Filtered HSS Courses (2022-23)
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 data science for financial applications using the Python programming language and its ecosystem of packages. Students will learn how to use Python to extract numerical and text data from websites, evaluate fundamental risk-reward trade-offs using real-world financial data, and combine quantitative and qualitative factors into a persuasive argument. Time permitting, we will cover topics in machine learning, algorithmic trading, and cryptocurrencies. Each student is expected to complete individual problem sets and a final project. Some programming experience is helpful though knowledge of Python is not assumed.
The main objective of the course is to develop insight into the process by which firms can create value for their shareholders. We will study major corporate decisions from the perspective of the firm with an emphasis on the interaction of the firm with financial markets: quantitative project evaluation for investment, choice between borrowing and issuing stock, dividend policy, organizational form (for example, mergers and acquisitions). Theory, empirical evidence, and case analysis all play significant roles in the course. Topics include discounted cash flow models, risk and return, capital asset pricing model, capital market efficiency, capital structure and the cost of capital and dividend policy. Not offered 2022-23.
In this course we will go over recent works on topics broadly contained in the newly emerging field of Fintech. In particular, the topics include mathematical modeling of strategic actions of agents interacting via a blockchain technology, via crowdfunding platforms, and via online investment platforms ("robo-advisors"). Not offered 2022-23.
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.
An introduction to the theory and practice of venture capital financing of start-ups. This course covers the underlying economic principles and theoretical models relevant to the venture investment process, as well as the standard practices used by industry and detailed examples. Topics include: The history of VC; VC stages of financing; financial returns to private equity; LBOs and MBOs; people versus ideas; biotech; IPOs; and CEO transitions. Not offered 2022-23.
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.
The course offers an introduction to international financial markets, their comparative behavior, and their inter-relations. The principal focus will be on assets traded in liquid markets: currencies, equities, bonds, swaps, and other derivatives. Attention will be devoted to (1) institutional arrangements, taxation, and regulation, (2) international arbitrage and parity conditions, (3) valuation, (4) international diversification and portfolio management, (5) derivative instruments, (6) hedging, (7) dynamic investment strategies, (8) other topics of particular current relevance and importance. Not offered 2022-23.
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.
Much of modern financial economics works with models in which agents are fully rational, in that they maximize expected utility and use Bayes' law to update their beliefs. Behavioral finance is a large and active field that develops and studies models in which some agents are less than fully rational. Such models have two building blocks: limits to arbitrage, which makes it difficult for rational traders to undo the dislocations caused by less rational traders; and psychology, which provides guidance for the kinds of deviations from full rationality we might expect to see. We discuss these two topics and consider a number of applications: asset pricing; individual trading behavior; the origin of bubbles; and financial crises. Not offered 2022-23.
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). Not offered 2022-23.
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.