Teaching

Please find an overviewed of some courses. If you have any specific questions about course topics, feel free to message my office, and a teaching staff member will respond as soon as possible.

Machine Learning in Finance

Winter Semester


This course focuses on the application of machine learning techniques to asset pricing in financial markets. Students will explore key machine learning methods, including supervised and unsupervised learning, and deep learning, tailored to the challenges of asset pricing. The course emphasizes practical implementation, teaching students how to develop predictive models for stock returns and factor models. Through a combination of lectures and hands-on programming sessions using real financial data, students will gain the analytical skills needed to apply machine learning in asset pricing and enhance their understanding of market efficiency and dynamics. 

Summer Semester

This seminar focuses on the application of data science techniques to asset pricing in financial markets. Students will explore advanced topics in asset pricing, including factor models, risk premia, and market anomalies, alongside data science methodologies. The seminar is designed to enhance students' understanding of how data-driven approaches can be applied to develop and test asset pricing models. Through a combination of literature review, empirical research, and hands-on data analysis, students will undertake projects that apply these techniques to real-world financial data, presenting their findings and contributing to discussions on contemporary challenges in asset pricing.

Winter Semester

This course provides a comprehensive exploration of asset pricing theories and portfolio optimization techniques. It covers foundational models, such as the Capital Asset Pricing Model (CAPM) and multi-factor models, along with their applications in estimating expected returns and assessing risk. The course also delves into portfolio construction and optimization strategies, focusing on methods like mean-variance optimization, the Black-Litterman model, and robust optimization techniques. Students will engage with real-world data and case studies to develop skills in constructing efficient portfolios, evaluating investment strategies, and understanding the trade-offs between risk and return in various market conditions. 

Summer Semester

The lecture Investment and Financial Management covers theories and best practices related to firms’ financial and investment decisions. Regarding financial decisions, upon completion of this module, students will be able to understand the time value of money and how to use financial information to make decisions, such as reading balance statements and using company performance indicators to find an adequate capital structure and estimate the cost of capital. Furthermore, students will be able to apply concepts of capital budgeting and investment analysis to choose among investments.

Winter Semester


This seminar is designed to deepen undergraduate students' understanding of financial valuation techniques and their application in financial management. The course emphasizes practical skills in valuing companies, projects, and investment opportunities using various methodologies, including discounted cash flow (DCF) analysis, relative valuation, and option pricing models. Students will apply their theoretical knowledge to real-world cases, conducting comprehensive valuations and developing investment recommendations.