Filtered HSS Courses (2021-22)

Weekly seminar by a member of the Caltech Social Sciences faculty to discuss a topic of their current research or teaching at an introductory level. The course can be used to learn more about different areas of study and about undergraduate courses within the Social Sciences. The course will also be useful to those interested in pursuing the BEM, EC or PS options, or participating in research (SURF, for example) under supervision of the Social Science faculty. The students will be asked to write a discussion of each seminar presentation. Graded pass/fail.

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.

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.

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.

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.

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.