Empirics in Corporate Finance

llsms2110  2023-2024  Louvain-la-Neuve

Empirics in Corporate Finance
5.00 credits
30.0 h
Shrestha Prabal (compensates Thewissen James); Thewissen James;
Fundamental mathematical and statistical concepts (such as those covered in Mathématiques avancées et fondements d'économétrie [ LECGE1337 ])
Main themes
This course provides a comprehensive overview of the theory and practice of decision-making within the corporation. It is taught primarily through lectures, case studies and presentations. We will study important concepts in corporate finance, and develop skills for making corporate investment and financing decisions. The concepts and skills will be applied into practice through case studies.
Learning outcomes

At the end of this learning unit, the student is able to :

1 Upon completion of this course, students are expected to complete the following key tasks:
  1. Have a good understanding of important issues in corporate finance;
  2. Have a good understanding of business ethical values;
  3. Be able to apply concepts and tools learned in class;
Upon completion of this course, students are expected to develop the following capabilities:
  1. Corporate citizenship;
  2. Communication and Interpersonal skills;
  3. Critical thinking skills;
  4. Teamwork and leadership.
The goal of this course is to initiate Masters students to research in Empirical Corporate Finance and to prepare them for their Master Thesis. The course is organized based on published and working papers in the field with a focus on specific econometric methods and the publication process. Instead of providing an exhaustive overview of the field, the course focuses on a pre-selected topic (ESG, agency issues, cryptocurrencies, the impact of regulation...) and illustrates different empirical approaches to the same or related questions. Using papers on this specific topic, the course will highlight the following empirical topics: endogeneity, difference in difference estimators and event studies.
Teaching methods
Empirics in Corporate Finance is a course in the corporate finance track. It is organized in a way for students to think critically about research and policy. After taking this course, students should have a good overview of standard empirical approaches to research questions and a grasp of some of the underlying theory. They should also have a clear understanding of what the state of the art is in the topics we cover and where the contributions stand. Material covered in other corporate finance courses, e.g. Current Issues in Finance (LLSMS2108) has direct relevance to this course. In particular, topics relating to M&As, agency issues, ESG, earnings management, textual analysis and tax aggressiveness provide a useful foundation for understanding the topics at a more advanced level. The students will also learn to communicate research ideas and issues verbally in a clear and logical manner through in-class presentation. Knowledge of the program R or Python is assumed.
Evaluation methods
Students are expected to write a MiniThesis of 5,000 words maximum for this course. The short length of this thesis does not reduce the amount of work. On the contrary, the short length aims at learning how to increase the impact of the writing and communication.
The students will for groups of maximum 5 people. Depending on the number of people attending the course, this limitation might change. The MiniThesis will be conducted in a time frame of six weeks and include sections regarding the 1) Motivation/introduction, 2) contribution, 3) Data & method, 4) Result, 5) robustness tests and 6) Conclusion/Discussion. The students will have to organize themselves to collect their own data on a specific topic and conduct the analyses in R. The students will present their research proposal in the third week and defend their findings in the final week of the course with a podcast. Each group will be evaluated by their peers on a list of pre-defined criteria. 
The final grade is distributed as follows: 
  • 45% Two presentations:
    • 15% – 5-min presentation the review of the literature (peer-evaluation but adjustable by the Professor if free-rider issue or other manipulation occuring).
    • 30% – 5 min presentation on your methodolgy and findings in the form of a podcast. Originality is key! (peer-evaluation but adjustable by the Professor if free-rider issue or other manipulation occuring).
  • 35% Final report: A 5,000-word letter that summarizes your research question (peer-evaluation but adjustable by the Professor if free-rider issue or other manipulation occuring).
  • 20% Active presence & Participation in class (peer-evaluation but adjustable by the Professor if free-rider issue or other manipulation occuring). To avoid free-riders in the group, an intra-group evaluation will be performed. Bad scores will downgrade your own grade. If you do not fully peer-review all the groups, your grade will be reduced by 40% for that part. 
To avoid any abuse or manipulation, all grades remain at the instructor's discretion, which is final. 
Other information
The class combines presential and distancial meetings (dates TBC). The contact moments will be on Teams. The presentations of this class will be fully organized on Campus. 
Online resources
Where course participants use generative artificial intelligence (AI) and AI-assisted technologies in the writing process, they should only use these technologies to improve readability and language. Applying the technology should be done with human oversight and control, and participants should carefully review and edit the result, as AI can generate authoritative-sounding output that can be incorrect, incomplete or biased. 
Participants should disclose in their mini thesis the use of AI and AI-assisted technologies in the writing process (on the title page of their work). Please note that course participants are ultimately responsible and accountable for the contents of the work.
Teaching materials
  • You need to refer to the slides on Moodle, your prior courses and your own originality.
Faculty or entity

Programmes / formations proposant cette unité d'enseignement (UE)

Title of the programme
Learning outcomes
Master [120] in Management