Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

By the end of the course students, students will be familiar with basic quantitative methods used for analyzing financial data and the empirical asset pricing literature; be able to implement those skills in the context of potential applications for portfolio construction using a programming language. Also, students will be faimiliar with basic concepts of machine learning and its applications to quantitative finance.

Credits: 6

Schedule: 11.01.2023 - 20.02.2023

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Sean Shin

Contact information for the course (applies in this implementation):

CEFR level (valid for whole curriculum period):

Language of instruction and studies (applies in this implementation):

Teaching language: English. Languages of study attainment: English

CONTENT, ASSESSMENT AND WORKLOAD

Content
  • valid for whole curriculum period:

    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.

Assessment Methods and Criteria
  • valid for whole curriculum period:

    The final grade (0 5 scale) is based on total points (max 100 points); combining assignments and exam points. The exam can be substituted by additioanl assignments depending on situation. 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

Workload
  • valid for whole curriculum period:

    Classroom hours  
    Class preparation  
    Assignments/projects  
    Preparing Exam  
    Exam 

    Total 160h (6 op)

DETAILS

Substitutes for Courses
Prerequisites
SDG: Sustainable Development Goals

    1 No Poverty

    8 Decent Work and Economic Growth

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language : English

    Teaching Period : 2022-2023 Spring III
    2023-2024 No teaching

    Enrollment :

    This course is only open for BIZ students. A maximum of 70 students can be accepted to the course.

    Students at Aalto Finance M.Sc. programme (i.e. who have graduated as B.Sc.) will be guaranteed a seat on Finance M.Sc. courses. Students re-taking the course (grade already registered) will not be prioritized and can participate only if there are places remaining.

    Remaining seats are prioritized as follows:

    1. Finance M.Sc. exchange students from other universities
    1. Aalto Finance B.Sc. students with a finished B.Sc. thesis (registered in transcript of records)
    3. All other M.Sc. students at the School of Business

    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.