Mads Stenbo Nielsen

About

View research profile and publications

E-mail

msn.fi@cbs.dk

Telephone

Office: +4538153602

Mobile: +4541852540

Departments

Department of Finance

Room: SOL/A4.28

View research profile and publications

Subjects

Statistics Investment Big data Machine learning Quantitative methods ESG

Primary research areas

Machine learning in finance

A wide range of machine learning models find natural applications in finance. From risk management and return prediction to cluster analysis and optimal investment decisions. I work to explore which methods work the best in a given application.

Sustainability in investments

Sustainability is the new norm in finance. A wide variety of metrics such as CO2 emissions, ESG, SDG, EU taxonomy etc. provide a granular picture of a security’s sustainability profile. At the same time they also pose many questions about which metrics matter the most for a given security or investment decision. I work to explore the intricate links between sustainability and the risk/return tradeoff.

Credit risk modeling

Credit risk affects both a firm’s equity and debt, and hence also affects pricing of many securities. I explore how robust mathematical models can be used to measure and predict credit risk for stocks, bonds, and derivatives.

I put financial theory into practice

Based on a solid theoretical background in mathematics and statistics, I work to explore how theory finds its way into everyday practice, whether it is pricing of corporate bonds, minimizing risk in diversified portfolios, assessing the impact of sustainability on return performance, or predicting future stock returns.

I strive to make a difference by exploiting the strength of deep theoretical insights with a practical perspective on what is actually possible to implement. I exploit big data, machine learning, mathematics, and common sense to foster new ideas and create new solutions to a wide variety of finance and investment problems.