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Course module: 323063-M-6
323063-M-6
Empirical Methods in Finance
Course info
Course module323063-M-6
Credits (ECTS)6
CategoryMA (Master)
Course typeCourse
Language of instructionEnglish
Offered byTilburg University; Tilburg School of Economics and Management; TiSEM: Finance; Finance;
Is part of
M Accountancy
M Finance
Lecturer(s)
Lecturer
dr. J.A. Crego
Other course modules lecturer
Lecturer
dr. N. Pavanini
Other course modules lecturer
Academic year2020
Starting block
SM 1
Course mode
Full-time
RemarksCaution: this information is subject to change
Registration openfrom 25/08/2020 up to and including 20/08/2021
Aims
This course introduces basic methods that are used in contemporary financial research. The objective is to provide students with the necessary tools to study the relevant literature of other core courses in the program and to conduct empirical financial research within the framework of the Master's thesis.
 
The course pays ample attention to the intuition and the practical applicability of a variety of econometric techniques that are widely used in contemporary empirical research. Reference will be made to many real world examples from the corporate finance and asset pricing literature. The classes intend to provide hands on experience with an econometric package such as STATA and will focus on a careful interpretation of the empirical results obtained.

The course will be divided in two parts. The first part will be during the first block and will cover cross sectional methods, with applications to corporate finance topics. The second part will be during the second block and will cover time series methods, with applications to asset pricing topics.


Specifics
 
Your grade depends on the performance in the midterm and final exams, as well as in the two group assignments. The midterm exam will refer to the content of the first part of the course and will take place at the end of the first block. The final exam will refer to the content of the second part of the course and will take place at the end of the second block. Similarly, the first group assignment will refer to the content of the first part of the course, whereas the second group assignment will refer to the content of the second part of the course. The resit exam will cover the content of both the first and second part of the course.

If the average of the grades of the midterm and final exams is below 50 out of 100, you will fail and your grade will consist for 100% of the average of the midterm and final exam grades. If you score 50 points or more in the average of the midterm and final exams, your grade consists for 40% of the two assignment grades (20% each), for 30% of the midterm exam grade, and for 30% of the final exam grade. 

A similar rule applies for the resit exam. If you score less than 50 points on the resit exam you will fail and your grade consists for 100% of the resit grade. If you score 50 points or more for the resit, your grade consists for 40% of the two assignment grades (20% each) and for 60% of the resit exam grade.
 
Content
On successful completion of this course, students will be able to:
  1. Describe the features of a dataset (e.g. cross section, time series, panel data) and define the relevant variables.
  2. Interpret descriptive statistics, such as mean, variance, correlation, and recognize the important variables to use.
  3. Choose the appropriate empirical model (e.g. OLS, Instrumental Variables, Logit, Probit, Time series or panel estimators) and perform the tests needed (e.g. t-Test, F-Test, heteroscedasticity, serial correlation).
  4. Analyze the results and compare different models.
  5. Comment and interpret the results obtained, based on theoretical hypotheses from the literature and on other empirical studies.
  6. Draw conclusions based on the results obtained to answer the research question.

More specifically, the content of the course can be summarized with the following five topics:
  1. Find and assemble the appropriate data to address a specific research question. This topic requires student to develop the skill to search for and find the appropriate data that they need to answer a specific research question. It also requires their ability to perform data manipulation to clean and prepare the dataset for the statistical analysis they will carry on.
  2. Describe and analyze the information contained in a dataset. This topic requires students to have the ability to describe and analyze the key relevant information from the dataset they assembled, and especially to highlight the key features of the data functional to address the research question of interest.
  3. Use the appropriate methodology to conduct empirical research given the data and the research question. Once students get acquainted with the data, they need to be able to understand what is the right econometric methodology to use based on the kind of variables in the dataset and on the research question they are addressing. Moreover, they need to make sure that the methodology is applied in the correct way, performing all the key statistical tests.
  4. Interpret correctly and critically the results of the empirical analysis. Once the students obtain an output from the econometric method they applied, they are required to correctly interpret the results of their model and of the tests they performed. The interpretation should be both descriptive and critical, as from the results students should infer any potential drawback of their application.
  5. Draw conclusions on how the results can be used to improve our understanding of a topic. Last, students should give an economic interpretation of their results, being able to provide a clear answer to their research question, and suggesting any potential policy implication of their findings.

Read the course syllabus for a more extensive description of the topics covered.

 
Type of instructions

There will be 2 lectures per week of 2 hours. During the two weeks before the midterm and final exams there will be 4 instructions of 2 hours each, to help students with the preparation of the exams. Additionally, there will be 12 hours of computer lab (without instructor), during which one of the university computer rooms will be reserved for EMF students to practice with the statistical software STATA and work on the assignments.


Type of exams

There will be 2 group assignments, one for each of the two parts of the course, each counting for 20% of the final course grade. There will be a midterm written exam at the end of the first part of the course, counting for 30% of the final course grade. There will be a final written exam at the end of the second part of the course, counting for 30% of the final course grade. If the average of the grades of the midterm and final exams is below 50 out of 100, there will be a resit written exam counting for 60% of the final course grade.


Recommended Readings
  1. Lecture slides and notes
  2. Wooldridge J.M., "Introductory Econometrics: A Modern Approach", Cengage learning, 6th edition (previous editions are also ok but have less exercises)
Course available for exchange students
Master level, conditions apply
Contact person
dr. N. Pavanini
Timetable information
Empirical Methods in Finance
Written test opportunities
DescriptionTestBlockOpportunityDate
Written test opportunities (HIST)
DescriptionTestBlockOpportunityDate
Midterm (30%) / Midterm (30%)MIDTERM_01SM 1122-10-2020
Written exam (30%) / Written exam (30%)EXAM_01SM 1121-12-2020
Written exam (30%) / Written exam (30%)EXAM_01SM 1229-01-2021
Required materials
-
Recommended materials
-
Tests
Assignment 1 (20%)

Assignment 2 (20%)

Written exam (30%)

Final grade

Midterm (30%)

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