7.5 credits cr.
- Gå till denna sida på svenska webben
The objective of the course is to give students a thorough understanding of the most important research methods required for doing empirical analyses of financial data and for carrying out their bachelor thesis.
The course begins with a brief discussion of the academic writing on methodology and the simple estimation methods, such as the OLS, maximum likelihood, which is followed by a description of the time series financial data.
Thereafter, the course presents an explanation of the available methods for testing the most important asset-pricing model, i.e. CAPM. The course is ended with an event study.
The course concentrates on the following issues: OLS regression, Maximum Likelihood, Time series (Autoregressive models), Stationary and unit root test, , tests for the CAPM model, and an Event Study.
The teaching consists of lectures, computer labs and quizzes, and requires a significant portion of self-study on the part of students.
The course workload is 200 hours, equivalent to 7,5 higher education credits.
The language of instruction is English.
The teaching takes place on campus and online.
For more detailed course information, see the study guide, published on the learning platform no later than one month before the course commences.
Assessment for the course will be continuous and is carried throughout the different course activities.
Each assessment task is weighted in relation to its importance in the overall assessment of the course.
The student’s results from the different assessment tasks are added up to a total course score that will then translate into the final grade for the course.
Read more about the assessment in the course syllabus.
ScheduleThe schedule will be available no later than one month before the start of the course. We do not recommend print-outs as changes can occur. At the start of the course, your department will advise where you can find your schedule during the course.
Course literatureNote that the course literature can be changed up to two months before the start of the course.See syllabus for reading list
Course Coordinator: firstname.lastname@example.org
Head of Course: Namn och titel: Caihong Xu