- Teacher: Ann-Marie Åkers
Hanken Moodle
Sökresultat: 263
- Teacher: Anna Ahlskog
- Teacher: Jennie Bertula
- Teacher: Marika Finne
- Teacher: Sarah Hagström
- Teacher: Carl Hobbs
- Teacher: Susanne Holmlund
- Teacher: Alex Thilman
- Teacher: Olli-Pekka Kauppila
- Teacher: Jennie Sumelius
- Teacher: Mayvor Höglund
- Teacher: Matti Kukkonen
- Teacher: Henrik Palmen
- Teacher: Kenneth Högholm
- Teacher: Toni Sundqvist

Obligatorisk grundkurs inom kandidatexamen.
Kursnyckel: mfs2025hfors
- Teacher: Åke Finne
- Teacher: Gustav Medberg
- Teacher: Sickan Åberg
- Teacher: Peter Björk
- Teacher: Lisa Niemistö
- Teacher: Annika Ravald
- Teacher: Frida Nyqvist
- Teacher: Fredrik Weibull

- Teacher: Denis Davydov

- Teacher: Stefan Burggraf
- Teacher: Robert Ciuchita
- Teacher: Alexander Back
- Teacher: Karl Felixson
- Teacher: Jesper Haga
- Teacher: Kenneth Högholm
- Teacher: Toni Sundqvist
- Teacher: Emilia Vähämaa
R (quantitative analyses, simulations and graphics)
and RMarkdown (generation of documents with code chunks and text chunks
[text chunks include latex syntax for typesetting math formulas])
are used in rstudio https://rstudio.com/ in the course.
Students are supposed to acquire hand-on experience with R
by applying various techniques used in mathematical and quantitative finance
to synthetic and real data. No prior knowledge of programming is required.
Self-enrollment key: please, check 'Messages' in SISU about one week before the start of the course.
- Teacher: Agnieszka Jach
- Teacher: Martin Scheffel
- Teacher: Kenneth Högholm
- Teacher: Kenneth Högholm
- Teacher: Jesper Haga
- Teacher: Toni Sundqvist
- Teacher: Paulo Fraga Martins Maio
- Teacher: Benjamin Maury

Contents:
Introduction to Statistical Learning/Machine Learning
Subset Selection
Regularisation Methods: Ridge Regression and Lasso
Bootstrap
Tree-Based Methods
Tree-Based Methods for Classification
Unsupervised Learning: PCA, PCR and Clustering
Neural Networks
Articles using ML in Finance (NM)
Practical advice about Machine Learning
Ethics in Machine Learning
After completing the course, you will be able to:
Analyse financial data using machine learning methods
Use machine learning methods for prediction and decision making in Finance
Apply machine learning methods to solve research problem in Finance
- Teacher: Niklas Ahlgren
- Teacher: Theogene Habimana
- Teacher: Christian Johansson
- Teacher: Niclas Meyer
- Teacher: Emilia Vähämaa
- Teacher: Natalia Boltovskaia
- Teacher: Jesper Haga
- Teacher: Kenneth Högholm
- Teacher: Emilia Vähämaa
- Teacher: Kam-Ming Wan
Detta är den gemensamma Moodlesidan för kurserna 17160 för svenskspråkiga hankeiter och 17170 för övriga.
This is the combined Moodle page for the courses 17170 for non-Swedish speakers, and 17160 for Swedish speaking Hankeits.
Please read the course description carefully! It is available outside the course Moodle page. Please also attend the introductory lecture. The introductory lecture is held on Monday, 20 January 2025 at 14.15 pm. in room A304. The enrolment key will be distributed at least twice, i.e., once in the beginning of January, and another time about one week before the course starts. Please contact jan.antell@hanken.fi if you did not receive it.
Take measures to come up with a topic. Note that the topic is to be submitted already some one week after the introductory lecture. Check the data availability to the detail, as no data = no thesis.
In many cases, the model structure is as follows:
where
is a variable of interest, and
is a vector of control variables. When considering a topic, think of which variables that should be variables of specific interest, and which are control variables, i.e., variables known or expected to have an association with the phenomenon under study, but that are not of special interest. You need data both for variables of interest and for control variables.
- Teacher: Jan Antell
Kursen är en självstudiekurs där du rapporterar skriftligen efter din för ditt huvudämne relevanta arbetspraktik. Du kan välja att avlägga praktikkursen om antingen 5 sp eller 10 sp. För 5 sp behövs minst 8 arbetsveckor heltidsarbete och för 10 sp krävs minst 16 arbetsveckor.
Praktiken kan avläggas under en eller flera perioder och på en eller flera arbetsplatser. Om du är antagen till den integrerade kandidat- och magisterutbildningen kan rapporten hänvisa till arbetspraktik som utförts efter antagningen till kandidatnivån. Om du är antagen direkt till studier för endast magisterexamen på Hanken kan rapporten hänvisa till arbetspraktik som utförs under studietiden eller upp till högst tre år före antagning.
Notera att du får avlägga högst 10 sp praktik inom din magisterexamen. Således kan du av praktikkurserna på 5 sp och 10 sp bara avlägga en.
- Teacher: Kenneth Högholm
- Teacher: Denis Davydov

- Teacher: Matteo Vacca
- Teacher: Jan Antell
- Teacher: Eva Liljeblom

- Teacher: Matteo Vacca
- Teacher: Neema Komba
- Teacher: Chanyoung Park
- Teacher: Joakim Vincent
- Teacher: Tom Lahti
- Teacher: April Spivack
- Teacher: Torkel Tallqvist

- Teacher: Chanyoung Park
- Teacher: April Spivack

- Teacher: Henrik Höglund
- Teacher: Eva Ström
- Teacher: Karolina Söderlund
- Teacher: Timmy Thor

- Teacher: Henrik Höglund
- Teacher: Eva Ström
- Teacher: Karolina Söderlund
- Teacher: Timmy Thor
- Teacher: Mansoor Afzali
- Teacher: Theresia Harrer
- Teacher: Aaron Afzali
- Teacher: Hanna Silvola

- Teacher: Henrik Höglund
- Teacher: Dennis Sundvik

- Teacher: Theresia Harrer
- Teacher: Henrik Höglund
- Teacher: Mansoor Afzali

- Teacher: Henrik Höglund

- Teacher: Eva Ström
- Teacher: Karolina Söderlund
- Teacher: Karolina Söderlund
- Teacher: Karolina Söderlund
- Teacher: Karolina Söderlund