printer friendly pageMS&E 240: Accounting for Managers and Entrepreneurs (MS&E 140)
Non-majors and minors who have taken or are taking elementary accounting should not enroll. Introduction to accounting concepts and the operating characteristics of accounting systems. The principles of financial and cost accounting, design of accounting systems, techniques of analysis, and cost control. Interpretation and use of accounting information for decision making. Designed for the user of accounting information and not as an introduction to a professional accounting career.
Terms: Spr, Sum
| Units: 3
Instructors:
Lord, J. (PI)
;
Damian, M. (TA)
;
Jha, A. (TA)
;
Rajesh, N. (TA)
;
Sperla, E. (TA)
;
Vu, K. (TA)
MS&E 241: Economic Analysis (MS&E 141)
Principal methods of economic analysis of the production activities of firms, including production technologies, cost and profit, and perfect and imperfect competition; individual choice, including preferences and demand; and the market-based system, including price formation, efficiency, and welfare. Practical applications of the methods presented. Recommended: 111 or 211, and
ECON 50.
Terms: Win
| Units: 3-4
Instructors:
Sweeney, J. (PI)
;
Elfarsdottir, A. (TA)
MS&E 243: Energy and Environmental Policy Analysis
Concepts, methods, and applications. Energy/environmental policy issues such as automobile fuel economy regulation, global climate change, research and development policy, and environmental benefit assessment. Group project. Prerequisite: MS&E 241 or
ECON 50.
Terms: Spr
| Units: 3
Instructors:
Sweeney, J. (PI)
;
Elfarsdottir, A. (TA)
MS&E 244: Statistical Arbitrage
Practical introduction to statistical arbitrage, which typically refers to trading strategies that are bottom up, market neutral, with trading driven by statistical or econometric models. Models may focus on tendency of short term returns to revert, leads/lags among correlated instruments, volume momentum, or behavioral effects. A classic statistical arbitrage program is relatively high frequency over a large universe of stocks and is driven algorithmically. This course discusses a taxonomy of market participants and what motivates trading, data: different types, how to obtain data, timestamps, errors and dirty data, methods of exploring relationships between instruments, forecasting, portfolio construction across a large number of instruments, trading: the execution of portfolio changes in real markets, risks inherent in statistical arbitrage, nonstationarity of relationships due to changes in market regulations, fluctuations in market volatility and other factors, frictions such as c
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Practical introduction to statistical arbitrage, which typically refers to trading strategies that are bottom up, market neutral, with trading driven by statistical or econometric models. Models may focus on tendency of short term returns to revert, leads/lags among correlated instruments, volume momentum, or behavioral effects. A classic statistical arbitrage program is relatively high frequency over a large universe of stocks and is driven algorithmically. This course discusses a taxonomy of market participants and what motivates trading, data: different types, how to obtain data, timestamps, errors and dirty data, methods of exploring relationships between instruments, forecasting, portfolio construction across a large number of instruments, trading: the execution of portfolio changes in real markets, risks inherent in statistical arbitrage, nonstationarity of relationships due to changes in market regulations, fluctuations in market volatility and other factors, frictions such as costs of trading and constraints and how strategies scale, analysis of strategies. Prepares students with valuable skills for engaging in quantitative trading in a hedge fund or investment bank trading desk, understanding how to evaluate quantitative strategies from the point of view of an investor or asset allocator, including performance evaluation, risk analysis, and strategy capacity analysis. Occasional hands-on data projects supporting weekly topics. Weekly lectures and a final data-driven project. The objective of the final project is to build, test and analyze some kind of statistical arbitrage strategy. Prerequisites: MS&E 245A or similar, some background in probability and statistics, working knowledge of R, Python or similar computational/statistical package.
Terms: Spr
| Units: 3
Instructors:
Demers, D. (PI)
;
Zanotti, G. (TA)
MS&E 245A: Investment Science
Basic concepts of modern quantitative finance and investments. Focus is on the financial theory and empirical evidence that are useful for investment decisions. 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 portfolios; risk-return tradeoff: capital asset pricing model and extensions. No prior knowledge of finance is required. Concepts are applied in a stock market simulation with real data. Prerequisite: basic preparation in probability, statistics, and optimization.
Terms: Aut
| Units: 3-4
Instructors:
Pelger, M. (PI)
;
Archetti, E. (TA)
;
Duan, J. (TA)
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Instructors:
Pelger, M. (PI)
;
Archetti, E. (TA)
;
Duan, J. (TA)
;
Fan, Y. (TA)
;
Lerner, S. (TA)
;
Zanotti, G. (TA)
MS&E 245B: Advanced Investment Science
Formerly MS&E 342. Topics: forwards and futures contracts, continuous and discrete time models of stock price behavior, geometric Brownian motion, Ito's lemma, basic options theory, Black-Scholes equation, advanced options techniques, models and applications of stochastic interest rate processes, and optimal portfolio growth. Computational issues and general theory. Teams work on independent projects. Prerequisite: 245A.
Terms: Spr
| Units: 3
MS&E 246: Financial Risk Analytics
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, online lending, 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. Prerequisites: MS&E 245A or similar, some background in probability and statistics, working knowledge of R, Matlab, or similar computational/statistical package.
Terms: Win
| Units: 3
Instructors:
Giesecke, K. (PI)
;
Epstein, E. (TA)
MS&E 248: Blockchain and Crypto Currencies
Blockchain is one of the most significant technologies to impact law and business in many years. Blockchain is also one of the most interdisciplinary areas, bringing together new questions, and opportunities at the intersection of technology, business and law. This course is designed to employ this interdisciplinary nature, provide an overview of the technology behind blockchain, and explore current and potential real-world applications in technology, business and law. This is a lecture, discussion, and project-oriented class. Each lecture will focus on one of the topics, including a survey of the state-of-the-art in the area and in-depth discussion of the topic. Each week, students are expected to complete reading assignments before class and participate actively in class discussion.
Terms: Spr
| Units: 3
Instructors:
LaBlanc, G. (PI)
MS&E 249: Corporate Financial Management (MS&E 146)
Key functions of finance in both large and small companies, and the core concepts and key analytic tools that provide their foundation. Making financing decisions, evaluating investments, and managing cashflow, profitability and risk. Designing performance metrics to effectively measure and align the activities of functional groups and individuals within the firm. Structuring relationships with key customers, partners and suppliers. Limited enrollment. Recommended: 145, 245A, or equivalent.
Terms: Win
| Units: 3-4
MS&E 250A: Engineering Risk Analysis
Techniques of analysis of risk management decisions in engineering and other systems involving preferences and trade-offs (technical, human, environmental aspects). Elements of decision analysis; probabilistic risk analysis in the public or private sector (fault trees, event trees, systems dynamics); Bayesian updating and learning (elementary notions of quantum computing for complex cases); value of tests, economic analysis of failure consequences (human safety and long-term economic discounting); case studies such as space systems, nuclear power plants, medical systems and cyber security. Pre-requisites: probability, stochastic processes, and convex optimization.
Terms: Win
| Units: 3
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