My teaching interests are in FinTech, corporate finance, quantitative methods in finance, and risk management. Whenever possible, I try to teach theoretical and empirical concepts in finance with numerical examples, small cases, and assign “hands-on” exercises.

I consistently receive high evaluations from my students and feedback such as

“David has been very good to make me understand how the codes work and in general what FinTech in practice is about. The codingexercises David has chosen really shows how such skills can be used in real life. Eventhough much of the stuff seems very complicated David is really good at explaining it in an easy and non-complicated way. This is what makes this course unique.” (Corporate FinTech, Spring 2019)

“David is definitely one of the most motivating teachers I have had. Taking complicated stuff to a understandable level. Makes the lessonsworth a lot compared to other courses.” (Corporate FinTech, Spring 2019)

“We have had guest lecturers, who have demonstrated how the contents of the course is applied in selected industries. Very nice!” (Corporate FinTech, Spring 2019)

“He wants us to show him that we understand what he is talking about he is asking question s to clarify that we understood or in order tomake us participate in the lecture” (Derivatives and Risk Management, Fall 2019)

“The Excel walk troughs in class are AWESOME” (Derivatives and Risk Management, Fall 2019)

“Good with the excel worksheets that we have in class. Also, David is a very nice teacher, because he makes sure that everybody understands.” (Derivatives and Risk Management, Fall 2019)

Graduate Level/Doctoral

  • FinTech (Disruptive und innovative Ansätze)
  • IU International University, BSc, E-Learning, since 2022
  • Topics: Crowdfunding, Social Trading, Robo Advisory, Payment-Lösungen, Big Data Geschäftsmodelle, Smart Contracts.
  • FinTech (Überblick und technologische Grundlagen)
  • IU International University, BSc, E-Learning, since 2022
  • Topics: Grundlagen, internetbasierte Plattformlösungen, Automatisierung Geschäftsprozesse, Machine Learning und KI-basierte Systemem, Big Data, Kryptografie, Blockchain, Disintermediation durch FinTech.
  • Corporate Finance
  • IU International University, MSc, E-Learning, since 2022
  • Topics: Portfolio and capital market theory, capital structure, capital budgeting, business valuation, M&A, corporate governance.
  • Corporate FinTech
  • University of Southern Denmark, MSc, Spring 2019, 2020, 2021
  • Topics: Financial Data Analytics, Cryptocurrencies, Initial coin offerings (ICOs), Crowdfunding. Methods: Python programming and case studies, Machine Learning, Natural Language Processing (NLP).
  • Derivatives and Risk Management
  • University of Southern Denmark, MSc, Fall 2017, 2018, 2019, 2020
  • Topics: Forwards and futures, stock options, hedging, risk-neutral valuation, term structure models, Monte Carlo simulation, stochastic calculus. Methods: Excel case studies.
  • Topics in Finance
  • University of Southern Denmark, MSc, Spring 2018, 2020
  • Topics: Credit ratings, credit risk modeling, estimating default probabilities, credit portfolio models,
    selected empirical methods (e.g. event studies). Methods: Excel case studies.
  • Advanced Topics in Financial Management
  • HKUST Business School, MSc, Spring 2017
  • Topics: Basic valuation, agency costs of debt and equity, corporate governance, executive compensation, capital structure, raising funds, IPO. Methods: Case studies.
  • Risk Management for Financial Institutions
  • HKUST Business School, MSc, Spring 2017
  • Topics: Banking regulation, credit ratings, credit risk modeling, estimating default probabilities, credit default swaps, market risk modeling, estimating Value-at-Risk. Methods: Excel case studies.
  • Empirical Corporate Finance
  • LMU Munich, Doctoral/MSc, Summer 2012, 2014, 2016
  • Topics: Students replicate selected research papers in empirical corporate finance and develop their own research proposal (for Master or PhD thesis). Methods: Stata, research proposal presentation.
  • Investment Banking
  • LMU Munich, MSc, Summer 2015
  • Topics: Students read selected research papers in investment banking and conduct an event study analysis of a M&A transaction. Methods: Stata, event study, research presentation.
  • Quantitative Methods
  • LMU Munich, Doctoral, Winter 2014
  • Topics: Estimation frameworks, statistical inference, OLS, endogeneity, instrumental variables, limited dependent variables, panel data estimation. Methods: Monte Carlo simulation, Matlab case studies.
  • Advanced Risk Management
  • LMU Munich, MSc, Winter 2011
  • Topics: Credit ratings, estimating default probabilities, default correlation, credit portfolio models, credit default swaps, market risk modeling, estimating Value-at-Risk. Methods: Excel case studies.

Undergraduate Level

  • Mergers, Acquisitions, and Corporate Restructuring
  • HKUST Business School, Fall 2016
  • Topics: Basic valuation, deal structuring, merger strategy, stock market reactions, merger arbitrage, takeover tactics and defenses, leveraged buyouts, corporate restructuring. Methods: Case studies.
  • Banking and Risk Management
  • HKUST Business School, Fall 2016
  • Topics: Banking regulation, credit ratings, credit risk modeling, estimating default probabilities, credit default swaps, market risk modeling, estimating Value-at-Risk. Methods: Excel case studies.
  • Commercial Banking
  • LMU Munich, Winter 2015
  • Topics: Financial intermediation, banking regulation, credit ratings, estimating default probabilities, credit default swaps, concentration risk, securitization. Methods: Excel case studies.
  • Risk Management
  • LMU Munich, Winter 2014
  • Topics: Financial options, binomial option pricing model, Black-Scholes option pricing model, insurance, hedging of commodity price risk, exchange rate risk, and interest rate risk. Methods: Excel case studies.
  • Seminar “The Theory of Going Public”
  • LMU Munich, Summer 2016
  • Topics: Theoretical models of the decision to go public. Methods: Monte Carlo simulation, research presentation.
  • Seminar “Endogeneity in Empirical Corporate Finance”
  • LMU Munich, Winter 2011, 2015, Summer 2014
  • Topics: Omitted variables bias, simultaneity, measurement error. Methods: Monte Carlo simulation, research presentation.

Executive Level

  • Financial Training
  • InvestmentDataServices GmbH (Allianz SE), Every Fall/Spring, 2015 – present
  • Topics: Time value of money, equity valuation, portfolio selection and CAPM, bond valuation, term structure of interest rates, derivatives. Methods: Excel case studies.


  • Corporate Finance (Business Informatics) 
  • German University in Cairo, Berlin Campus, Undergraduate Level, Winter 2022
  • Certified Risk Manager CRM: Kreditratings und Ratingverfahren 
  • DVFA Deutsche Vereinigung für Finanzanalyse und Asset Management, Executive Level, Lecture, Spring 2020
  • Fundamentals of Finance: Equity 
  • Goethe Business School, Executive Level, Lecture, Spring 2016
  • Financial Management 
  • EM Lyon, Graduate Level, Tutorial, Fall 2015
  • Principles of Corporate Finance (Investition und Finanzierung
  • LMU Munich, Undergraduate Level, Course held in German,  Winter 2012
  • Financial Analysis Using MATLAB 
  • LMU Munich, Undergraduate Level, Lecture, Winter 2007