Machine Learning in Finance

Recent developments in analytical techniques, massive increases in computing power, and growing amounts of data have led to an unprecedented rise in machine learning applications across many areas of finance, such as asset pricing (Gu et al. 2020; Bianchi et al. 2021), earnings forecasts (van Binsbergen et al. 2022), and credit scoring (Fuster et al. 2021). Clearly, machine learning has become an important skill for any aspiring financial researcher.

The goal of this course is to provide participants with a solid methodological foundation in machine learning models, demonstrate how these methods can be applied to address financial research questions, and enable the participants to analyze both structured and unstructured data using machine learning in R.
At the end of the course, participants will apply their knowledge in a Kaggle coding competition, where they team up with fellow participants to develop the most effective prediction model for a prediction task in a financial context.

The course will be offered online via Zoom. After the kickoff session in week 1, weeks 2 – 5 each include a 4-hour lecture and a 4-hour computer lab session. Afterwards, there will be a small Kaggle coding competition with group presentations of the results.

Days: April 13, 20-21, 27-28, May 4-5, 11-12, 25, 2027

Anmeldefrist: March 13, 2027

Sprache / Language

Deutsch

Ort / Location

Tickets

Noch 20 Plätze verfügbar.