EM3E5M31
Program
PGE
PGE 3A -Finance (FIN)
PGE 3A -Finance (FIN)
UE
Financial Institutions
Semester
A
Discipline
Finance
Contact hours
22 H
Number of spots
45
ECTS
5
Open to visitors
Yes
Language
Coordinator
Régis BLAZY
List of lecturers
| Lecturer(s) | Contact hours - lecture | |
|---|---|---|
| Régis BLAZY | regis.blazy@unistra.fr | 22 h |
Pedagogical contribution of the course to the program
LEARNING GOAL 1 : Students will master state-of-the-art knowledge and tools in management fields in general, as well as in areas specific to the specialized field of management. |
|---|
| Students will identify a business organization’s operational and managerial challenges in a complex and evolving environment. |
| Students will understand state-of-the-art management concepts and tools and use them appropriately. |
| Students will implement appropriate methodologies to develop appropriate solutions for business issues. |
| LEARNING GOAL 4: Students will study and work effectively in a multicultural and international environment. |
| Students will demonstrate written and oral competency in two foreign languages. |
| Students will analyze business organizations and problems in a multicultural and international environment |
Description
This course aims at providing a panorama of the main techniques involved in credit risk management. After having presented the context of those techniques, this module mainly focuses on the prediction tools, such as credit scoring methods (discriminant analysis). The students are then given the opportunity to work on a real database made of bankrupt SMEs.Teaching methods
Face-to-face
- Lectures- Tutorials
In group
- Exercises- Other : XlStat application
Interaction
- Discussions/debates- Games (educational, role play, simulation)
Others
No items in this list have been checked.Learning objectives
Cognitive domain
Upon completion of this course, students should be able to- - (level 2) Describe the legal environement of bankrupty.
- - (level 2) Summarize the context of credit risk management.
- - (level 3) Use XLSTAT(c) software.
- - (level 4) Examine the quality of financial reports.
- - (level 5) Arrange financial ratios.
- - (level 6) Discriminate between good and bad firms.
- - (level 6) Appraise the risk of default.
Affective domain
Upon completion of this course, students should be able toNone affective domain have been associated with this course yet
Outline
1) Understanding the context of credit risk management - Definitions: (1) what is lending? (debt contract, collaterals), (2) what are (a) ratings, (b) credit scoring models, (c) credit risk management? - Predicting "default" or "bankruptcy"? A short view of corporate bankruptcy systems (Europe) - The importance of information in credit lending: main issues (adverse selection, rationing, collaterals) - The role of rating agencies in acquiring systematic information (presentation of the main agencies) - What does happen when ratings are wrong? (the role of the rating agencies in the 2008 financial crisis) - The rating, as a central element of the Basel agreements (Basel 2 & 3, pillars 1 to 3, expected loss). 2) Acquiring the technique of credit risk prediction (scoring) - Introduction through a practical example: the National Banks’ scoring functions. - Presentation of linear discriminant analysis: theory & assignments. - Credit scoring is the central element of credit risk management (elements on: credit migration, correlation of defaults, transition matrixes, portfolio risk management...) 3) Managing credit scoring in "real life" - Preparing a scoring (a question of point of view, accounts reprocessing, most common financial ratios used to build a scoring). - Presentation of a software dedicated to discriminant analysis: XlStat© - The students build their own credit scoring, using real corporate datasets. - Extensions: beyond credit scoring issues (financial marketing, surveys...).No prerequisite has been provided
Knowledge in / Key concepts to master
- EXCEL(c) - Statistics - Linear algebra - Financial analysis & Corporate reportingTeaching material
Mandatory tools for the course
- Computer- Calculator
- Other :
Documents in all formats
- Photocopies- Newspaper articles
- Worksheets
Moodle platform
- Upload of class documents- Assessments
- Other : Access to tutorial videos
Software
- Pack Office (Word, Excel, PowerPoint, Access)- Other : XlStat (Excel)
Additional electronic platforms
- Other : Unistra Cloud
Recommended reading
In English: - Credit Risk Scorecards: Developing and Implementing Intelligent Credit Scoring (2005). N. Siddiqi (John Wiley & Sons ed). - Credit Scoring Methods: cf. your forwarded document #1. M. Vojtek and E. Kocenda. In French: Analyse discriminante (Dunod). M. Bardos (Dunod). Mesure et gestion du risque de crédit dans les institutions financières. J. Petey and M. Dietsch (Revue Banque Ed.).
Several PDF documents uploaded on Moodle (Credit Risk Management class).
EM Research: Be sure to mobilize at least one resource
Textbooks, case studies, translated material, etc. can be entered“Building legal indexes to explain recovery rates: An analysis of the French and English bankruptcy codes”, R. Blazy, B. Chopard, N. Nigam. Journal of Banking and Finance, 2013. Vol.37, n°6, pp 1936–1959.
Assessment
List of assessment methods
Final evaluationExam week
Written (120 Min.) / Individual / English / Weight : 100 %
This evaluation is used to measure LO1.1, LO1.2, LO1.3, LO4.1, LO4.2
Written (120 Min.) / Individual / English / Weight : 100 %
This evaluation is used to measure LO1.1, LO1.2, LO1.3, LO4.1, LO4.2