CME 232: Introduction to Computational Mechanics (ME 332)
Provides an introductory overview of modern computational methods for problems arising primarily in mechanics of solids and is intended for students from various engineering disciplines. The course reviews the basic theory of linear solid mechanics and introduces students to the important concept of variational forms, including the principle of minimum potential energy and the principles of virtual work. Specific model problems that will be considered include deformation of bars, beams and membranes, plates, and problems in plane elasticity (plane stress, plane strain, axisymmetric elasticity). The variational forms of these problems are used as the starting point for developing the finite element method (FEM) and boundary element method (BEM) approaches providing an important connection between mechanics and computational methods.
Terms: not given this year

Units: 3

Grading: Letter (ABCD/NP)
CME 237: Networks, Markets, and Crowds (MS&E 237)
The course explores the underlying network structure of our social, economic, and technological worlds and uses techniques from graph theory and economics to examine the structure & evolution of information networks, social contagion, the spread of social power and popularity, and information cascades. Prerequisites: basic graph and probability theory.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
CME 238: Artificial Intelligence in Financial Technology (MS&E 446)
Survey the current Financial Technology landscape through the lens of Artificial Intelligence applications, with emphasis in 4 areas: Payments, Blockchain and Cryptocurrencies, RoboAdvisory, 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.
Terms: Aut

Units: 3

Repeatable for credit

Grading: Letter (ABCD/NP)
CME 239B: Workshop in Quantitative Finance (STATS 239B)
Topics of current interest. May be repeated for credit.
Terms: not given this year

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
CME 242: Mathematical and Computational Finance Seminar (MS&E 446A, STATS 239)
Terms: Aut, Spr

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Jain, K. (PI)
CME 243: Risk Analytics and Management in Finance and Insurance (STATS 243)
Market risk and credit risk, credit markets. Back testing, stress testing and Monte Carlo methods. Logistic regression, generalized linear models and generalized mixed models. Loan prepayment and default as competing risks. Survival and hazard functions, correlated default intensities, frailty and contagion. Risk surveillance, early warning and adaptive control methodologies. Banking and bank regulation, asset and liability management. Prerequisite:
STATS 240 or equivalent.
Terms: not given this year

Units: 3

Grading: Letter or Credit/No Credit
CME 244: Project Course in Mathematical and Computational Finance
For graduate students in the MCF track; students will work individually or in groups on research projects.
Terms: Aut, Win, Spr, Sum

Units: 16

Grading: Letter (ABCD/NP)
Instructors:
Jain, K. (PI)
;
Horel, E. (TA)
CME 245: Topics in Mathematical and Computational Finance
Description: Current topics for enrolled students in the MCF program: This course is an introduction to computational, statistical, and optimizations methods and their application to financial markets. Class will consist of lectures and realtime problem solving. Topics: Python & R programming, interest rates, BlackScholes model, financial time series, capital asset pricing model (CAPM), options, optimization methods, and machine learning algorithms. Appropriate for anyone with a technical and solid applied math background interested in honing skills in quantitative finance. Prerequisite: basic statistics and exposure to programming.Can be repeated up to three times.
Terms: Aut, Win, Spr, Sum

Units: 1

Repeatable for credit

Grading: Satisfactory/No Credit
Instructors:
Jain, K. (PI)
;
Sircar, K. (PI)
CME 249: Using Design for Effective Data Analysis
Teams of students use techniques in applied and computational mathematics to tackle problems with real world data sets. Application of design methodology adapted for data analysis will be emphasized; leverage design thinking to come up with efficient and effective data driven insights; explore design thinking methodology in small group setting.;apply design thinking to a specific data centric problem and make professional group presentation of the results. Limited enrollment. Prerequisites:
CME100/102/104 or equivalents, or instructor consent. Recommended:
CME106/108 and familiarity with programming at the level of
CME 192/193.
Terms: offered occasionally

Units: 1

Grading: Satisfactory/No Credit
CME 249A: Statistical Arbitrage
Course will cover trading strategies that are bottom up, market neutral, with trading driven by statistical or econometric models and strategies such as pair trading and index arbitrage. Models may focus on tendency of short term returns to revert, leads/lags among correlated instruments, volume momentum, or behavioral effects. nTopics include: (a) a taxonomy of market participants and what motivates trading, (b) methods of exploring relationships between instruments, (c) portfolio construction across a large number of instruments, (d) risks inherent in statistical arbitrage (e) nonstationarity of relationships due to changes in market regulations, fluctuations in market volatility and other factors and (f) frictions such as costs of trading and constraints. Students will team to analyze the provided data sets which cover distinct dynamic market regimes.
Terms: offered occasionally

Units: 1

Grading: Satisfactory/No Credit
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