach. McGraw Hill.
• Palepu K. G., P. M. Healy, and E. Peek (2022), Business Analysis and Valuation: IFRS Edition (Cengage Learning), 6th edition.
• Penman, S. H. (2013), Financial Statement Analysis and Security Valuation (McGraw-Hill), 5th edition.

Exam (60%), duration: 120 Minutes, reading time: 15 minutes in the Spring exam period.

Project (40%) in Winter Term Week 1.