Marat Molyboga Ph D CFA

Designed curriculum for a Master-level Quantitative Investment Strategies class that covers Asset Pricing Theory, Fund Selection, Asset Allocation, Risk Management, and Trade Execution. The class has been featured on the school website as an innovative class that provides real-world experience. Class objectives focus on students acquiring important investment industry skills such as statistical modeling, hypothesis testing, model estimation, and validation, writing research reports, presenting research findings, and solving challenging quantitative real-life investment and risk problems under the guidance of mentors from the industry. Developed curriculum for a PhD-level Investments class that covers statistical estimation strategies, hypothesis testing, sampling methods, modeling, and validation. The class provides a comprehensive review of frequentist, Bayesian, and Machine Learning theory and empirical methods and guidance on selecting the right statistical tools for a given problem. The class covers many popular topics such as market anomalies, APT testing, return predictability, hedge fund performance evaluation, skill detection, empirical asset pricing with machine learning, and text analysis. Created a series of Python-based problem sets that include practical implementation of the techniques covered in class.