Teaching

In this module, the students will learn basic and advanced econometric methods aimed at modeling and forecasting the relationship between financial market variables. The application of quantitative methods in financial research and practice includes numerous use cases such as modeling and forecasting future prices, volatilities, correlations, etc. They are therefore an indispensable tool for financial analysts, portfolio managers, risk managers and other professionals in the financial industry.

In addition to learning new quantitative methods, this course focuses on a research-oriented approach. Starting with the simple linear regression model, the students will learn how to read and replicate research papers. After replication, they will get to know and apply advanced methods such as GARCH, AR, VAR, ARIMA, PCA and regularized linear models and compare them with simple linear regression.

Upon completing a finance and investments course, students will have acquired a basic understanding of the principles, theories, and practical applications within the realm of finance and investment management. Students will gain knowledge of various financial markets, including equity markets, bond markets, foreign exchange markets, and derivatives markets. Students will understand the concept of time value of money and learn how to use DCF models to evaluate the present value of future cash flows. They will be able to calculate the intrinsic value of an investment by discounting its expected cash flows at an appropriate discount rate. 

Students will gain knowledge of portfolio theory and learn how to construct efficient investment portfolios. This includes understanding the principles of diversification, asset allocation, and risk-return trade-offs. They will be able to apply techniques such as the Markowitz mean-variance optimization framework to select portfolios that maximize expected returns for a given level of risk or minimize risk for a given level of return.

Upon completing this course, participants will have developed a range of valuable competencies: Specifically, they will be proficient in using statistical software such as Matlab to analyze empirical data. This includes mastering essential programming concepts like loops, conditional statements, and branching within programming languages. Furthermore, they will possess the skills necessary to plan their approach when tackling complex scientific questions. This involves structuring material independently and creating outlines autonomously.

Participants will also excel in conducting targeted research, particularly within databases and other relevant information sources. They will be adept at evaluating the quality and reliability of diverse data sources, essential for conducting robust empirical studies. Moreover, they will be capable of designing and executing simple empirical investigations, demonstrating their understanding of scientific methodologies. Finally, they will have the ability to effectively communicate their findings through the preparation of well-written scientific works. This encompasses the synthesis of research findings, analysis, and conclusions in a coherent and scholarly manner.

Upon completing this course, participants will have developed a range of valuable competencies: Specifically, they will be proficient in using the statistical software R to analyze empirical data. This includes mastering essential programming concepts like loops, conditional statements. Furthermore, they will possess the skills necessary to plan their approach when tackling complex scientific questions. This involves structuring material independently and creating outlines autonomously.

Upon completion of a machine learning in finance course, students will have acquired a comprehensive understanding of the intersection between machine learning techniques and financial applications. Students will grasp foundational concepts in both machine learning and finance, including but not limited to regression analysis, classification algorithms, time series analysis, risk management, portfolio optimization, and financial modeling. They will become proficient in preprocessing financial data, including cleaning, normalization, and feature engineering, ensuring that data is suitable for analysis by machine learning algorithms. Students will learn to select and implement appropriate machine learning algorithms for various financial tasks, such as predicting asset prices, identifying trading signals, and detecting anomalies or fraud. Through hands-on projects and case studies, students will gain practical experience in applying machine learning techniques to real-world financial data sets, reinforcing their understanding of theoretical concepts and best practices.