MS&E 446 Artificial Intelligence in Financial Technology.
This course will survey the current Financial Technology landscape through the lens of Artificial Intelligence applications, with emphasis in 4 areas: Payments, Blockchain and Cryptocurrencies, Robo-Advisory, and Marketplace Lending. Students work in groups of 4 to develop an original financial technology project, research paper or product prototype within a chosen area. Final project posters to be presented to the class and posted online. Top posters to be selected and presented at the Stanford Financial Technology conference in January. Classes will alternate between industry speakers, lectures and scheduled group meetings with teaching team. Advanced undergraduates, graduate students, and students from other Schools are welcome to enroll. Prerequisites: Basic programming skills, knowledge of design process, introductory Statistics. No formal finance experience required. Enrollment is capped at 32. 3 units, letter graded. Autumn.
MS&E 246 Financial Risk Analytics. Course Website
This course provides a practical introduction to financial risk analytics. The focus is on data-driven modeling, computation, and statistical estimation of credit and market risks. Case studies based on real data will be emphasized throughout the course. Topics include mortgage risk, asset-backed securities, commercial lending, consumer delinquencies, marketplace lending, transactions analytics, derivatives risk. Tools from machine learning and statistics will be developed. Data sources will be discussed. The course is intended to enable students to design and implement risk analytics tools in practice. Winter.
MS&E 347 Credit Risk: Modeling and Management. Course Website
This course treats the quantitative modeling of credit risk using stochastic point processes. Topics include point process transforms, point process simulation, likelihood inference and fitness testing for event timing models, corporate bonds, credit swaps, forwards and options on credit swaps, index swaps, tranches, cash collateralized debt obligations, portfolio credit risk. Practical examples are given. Market data will be emphasized. Grading will be based on a team project. Prerequisites: knowledge of stochastic processes at the level of MSE 321, 322 or equivalent, and knowledge of financial engineering at the level of MSE 342, MATH 180, MATH 240, F 622 or similar. Winter.
MS&E 245A Investment Science.
Introduction to the basic concepts of modern quantitative finance and investments. Focus is on basic principles and how they are applied in practice. Topics: basic interest rates; evaluating investments: present value and internal rate of return; fixed-income markets: bonds, yield, duration, portfolio immunization; term structure of interest rates; measuring risk: volatility and value at risk; designing optimal security portfolios; the capital asset pricing model. Group projects involving financial market data. No prior knowledge of finance required. Appropriate for engineering or science students wishing to apply their quantitative skills to develop a basic understanding of financial modeling and markets. Prerequisites: knowledge of basic probability, statistics and economics (MSE 120, 121, MATH51, ENGR 60, or equivalents). No prior knowledge of finance is assumed. Autumn.
MS&E 444 Investment Practice. Course Website
This is a projects course. Students work in small teams to tackle projects co-developed with an industry partner. No prior knowledge in finance necessary, but strong quantitative skills for tackling market data-rich problems are key. At the end of the quarter, each team presents the project to the class and representatives from the project sponsor. Spring.
Finance. Course Website
A free online course.