Learning objectives

The objective of this course is to provide students critical skills to address original and novel research questions in finance along with a knowledge of the most cutting-edge methodologies for doing research in finance.

Program

Class 1 (5 h)
An overview of the main datasets and identifications tools in finance (Part I).

Class 2 (3 h)
An overview of the main datasets and identifications tools in finance (Part II).

Didactic methods

This course is designed to develop the first academic skills to conducting high-quality research into the financial domain. Students will get the basic skill to identify the most intriguing and promising research fields in finance, to develop research hypothesis, conduct analysis and present results in a rigorous way.

Learning assessment procedures

Grade Components:
1. Class Preparation, Participation, (30%)
2. Contribution and Discussion (20%)
3. Assignment, Research Paper & Presentation (50%).

Assessment

Grading Scale:
A. Excellent
B. Very good
C. Good
D. Sufficient
E. Not sufficient

We are committed to the ethical use of Artificial Intelligence (AI) in Ph.D. student assignments, prioritizing fairness and transparency.
While AI may enhance learning experiences, its use in generating assignments, including data, results, and references, is not allowed.
Students must describe any content created or modified, and appropriately cite the name and version of the AI tool used. This ensures transparency and upholds the integrity of academic work, aligning with our commitment to honesty and accountability. Please follow the Emerald or Wiley guidelines assigned during the Research Design and Research Organization seminar, or consult the professor who followed the specific assignment/task requested for
any doubts.

In-Class Procedures:
Open discussion, presentations, and critique.

Scheduled Lessons