The Asset Management curriculum is still under development, but it will cover 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, leaders from the Yale Investments Office, and guest lecturers from firms such as AQR and Bridgewater.
Principal and Co-Head of the Global Stock Selection Team at 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.
The following are sample course descriptions being developed for the Asset Management program. Details may change before program launch in fall 2020.
This course provides the theoretical background and economics underlying asset pricing theory and used to describe the levels and dynamics of asset prices in markets.
Financial Empirical Methods
This course derives the empirical models and methods for testing asset pricing theory and analyzing financial security price data. 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. Financial econometrics is the intersection of statistical techniques and finance. Financial econometrics provides a set of tools that are useful for modeling financial data and testing beliefs about how markets work and prices form. Conversely, new techniques in analyzing financial data can lead to empirical facts inconsistent with existing theories, begging for new models or investment strategies.
Speculation and Hedging in Financial Markets
Financial markets provide investors with a rich set of opportunities to hedge and speculate on a multitude of risks, offering significant potential benefits to asset managers, individual investors, and firms exposed to such risks. In this course we develop the framework needed to understand the financial instruments investors can use to trade many macroeconomic and financial risks. We examine a variety of risks including those based on exchange rates, interest rates, commodities, real estate, weather events, systemic risk, crash risk, and volatility risk. We first develop the theoretical framework for pricing derivatives, and then study empirically the characteristics of risk and returns in the different markets. The course will have a substantial empirical component, analyzing the performance of equity-based and derivative-based trading strategies.
This course delves into 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. Attention will also be paid to how these concepts fit within the theoretical paradigms of risk-based and behavioral asset pricing theory.
Big Data and Machine Learning in Finance
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.
Hedge Fund Strategies
The class describes some of the main strategies used by hedge funds and proprietary traders and provides a methodology to analyze them. In class and through exercises and projects, the strategies are illustrated using real data and students learn to use “backtesting” to evaluate a strategy. The class also covers institutional issues related to liquidity, margin requirements, risk management, and performance measurement. The class is highly quantitative. As a result of the advanced techniques used in state-of-the-art hedge funds, the class requires the students to work independently, analyze and manipulate real data, and use mathematical modeling.
Research in the field of behavioral finance aims to improve our understanding of financial markets, investor behavior, and corporate finance using frameworks that are psychologically more realistic than their predecessors—frameworks that, in particular, allow for less-than-fully-rational thinking on the part of some individuals in the economy. The emergence of this field is one of the biggest conceptual developments in financial economics over the past 30 years. Over the course of the semester, we will discuss dozens of academic papers.
Implementation and Trading
Focus on implementation and trading execution of portfolios and models in real-time markets. Using theory on trading and market design, develop algorithms and models to trade and approximate trading costs from live data. Incorporate trading cost models and expectations into analysis of backtesting a strategy and in designing a strategy or model. Optimization techniques incorporating trading costs. Assessing, evaluating, and managing the risk of a strategy and portfolio of strategies at both the investment and firm level. Models of risk management applied to data and simulations to assess risk and how to monitor it. Optimization techniques that incorporate risk assessment into portfolio and strategy design, as well as implementation of the portfolio.
Asset Management Colloquium
Lectures by leaders in the field of asset management. Four to five speakers per semester.