Schedule: 27.02.2020 - 08.04.2020
Teacher in charge (valid 01.08.2018-31.07.2020):
Contact information for the course (applies in this implementation):
Feel free to ask any questions via email.
Teaching Period (valid 01.08.2018-31.07.2020):
IV (Spring 2019) Otaniemi campus
IV (Spring 2020) Otaniemi campus
Learning Outcomes (valid 01.08.2018-31.07.2020):
By the end of the course students, students will be familiar with basic quantitative methods used for analyzing financial data; be able to implement those skills in the context of potential applications for portfolio construction using a programming language.
Content (valid 01.08.2018-31.07.2020):
This course covers basic quantitative skills for analyzing financial data. This course also reviews basic academic papers related to empirical topics, such as behaviors of securities prices relative to the benchmark asset pricing models.
Details of assignments will be provided by the lecturer through MyCourses.
Details on the course content (applies in this implementation):
The following contents are tentative and subject to change as needed:
Course Orientation and Introduction.
Evaluating asset returns: Time-Series vs. Cross-sectional tests.
Quality Investing, Betting against Beta, and Q-factor Model
Group Presentation: Key Paper and Project Execution Plan
Factor Investing: is it robust?
Investing Other Asset Market: Fixed Income Markets
Big Data and Machine Learning in Finance Research
Big Data and Machine Learning in Finance Research (cont.)
Final Group Presentation 1
Final Group Presentation 2
Assessment Methods and Criteria (valid 01.08.2018-31.07.2020):
The final grade (0 – 5 scale) is based on total points (max 100 points); combining assignments (50 %) and exam (50 %) points. To pass the course, you have to get at least 40% of exam points, i.e. 20 points. Conditional on that, your final grade is based on the following scale:
90≤x≤100: Final grade = 5
80≤x<90: Final grade = 4
70≤x<80: Final grade = 3
60≤x<70: Final grade = 2
50≤x<60: Final grade = 1
0≤ x<50: Final grade = 0, Fail
Elaboration of the evaluation criteria and methods, and acquainting students with the evaluation (applies in this implementation):
Workload (valid 01.08.2018-31.07.2020):
Classroom hours 24h
Class preparation 40h
Preparing Exam 34h
Total 160h (6 op)
Details on calculating the workload (applies in this implementation):
Study Material (valid 01.08.2018-31.07.2020):
Readings, slides, and other materials will be provided by the lecturer through MyCourses.
Details on the course materials (applies in this implementation):
Prerequisites (valid 01.08.2018-31.07.2020):
Investment Management (28C00300) and Econometrics for Finance (28C00200), or equivalent courses.
Grading Scale (valid 01.08.2018-31.07.2020):
Registration for Courses (valid 01.08.2018-31.07.2020):
The course registration is done via WebOodi and closes always 7 days before the first lecture/ start of the teaching period. The study coordinator will check the registrations first and then discuss with the lecturer and provide the lists. We have student quotas for our M.Sc. level courses and we use prioritization list.
Further Information (valid 01.08.2018-31.07.2020):
A maximum of 40 students can be accepted to the course.
First priority are Finance M.Sc. programme students (i.e. who have graduated as B.Sc.) and CEMS students (those courses that have been designated as CEMS courses).
Remaining seats are prioritized as follows, in the order of registration in WebOodi within one category:
1. Finance M.Sc. exchange students from other universities
2. Aalto Finance B.Sc. students with a finished B.Sc. thesis (registered in transcript of records)
3. All other M.Sc. students
Please follow carefully the registration deadlines of the courses and exams! Missing registration deadline automatically foregoes a guaranteed seat for Finance M.Sc. courses and puts prospective student at the bottom of the prioritization list.
Students may be required to confirm their registration by signing the participation list circulated during the first lecture.
Additional information for the course (applies in this implementation):
Details on the schedule (applies in this implementation):
This will be announced during the first class.
- Teacher: Sean Shin