Courses 2024-25: 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 revolves around qualitative topics and quantitative methods in data science for finance and economics. We discuss linear models and inference, sources of bias, databases and data validation, scraping, clustering, and text analysis. A special emphasis is placed on developing a robust understanding of the strengths and weaknesses of social science research methods. The class also introduces students to the Python programming language and its ecosystem of packages.

In this course we will develop a deep understanding of the institutional foundations of Chinese finance, and we will use this framework to study the strengths and weaknesses of the Chinese economic system through the lens of finance. We will start from a historical overview of Chinese finance and will study the institutions that drive financial market development. Next, we will focus on the unique economic features of the three main channels of Chinese finance: capital markets (including the stock and bond markets), bank- and fund-based intermediation (including the banking sector, shadow-banking, and private equity, and venture capital), and informal finance. Finally, we will study the opportunities and challenges posed by Chinese-style finance for the future development of the global financial system.

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. The special nature of modeling long/short portfolios (hedge funds) is explored. The text for the class is a Jupyter Notebook with Python code segments.

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.

This course will serve as an introduction to the American legal system emphasizing concepts which students are likely to encounter in their professional lives such as Constitutional law (federalism; states’ rights; structure of court systems), dispute resolution (litigation; mediation; arbitration), contracts (elements; defenses to formation; remedies), employment law, business associations (partnerships; corporations; LLCs), securities regulation, and intellectual property (patents; trademarks; trade secrets; copyrights). By learning to identify issues which would alert one to the need for a lawyer, the class will sensitize students to key legal issues they may encounter in the normal course of business. Textbook reading and lectures will be supplemented by practicing lawyers expert in their field of business law as guest speakers.

This course provides a survey from the perspective of economics of public policy issues regarding the management of natural resources and the protection of environmental quality. The course covers both conceptual topics and recent and current applications. Included are principles of environmental and resource economics, management of nonrenewable and renewable resources, and environmental policy with the focus on air pollution problems, both local problems (smog) and global problems (climate change).

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.