131 - 140 of 236 results for: MS

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. Prerequisite: microeconomics and welfare economics. Recommended: 111 or 211, and ECON 50.

Introduces various machine learning techniques and their practical applications in quantitative finance and algorithmic trading in three modules. The first module covers various regression methods, focusing on their role in designing pairs trading strategies and statistical arbitrage. The second module introduces the foundation of reinforcement learning, emphasizing its use in high-frequency trading. Finally, the third module explores diffusion models and generative AI, highlighting their potential in financial scenario generation and stress testing for trading strategies.

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.

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.

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.

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.

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.

This course provides a comprehensive overview of blockchain technology and decentralized finance (DeFi). It begins with an in-depth examination of the cryptocurrency value chain, exploring how tokens are issued, listed, and sustained - and uncovering the key players and financial incentives involved. The course then investigates how tokenomics and market design address inefficiencies in traditional finance. Students explore major blockchain and DeFi innovations, including public blockchains, Layer 2 solutions, decentralized exchanges, automated market makers (AMMs), and on-chain prediction markets, emphasizing both their economic principles and technological foundations. Entrepreneurial strategies for leveraging blockchain to reshape global finance are a focal point, culminating in discussions on potential future developments. Industry guest speakers provide practical insights from the field. The course is designed for broad accessibility, with no extensive technical prerequisites.

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.