Applied Financial Instruments

Durée

45h Th

Nombre de crédits

Enseignant

Julien Hambuckers

Suppléant(s)

Marco Valerio Geraci, Philippe Hübner

Coordinateur(s)

N...

Langue(s) de l'unité d'enseignement

Langue anglaise

Organisation et évaluation

Enseignement au deuxième quadrimestre

Horaire

Horaire en ligne

Unités d'enseignement prérequises et corequises

Les unités prérequises ou corequises sont présentées au sein de chaque programme

Contenus de l'unité d'enseignement

The 'Applied Financial Instruments' course provides an introduction to theory and empirical methods that play an important role in finance. We cover selected topics in asset pricing (modern portfolio theory, Black-Litterman model, capital asset pricing model, factor models, prospect theory), fundamental analysis (bond pricing, discounted cash flow) and corporate valuation (methods of the comparables, DCF analysis). As the name suggests it, the course has aksi a strong practical component. Half of the course is dedicate to practical sessions with the software R.

The course is structured in two parts:

Part I: Asset pricing and portfolio analysis  

Part II: Applied tools in corporate finance 

A Part 0 will review fundamental concepts in statistics, like random variable, distribution, and regression model. 

The course consists in a set of lectures where equilibrium and investor's behaviour theories are presented. Their implications, in terms of real-life investment decisions, are then discussed. In the practical sessions, the students rely on these concepts to derive quantitative analysis techniques, implement them in the software R and find a solution to investment decision, portfolio optimization and corporate valuation problems. 

Acquis d'apprentissage (objectifs d'apprentissage) de l'unité d'enseignement

The course provides students with an understanding at the fundamental level of main concepts used in finance disciplines such as market finance and corporate finance. Through the exercise sessions, students will develop analytical and modeling skills in finance as well as strengthen their communication skills in English. In particular, students will improve their skills in empirical analysis - competences that will turn out to be crucial not only for group and individual tasks to be delivered throughout their master studies but as well for the empirical work of their master thesis.

This course contributes to the following Intended Learning Outcomes : 
ILO-6: being able to use accounting, mathematical, statistical and IT tools to solve a management problem
ILO-7: being able to conduct research autonomously and methodically to obtain the information needed to solve this problem using a critical approach 
ILO-9 : being able to work within a team 

Savoirs et compétences prérequis

Fundamental knowledge of financial markets,linear algebra, statistics and programming. In particular, a strong willingness to learn how to use the statistical software R is required. Fundamental programming knowledge such as variable definition, use of a graphical user interface, conditional (logical) statements and loops.

Good understanding of the concepts seen in courses like "Probabilité et inférence statistique" and "Marchés Financiers" are expected

The exercises of the asset pricing part will be conducted with the software R. Several tutorial, as well as access to a datacamp "An introduction to R" will give the students the needed prerequisites for the exercise session. However, it is strongly suggested to prepare/review these concepts before the course using e.g. online (free) resources.

Activités d'apprentissage prévues et méthodes d'enseignement

The course is structured along two lectures per week: 3 hours lecture and 2 hours technical tutorial classes.

Mode d'enseignement (présentiel, à distance, hybride)

Cours donné exclusivement en présentiel


Explications complémentaires:

Lectures (around 20 hours) and exercise sessions (around 25 hours).

Lectures recommandées ou obligatoires et notes de cours

Book chapters and scientific articles will be announced during the class.
  

Modalités d'évaluation et critères

Examen(s) en session

Session de mai-juin

- En présentiel

évaluation écrite ( questions ouvertes )

Session de août-septembre

- En présentiel

évaluation écrite ( questions ouvertes )

Travail à rendre - rapport

Evaluation continue


Explications complémentaires:

The final exam in June is a computer-based exam, where students are expected to : 

- solve several asset allocation/portfolio optimization/investment decision problems with the help of R and Excel

- explain theoretical concepts and solve exercises similar to the ones seen during the lecture sessions.

The final exam typically counts for 75% of the final grade whereas two assignments (to be completed per group) will count for 12.5% each (these percentages may change and will be confirmed during the course). Notice that the assigmnents are accounted for in the final grade only if: 

- the student obtains a passing grade to the written exam.

- the student obtains a passing grade on average over the two assignments. 

Otherwise, the final exam counts for 100% of the fiinal grade.

Completion of a preliminary datacamp and regular attendance to exercise sessions may lead to bonus points up to 5%.

The september exam counts for 100% of the final grade.

Stage(s)

Remarques organisationnelles et modifications principales apportées au cours

Contacts

Prof. Julien Hambuckers
email: jhambuckers[at]uliege.be
 
Teachinig assistant: P. Hübner.
 

Association d'un ou plusieurs MOOCs

Notes en ligne