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Sample Courses

The Asset Management curriculum covers a range of topics, including investment theory, investment practices, and trading and portfolio execution. Courses will be taught and often co-taught by Yale SOM’s full-time finance faculty, Yale Law School and economics faculty, leaders from the Yale Investments Office, and adjunct lecturers from firms such as AQR Capital Management, Bridgewater Associates, and Cyrus Capital Partners.

Andrea Frazzini

Principal and Head of Global Stock Selection, AQR Capital Management

In our industry, we are constantly looking for bright people who have rigorous quantitative training plus an understanding of how theories and conceptual tools can be applied to markets. The Yale finance faculty are at the forefront of research in asset valuation and market dynamics, and they maintain close relationships with investors, providing students of the Yale Master’s in Asset Management with exceptional instruction on both sides of that equation. By co-teaching many classes with practitioners, they strengthen the bridge between theory and practice—an ideal environment in which to start a career in asset management.

Sample Courses

Asset Management Colloquium

Six to eight talks per year by leaders in the field of asset management. Potential topics include the following: client relations, cryptocurrencies, data technology, discretionary macro, real assets, risk management, short selling, and venture capital.

Asset Pricing Theory

This course provides the theoretical background and economics underlying asset pricing theory used to describe the levels and dynamics of asset prices in markets. The course will cover the classic CAPM as well as multifactor models motivated from several theories. Conditional asset pricing models and macroeconomic models will also be discussed.

Behavioral Finance

The field of behavioral finance tries to make sense of investor behavior, financial markets, and corporate finance using models that make psychologically realistic assumptions about the way people think, e.g., that allow for less than fully rational thinking. In this course, we cover both the classic contributions to the field and also the most recent research developments.

Financial Econometrics and Machine Learning

This course derives the empirical models and methods for testing asset pricing theory and analyzing financial security price data. Financial econometrics provides a set of tools that are useful for modeling financial data and testing beliefs about how markets work and prices form. The second part of the semester focuses on the latest techniques for applying big data and machine learning to problems in asset management. Students will learn a series of methods and techniques for handling big data and using machine learning and AI techniques as well as applying the state-of-the-art methods to financial problems, including some examples currently being applied at top quant firms. 

Investment Analysis and Private Equity

This case-driven course has ambitious, pragmatic goals: to teach a set of core skills, processes, concepts, and historical perspectives with sufficient rigor to enable students to engage in the actual work of either making private and public equity investments or managing others who do. Major course themes will include: an information-centric understanding of risk management; factors that drive investment returns, especially in illiquid equities; the use of probability-weighted scenarios in assessing and managing investments; financial engineering; patterns of business development; leadership and investment outcomes; and shareholder and stakeholder interests.

Quantitative Investing

This course delves into quantitative factor investing, the basic building blocks of quantitative models of investing. We will explore the scientific evidence and theory behind factor investing and examine its applications, including index funds, smart beta, quantitative hedge fund models, and performance evaluation. The course is primarily empirically focused with heavy data applications. Students will replicate studies and design their own tests of theories and apply them to the data. Attention will also be paid to how these concepts fit within the theoretical paradigms of risk-based and behavioral asset pricing theory.