The Stanford Quantitative Finance Program consists of five courses, each involving approximately 24-26 hours of instruction. The courses are designed to meet the demands of professionals by bridging the gap between theory and practice in finance. They take a practical approach. Models and methods are applied in a hands-on fashion to real problems. Each course will contain data-driven examples and in-class exercises that are designed to reinforce learning. The goal is to introduce students to the best practices in finance.
The Program features top professors of the Department of Management Science and Engineering at Stanford University. These professors conduct cutting-edge research in the areas in which they teach. They also have significant practical experience, often gained through extensive consulting activities or starting and running financial or investment firms.
The Program is offered on a part-time basis, and the individual courses are offered approximately every eight weeks. Students are encouraged to take a foundation course that will be offered online during the orientation week.
The individual course modules are as follows.
The Program takes a quantitative approach to finance. A certain level of mathematical knowledge is beneficial. This includes basic understanding of equations, what a variable is, the use of logarithms and exponentials, and some familiarity with mathematical manipulations and data analysis. The Foundation Course will review the basic mathematical concepts and tools required to make the most out of the Program. It will be available online through Stanford's VentureLab platform. Students who have the required background need not take this course.
This course covers the foundational topics in quantitative finance, including risk and risk measures such as value at risk, option pricing, implied volatility, and hedging. Students will learn about the statistical properties of asset returns, how to measure the risk of securities and asset portfolios, the latest risk management regulations, how to model the behavior of stock prices, how to value and hedge derivative securities such as forwards, options, and swaps, how to compute derivatives prices and hedges, and how to estimate parameters from financial market data. Practical examples are used to illustrate theoretical concepts, and to give students an appreciation of market data and conventions. Faculty: Professor Peter Glynn.
This course module provides an introduction to fixed income and credit markets. It discusses government bonds, corporate bonds, repos, mortgages, interest rate swaps, credit default swaps, and structured products such as collateralized debt obligations and mortgage-backed securities. We will emphasize trading, pricing, and hedging problems. Topics covered include the relative pricing of securities with fixed cash flows, measures of interest rate risk and hedging, bond portfolio management, term structure analysis, repo markets, and default risk. The course will also discuss recent developments in the financial technology area pertinent to credit markets, including online (aka p2p or marketplace) lending to consumers and small businesses, fintech regulation, and fintech business opportunities. Practical, data-driven case studies will be used to illustrate the material. These case studies will feature real data sets (mortgages, consumer and small business loans, CFPB complaints) and highlight the application of machine learning algorithms for analyzing risk.
This course will review the concepts underlying the construction of equity portfolios, discuss alternative model formulations, their impact on portfolio performance, and discuss the important principles underlying active portfolio management. It will be shown how to measure and control for total risk and active risk of a portfolio based on the portfolio's exposures to various risk factors. It will be discussed how to use factors to construct portfolios that have the potential to outperform a benchmark. It will also be shown how to account for and reduce the impact of estimation error in determining optimal equity portfolio strategies. Practical examples illustrate the concepts and insights. Case studies based on US (and potentially international) equity data will be presented throughout the course. Portfolio optimization software will be used for constructing real-life portfolios and for back-testing of strategies. Faculty: Professor Gerd Infanger.
The goal of this course is to build a foundation in statistical tools for the analysis of financial data. In case studies statistical software will be used to study the basic characteristics of financial data, estimate, and test financial models. Students will build models for stock prices, interest rates and other quantities and learn how to predict future outcomes. Faculty: Professor Markus Pelger
This module delivers a comprehensive introduction to algorithmic trading and discusses some big data techniques that are useful in algorithmic trading. The course begins with a review on how systematic trading can be performed under modern electronic infrastructures. We also highlight some regulatory and risk management issues that are commonly encountered in algorithmic trading. Then, the stylized empirical facts of high frequency and low frequency transactions data are studied. From these empirical facts, we analyze share price models under different sampling frequencies and introduce corresponding volatility estimation techniques. In addition to transactions data, the electronic platforms of the exchanges also disseminate data of limit order book (LOB) that record the configurations of outstanding limit orders in a high frequency manner. These LOB data play pivotal role in the development of trading strategies. Thus, the mathematical models of LOB, such as Hawkes processes and queuing models, are presented as one of the main themes of this course. Some commonly used algorithms for optimal execution of assets are also discussed. The application of big data techniques in algorithmic trading is another main theme of this course and is reviewed under two topics. The first one is on the implementation of smart order routing via real-time learning algorithms, i.e., how to use machine learning algorithms to optimally distribute the orders over many exchanges or dark pools. The second one is on the reinforcement learning and its relationship with the multi-period investment strategies that are derived by solving complex optimization problems which incorporate the non-negligible transaction cost incurred from rebalancing the trading positions over the investment horizon.
Machine learning tools, such as LASSO (least absolute shrinkage and selection operator) and other model shrinkage methods, deep learning and deep neural networks, and, CART (classification-and-regression-trees) and random forest, are studied under this topic. The statistical software, R, is used to illustrate how to (a) perform analysis on real market data, (b) simulate mathematical models, and (c) back-test trading strategies.