2014-2015 2015-2016 2016-2017 2017-2018 2018-2019
by subject...

141 - 150 of 165 results for: MS&E

MS&E 403: Integrative Modeling

Modeling approaches for examining real life problems: how to get started. Critical thinking in framing and problem formulation leading to actionable solutions and communication of results to decision makers. Models to identify and evaluate multiple objectives/metrics. Models examined include both deterministic and probabilistic components. Overview of optimization and probability, decomposition principles to model large scale problems, appropriate integration of uncertainties into model formulations. Primarily team-project based assignments, with three to four group projects. Project topics drawn from applications with real data. Sample project topics include: optimizing group phone plans for large corporations, life insurance business models, making sense of the health care debate, logistic decision problems. Project teams will critically grade other teams¿ project reports using provided guidelines. Project presentations throughout the quarter. Prerequisites: 211, 220.
Terms: Win | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Chiu, S. (PI)

MS&E 408: Directed Reading and Research

Directed study and research on a subject of mutual interest to student and faculty member. Prerequisite: faculty sponsor.
Terms: Aut, Win, Spr, Sum | Units: 1-15 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 408A: Directed Reading and Research

Directed study and research on a subject of mutual interest to student and mentor.
Terms: Aut, Win, Spr, Sum | Units: 1-4 | Repeatable for credit | Grading: Letter or Credit/No Credit

MS&E 431: Projects in Computational Social Science

Students work in interdisciplinary teams to complete a project of their choice in computational social science. Groups present their progress throughout the term, receiving regular feedback on their own projects and providing feedback on other students' projects. Students learn how to deal with the computational and statistical challenges of working with large, real-world datasets in the context of a motivating, substantive problem in the social sciences. Lectures and discussions are tailored to the specific topics that the groups pursue. Enrollment is by application only; details will be posted in the fall quarter. Prerequisite: MS&E 231 or similar.
Terms: not given this year | Units: 3-4 | Grading: Letter (ABCD/NP)

MS&E 441: Policy and Economics Research Roundtable (PERR)

Research in progress or contemplated in policy and economics areas. Emphasis depends on research interests of participants, but is likely to include energy, environment, transportation, or technology policy and analysis. May be repeated for credit.
Terms: Aut, Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit

MS&E 445: Projects in Wealth Management

Recent theory and standard practice in portfolio design for institutions, individuals, and funds. Student projects and case studies derived from the financial industry.
Terms: Spr | Units: 3-4 | Grading: Letter (ABCD/NP)

MS&E 447: Systemic and Market Risk : Notes on Recent History, Practice, and Policy

The global financial crisis of 2007-8 threw into sharp relief the ongoing challenges of understanding risk, the financial system, links with the global economy, and interactions with policy. We will explore elements of the crisis, a few other key events, and ongoing debates about systemic risk. Group projects will explore in more detail past events and current topics in systemic risk. Supplements a rigorous technical curriculum in modern finance with select aspects relevant to understanding the practice and broader context of modern financial activities such as derivatives, financial engineering, and risk management.
Terms: Aut | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Wong, A. (PI)

MS&E 448: Big Financial Data and Algorithmic Trading

Project course emphasizing the connection between data, models, and reality. Vast amounts of high volume, high frequency observations of financial quotes, orders and transactions are now available, and poses a unique set of challenges. This type of data will be used as the empirical basis for modeling and testing various ideas within the umbrella of algorithmic trading and quantitative modeling related to the dynamics and micro-structure of financial markets. Due to the fact that it is near impossible to perform experiments in finance, there is a need for empirical inference and intuition, any model should also be justified in terms of plausibility that goes beyond pure econometric and data mining approaches. Introductory lectures, followed by real-world type projects to get a hands-on experience with realistic challenges and hone skills needed in the work place. Work in groups on selected projects that will entail obtaining and cleaning the raw data and becoming familiar with techniques and challenges in handling big data sets. Develop a framework for modeling and testing (in computer languages such as Python, C++ , Matlab and R) and prepare presentations to present to the class. Example projects include optimal order execution, developing a market making algorithm, design of an intra-day trading strategy, and modeling the dynamics of the bid and ask. Prerequisites: MS&E 211, 242, 342, or equivalents, some exposure to statistics and programming. Enrollment limited. Admission by application; details at first class.
Terms: Spr | Units: 3 | Grading: Letter (ABCD/NP)
Instructors: Borland, L. (PI)

MS&E 449: Buy-Side Investing

In-class lectures and guest speakers who work in the Buy-Side to explore the synergies amongst the various players¿ roles, risk appetites, and investment time and return horizons. We aim to see the forest and the different species of trees growing in the forest known as the Buy-Side, so as to develop a perspective as financial engineers for how the ecosystem functions, what risks it digests, how it generates capital at what rate and amount for the Sell-Side, and how impacts in the real economy are reflected - or should be reflected - in the culture and risk models adopted by the Buy-Side participants.
Terms: Win | Units: 1-2 | Grading: Satisfactory/No Credit
Instructors: Cahan, B. (PI)

MS&E 450: Lessons in Decision Making

Entrepreneurs, senior management consultants, and executives from Fortune 500 companies share real-world stories and insights from their experience in decision making.
Terms: Spr | Units: 1 | Repeatable for credit | Grading: Satisfactory/No Credit
Instructors: Shachter, R. (PI)
Filter Results:
term offered
updating results...
number of units
updating results...
time offered
updating results...
updating results...
UG Requirements (GERs)
updating results...
updating results...
updating results...
© Stanford University | Terms of Use | Copyright Complaints