Curriculum: Quantitative Finance Graduate Certificate

This is an introductory masters level course in econometrics emphasizing econometrics foundations and financial data analysis. The course covers topics in time series analysis with an emphasis on applications rather than econometric theory. The course is designed to enable students to perform independently comprehensive financial data analysis using statistical software packages.

Analysis of financial data is a core component of investment management. You need to be comfortable and adept at sampling, modeling, regression analysis, and hypothesis design and testing in order to be an effective financial analyst. In this course we will cover many of the basic statistical and probability concepts that are central to financial analysis. Along the way we will touch on various finance concepts and terms, so, in part, this course will provide you with a conceptual introduction to various investment topics.

This course examines fixed-income markets, with an emphasis on the pricing and risk of fixed income securities, derivatives, and portfolios. Bond immunization and trading strategies will be discussed with an in-depth coverage of both Treasury and Corporate Debt Securities. We will explain how Federal Reserve uses monetary policy to influence the term structure of interest rates.This course helps students to establish a solid foundation in understanding fixed-income securities and furthermore to apply such knowledge to real-world investment decisions in bond markets.

The principal objective of this course is to provide a detailed examination of options, futures, forwards, and swaps. By the end of the course students will have a good knowledge of how these contracts work, how they are traded, how they are used, and how they are priced. A major emphasis in the class will be on how derivative instruments are used by financial institutions in light of recent economic events.

This course introduces students to applied research and applications in quantitative equity investing. First, students will learn about the empirical evidence related to prominent equity factors including size, value, and momentum. Second, students will learn how to construct and backtest factor strategies using real data. Finally, students will be exposed to real-world applications of factor investing in quantitative asset management via case studies. The goal of the course is to equip students with necessary knowledge and skills for applied research in quantitative equity management. More broadly, this course can be a useful part of training for students who are interested in a career in financial data analytics.

This course provides a framework for developing real-world machine learning systems that are deployable, reliable, and scalable. Designing machine learning systems is the process of defining the software architecture, infrastructure, algorithms, and data for a machine learning system to satisfy specified business requirements. Without deliberate design, machine learning systems can get outdated quickly because (1) the tools continue to evolve, (2) business requirements change, and (3) data distributions constantly shift. Students will learn about data management, data engineering, feature engineering, approaches to model selection, training, scaling, and how to continually monitor and deploy changes to ML systems for successful business applications. They will also be exposed to managing the human side of ML projects such as team structure and business metrics.

The main objective of the course is to introduce students to fintech and cryptocurrency. The goal is to understand the fundamental concepts underlying financial technologies and their applications. Examples of these include artificial intelligence, machine learning, and blockchain in financial markets, such as business activities, financing, and investments. The course consists of four parts. The first part introduces the status quo and fundamentals of financial technologies (fintech) as well as their main applications, including artificial intelligence, payments, robo-advising, insure-tech, and blockchain. The second part covers the mechanisms and applications of artificial intelligence and machine learning by focusing on the use of natural language processing (NLP) and large language models (LLM). The third part focuses on blockchain and cryptocurrencies, discussing Bitcoin, Ethereum, stablecoin, and NFTs. The last part of the course focuses on cryptocurrency markets and portfolios. The cryptocurrency market comprises exchanges (both on-chain and off-chain) and spot and derivative contracts. Cryptocurrency portfolio analysis studies the risk and return tradeoff of such portfolios. The course combines lectures, class discussions, and case study analyses.

This course provides a comprehensive treatment of the fundamental principles required to design and implement algorithmic trading models in financial markets. The course will introduce the best practices and the formal process of generating trading ideas, the differences between low-frequency and high-frequency trading signals, back-testing and its associated biases, optimization techniques, and industry metrics for evaluating algorithmic trading models’ performance. Students will have the opportunity to implement basic algorithms in well-known paper-trading platforms.