>> MS-FIN future students
Reach your goals faster. In only nine months, the MS‑FIN program develops deep expertise in corporate finance and asset/portfolio management, and provides you with the tools needed to manage complex financial institutions.
MS-FIN core courses
MS-FIN courses are delivered sequentially, building on and aligning your knowledge throughout the program, culminating in a capstone project.
The study of contemporary financial accounting and reporting systems, with an emphasis on the interpretation and evaluation of corporate external financial reports.
Introduction to contemporary finance theory and the application of analytical techniques to make optimal decisions under uncertainty. Course topics include statistical properties of asset returns; portfolio optimization techniques using linear algebra and differential calculus; tests of asset pricing using linear regression; sensitivity of capital budgeting outcomes using Monte Carlo simulations; mathematics of bond pricing and option valuation techniques using binomial trees; and the Black-Scholes option pricing model.
Quantitative Methods in Finance I
Empirical investigation of properties of financial data, such as basic probability theory, matrix algebra, ordinary least squares, and maximum likelihood estimation. Explores methods through both algebraic derivation and programmed implementation in Python. Provides the basis for portfolio optimization by focusing on the estimation and testing of financial factor models.
Presents principles of risk and return, portfolio diversiﬁcation, asset allocation, efﬁcient markets, active portfolio management and portfolio evaluation. Requires heavy use of various quantitative techniques to optimize portfolios and measure returns. Includes review of alternative strategies such as hedge fund investments.
Introduction to derivative assets such as futures, forwards, swaps, and options; financial engineering; risk management; and credit derivatives. Covers the pricing of derivative assets; securities with embedded options; and risk management strategies such as static and dynamic hedging in equity, commodity, and bond markets. Requires heavy use of quantitative methods and theoretical reasoning, with a view toward understanding the dynamics of instruments and associated markets.
Quantitative Methods in Finance II
Empirical investigation of financial data, using techniques such as autoregressive and vector-autoregressive models, dimension-reduction techniques motivated by latent factor models, and machine learning dimension-reducing techniques. Methods are explored using algebraic derivation and implementation in Python. Builds upon statistical and programming skills developed in FIN 509 and emphasizes forecasting for the optimization of portfolios.
Advanced Corporate Finance
Study of major decision-making areas and selected topics in corporate finance. Topics generally focus on quantitatively measuring the firm’s economic value creation proposition for investors from prospective and historical perspectives. Integrates microeconomic, statistical, and financial concepts to build working knowledge of financial valuation theory.
Dedicated to the valuation of fixed income securities. Studies the unique features of major types of fixed income securities, and develops a unifying framework of quantitative tools for the valuation of fixed income instruments. Relies on statistical and econometric analysis of market data to estimate key risks in fixed income securities — such as default risk, inflation risk, and interest rate risk — and builds an analytical apparatus for risk management in fixed income portfolios.
Rigorous introduction to key established principles in the financing opportunity set available to startup firms. Topics include assessment of aggregate financial risk and the impact upon the venture proposal; decision rules to discriminate among various funding sources by calculating the cost-benefit relationship from each source and the evaluation of the nascent startup venture; and using time value mathematics and statistical concepts to develop a risk-adjusted value for the startup.
Examines valuation and risk management in the context of real and financial international investment. Topics include development and application of equilibrium relationships among exchange rates, prices, and interest rates. Discrete-time and continuous-time financial mathematics is applied to international project valuation and the use of derivative instruments in hedging against foreign currency risk.
Financial Risk Management
Focuses on various aspects of interest rate, credit risk and other aspects of financial risk management. Topics include duration and convexity approximations using Taylor Series expansions; the pricing of path dependent interest rate swaps by Monte Carlo simulations; implementation of JPMorgan Risk Metrics and Credit Metrics models; KMV-Moody’s EDF probability of default model implementation; pricing of credit default swaps; hedge fund capital structure arbitrage strategies and mortgage derivatives using structural models; and determination of optimal securitization structures using binomial trees. Requires understanding of calculus and probability distribution functions.
Artificial Intelligence and Machine Learning Applications in Finance
Exploration of modern artificial intelligence and machine learning technologies, applications, techniques, and implications for financial decision-making. Knowledge of linear algebra, basic probability theory and calculus along with programming skills in Python are required for this course.
Applied Science in Finance (Capstone)
Designed to assess comprehensive understanding of the aggregate content of finance and associated courses in the program. Requires students to build and manage a comprehensive multi-asset/multi-security/multi-instrument portfolio for a given level of risk-aversion using time series analysis, portfolio optimization techniques, and other quantitative methodologies.