Courses 2026-27: Business Economics & Management

This course offers advanced undergraduates the opportunity to pursue research on a business problem individually or in a small group. Graded pass/fail.

Topics determined by instructor.

This course combines accounting and finance in a dynamic, user-oriented approach. The goal is to enable students to understand what financial statements are (sources of information about a company), what they are not (facts devoid of interpretation or management influence), and how to critically understand and analyze them. The course will utilize actual SEC filings for several companies, across a variety of industries, through which the students will be exposed to important accounting concepts.

Finance, or financial economics, covers two main areas: asset pricing and corporate finance. For asset pricing, a field that studies how investors value securities and make investment decisions, we will discuss topics like prices, risk, and return, portfolio choice, CAPM, market efficiency and bubbles, interest rates and bonds, and futures and options. For corporate finance, a field that studies how firms make financing decisions, we will discuss topics like security issuance, capital structure, and firm investment decisions (the net present value approach, and mergers and acquisitions). In addition, if time permits, we will cover some topics in behavioral finance and household finance such as limits to arbitrage and investor behavior.

Examines the theory of financial decision making and statistical techniques useful in analyzing financial data. Topics include portfolio selection, equilibrium security pricing, empirical analysis of equity securities, fixed-income markets, market efficiency, and risk management.

An introduction to option pricing theory and risk management in the discrete-time, bi-nomial tree model, and the continuous time Black-Scholes-Merton framework. Both the partial differential equations approach and the martingale approach (risk-neutral pricing by expected values) will be developed. The course will cover the basics of Stochastic, Ito Calculus. Since 2015, the course is offered in the flipped format: the students are required to watch lectures online, while problem solving and case and paper presentations are done in class.

This course introduces students to the principles and practice of data science in economics and finance. The goal is not only to learn technical tools, but to understand how empirical researchers design, evaluate, and interpret data-driven analyses. The course is structured around the data science pipeline: formulating questions, constructing datasets, representing data, estimating models, evaluating predictions, and translating results into decisions. Along the way we develop a framework for distinguishing prediction from causal inference, understanding heterogeneity in economic data, and evaluating models critically rather than treating them as black boxes. Students will learn how to work with structured and unstructured data, including text, and will gain exposure to modern machine learning methods. The course also introduces large language models and their role in modern empirical workflows, including how they can be used for data construction, labeling, and analysis. The course emphasizes verification, reproducibility, and critical evaluation of model outputs. Students are expected to attend all classes, actively participate in discussions, and complete two in-class exams in addition to problem sets.  Course open only to juniors and seniors.  This is not a writing intensive course.

Why do firms exist? What determines the boundaries of the firm? How are firms governed? How are firms financed? How do firms contract with employees and contractors? Do firms have a moral responsibility to society, or to shareholders? In this course, we develop a rigorous framework for understanding the logic of the firm. Building on a foundation of economic theory, we will analyze historical and contemporary corporate leaders, the decisions they faced, and the repercussions of those actions. The aim of this course is to equip students with a rigorous framework for understanding the way the business world works, and to help them navigate their careers.

This course looks at the implications of the fact that investors demand reward for taking risk. Concepts of Knightian risk and uncertainty; risk preference (risk-neutral Q vs. real-world P probability measures); coherent risk; and commonly used metrics for risk are explored. The integration of risk and reward in classical efficient portfolio construction is described, along with the drawbacks of this approach in practice and methods for addressing these drawbacks. The leptokurtic (fat-tailed) nature of financial data and approaches to modeling financial surprises are covered, leading to inherently leptokurtic techniques for estimating volatility and correlation. Scenario analysis, and regime-switching methods are shown to provide ways of dealing with risk in extreme environments. Fixed income portfolio factors including interest rates and credit are examined. The class also considers hedging techniques. The text for the class includes python code segments in Jupyter Notebooks.

This course combines elements of business, economics, engineering, financial statement analysis, strategy, and law to provide students interested in entrepreneurship with a practical understanding of the mechanics of growing a 'post-idea' company. The class will explain how prospective investor’s view entrepreneurs and their ideas, teach students about types of capital, sources of capital, and term sheets, and generally delve into the timing and financial alternatives and trade-offs facing entrepreneurs seeking capital in order to launch or grow a company. As such, this class is a complement to BEM 110 (Venture Capital) and E 102 (Scientific and Technology Entrepreneurship).

This course is an in-depth study of the hedge fund industry. We will study hedge fund trading strategies, the business model of hedge funds, hedge fund investors, as well as the institutional and regulatory framework in which hedge funds operate. The course will evaluate and analyze popular hedge fund trading strategies, including equity strategies (activist, market-neutral, long/short, event-driven, etc.), arbitrage strategies (derivatives, convertible, fixed-income, currency and global macro, etc.), and fund of hedge funds. The course will also analyze the hedge fund business model, including: performance evaluation and risk management; fund compensation and contractual features; transaction costs and market impact; as well as fund raising and marketing. In addition, the course will study the institutional relationships hedge funds have with service providers (prime brokers, custodian banks, etc.) and with regulators. We will also discuss public policy implications and the value of hedge funds in society. This course is designed to provide students with the skills necessary to evaluate hedge fund strategies, and to develop, manage, and successfully grow a hedge fund business.

The use of large data sets and innovative statistical methods has revolutionized professional and intercollegiate sports. This course introduces students to the academic and professional world of contemporary sports science. The course will meet biweekly with instructor lectures on sports science and with guest speakers from collegiate and professional sports. Students will be introduced to the primary data sources for sports science, to methods used to collect sports performance and outcomes data, and to the statistical tools used for sports analytics (for example, logistic regression, regression trees and random forest, network models, time series, and natural language processing). Students will be responsible for weekly writing or homework assignments based on readings and speaker presentations, as well as a quarter-long sports analytics research project. Students should have some background in econometrics, statistics and probability, data science, or machine learning.