MFin Curriculum

Required Foundational Courses: During the summer term, you’ll begin with rigorous courses in finance theory, financial mathematics, and financial accounting which provide foundational theories and principles you’ll draw from throughout the program and your career.

Computer Literacy: Students entering the MIT MFin program are expected to possess basic programming skills needed for processing and analyzing data. As part of the degree requirements, all students in the MFin program need to pass the Programming Literacy Test. Entering students will take the PLT at the early in the program using any of the following programming languages: R or Python. Those who do not successfully pass the test will be strongly encouraged to attend coding office hours and must retake the PLT.

Required Advanced Courses: You’ll work with analytical tools — financial modeling, portfolio and pricing theory, statistics and data analytics, and computational methods — to help tackle multi-faceted challenges that arise in finance, from capital budgeting and cash flow analysis, to dynamic asset allocation and big-data-based investment strategies, and more. A required professional development course adds an important dimension, focusing on finance ethics and social responsibility.

Required Action Learning Experience: Our signature finance Action Learning courses integrate financial theory and applications with hands-on work experience alongside financial industry practitioners. These project-based courses challenge students to solve real-world problems with MIT's partner corporations, culminating in student presentations to corporate decision makers.

An Array of Electives: You’ll choose from topics ranging from financial technology, to quantitative methods, to economics, to specialized disciplines such as healthcare finance, fixed income, mergers and acquisitions and asset management.  In addition, MFin students can access other MIT courses or cross-register at Harvard to expand their horizons.

Optional Concentrations: You can choose to further specialize your degree by completing one of three concentrations in financial engineering, capital markets, or corporate finance.

Optional Thesis or Independent Study:  An optional 24-unit thesis or an independent study is available with approval by the faculty director.

Finance Lecture Series and Study Tours: The MFin curriculum is supplemented with an optional lecture series with industry leaders and study tours to broaden exposure to global markets and the financial community.

Core theory of capital markets and corporate finance. Topics include functions and operations of capital markets, analysis of consumption and investment decisions of investors, valuation theory, financial securities, risk analysis, portfolio theory, asset pricing models, theory of efficient markets, as well as capital budgeting and financing and risk management decisions of firms. The course provides a theoretical foundation of finance and its applications.  

Preparation and analysis of financial statements. Focuses on measuring and reporting of corporate performance for investment decisions, stock valuation, bankers' loan risk assessment, and evaluations of employee performance. Emphasizes the required interdisciplinary understanding of business. Concepts from finance and economics (e.g., cash flow discounting, risk, valuation, and criteria for choosing among alternative investments) place accounting in the context of the business enterprise. 

15.454 Financial Mathematics

Provides an overview of essential fundamental mathematics needed for the study of modern finance: linear algebra, probability, stochastic processes, statistics, optimization, and programming in Matlab. 
 

15.455 Advanced Mathematical Methods for Financial Engineering

This course covers advanced mathematical topics essential for research and applications in financial engineering and quantitative finance: linear algebra, optimization, probability, stochastic processes, statistics, and basic programming in R and Matlab. These topics are covered at a more advanced level and at a faster pace than Fundamentals of Financial Mathematics. 

*Students must take either 15.454 or 15.455

This course focuses on the financial theories and empirical evidences that are useful for investment decisions. Its main content includes: financial risk factors, financial models, and financial markets. Financial theory and empirical evidence for making investment decisions. Topics include portfolio theory; equilibrium models of security prices, including the capital asset pricing model and the arbitrage pricing theory; the empirical behavior of security prices; market efficiency; performance evaluation; and behavioral finance. Preference to Course 15 students. 

Introduction to corporate financial management. Topics include capital budgeting, investment decisions and valuation; working capital management, security issues; dividend policy; optimal capital structure; and real options analysis. 

15.450 Analytics of Finance

This course covers the main quantitative methods of finance. It covers three broad sets of topics: financial econometrics, dynamic optimization, and derivative pricing using stochastic calculus. The emphasis is on rigorous and in-depth development of the key techniques and their application to practical problems. Provides a rigorous foundation for the main analytical techniques and quantitative methods necessary to succeed in the financial services industry. Topics include discrete and continuous asset pricing models, financial econometrics, machine learning methods, and dynamic optimization.

Examples of applications include portfolio management, risk management, derivative pricing, and algorithmic trading.

15.457 Advanced Analytics of Finance

This course is the advanced version of 15.450. It introduces a set of modern analytical tools to solve practical problems in finance. The goal is to build operational models, take them to the data, and use them to aid financial decision making.

Topics include: 

1. Overview of frequentist and Bayesian inference 
2. Regression and classification
3. Time series modeling
4. Event studies
5. Machine learning methods for finance
6. Structural approach to extract information from financial data
7. Optimization methodsExample applications are drawn from problems in quantitative trading, credit risk modeling, portfolio optimization, and Fintech.

*Students can take either 15.450 or 15.457

The proseminar provides students a unique opportunity to tackle original research problems in financial engineering that have been posed by leading experts from the financial community. Students are assigned to teams and each team is assigned one such problem. The team's solution is then presented at a seminar, which is open to the entire MIT community. 

This proseminar bridges the gap between finance theory and finance practice, and introduces students to the broader financial community. Students participate in a series of proseminars with industry guest speakers. Each guest, in collaboration with finance faculty, provides a problem and materials to a team of students. Each team then prepares a report and presents their analysis to the guest speaker and other students for evaluation and feedback. 

Bridges theory and practice, providing students with an immersive research and analysis experience. Students work with leading industry practitioners and a diverse cross-section of students on collaborative teams, focusing on topical, real-world finance research questions posed by the practitioners. Teams then deliver a nuanced analysis and report findings, gaining insight and coaching from the experts. Practitioners represent a range of financial institutions, including investment management, hedge funds, private equity, venture capital, risk, and consulting.

This course is mandatory for all MFin students and will take place during the semester.

This special seminar for professional development changes annually. In Spring 2015, this course supplemented the rigorous technical training of the MFin curriculum with an overview of the structure of financial regulation, an introduction to regulatory enforcement, and a primer on regulatory compliance, as well as a summary of the best practices, policies and procedures, and a discussion about the core values and professional conduct in the financial services industry.

Examines the elements of entrepreneurial finance, focusing on technology-based start-up ventures, and the early stages of company development. Addresses key questions which challenge all entrepreneurs: how much money can and should be raised; when should it be raised and from whom; what is a reasonable valuation of the company; and how funding, employment contracts and exit decisions should be structured. Aims to prepare students for these financial decisions, both as entrepreneurs and venture capitalists. In-depth analysis of the structure of the private equity industry. 

Covers advanced topics in corporate finance, including complex valuations, static and dynamic capital structure, risk management, and real options. Also considers security design, restructuring, bankruptcy, corporate control and governance, and international finance issues. 

Examines the economic role of options and futures markets. Topics: determinants of forward and futures prices, hedging and synthetic asset creation with futures, uses of options in investment strategies, relation between puts and calls, option valuation using binomial trees and Monte Carlo simulation, implied binomial trees, advanced hedging techniques, exotic options, applications to corporate securities and other financial instruments. 

Designed for students seeking to develop a sophisticated understanding of fixed income valuation and hedging methods, and to gain familiarity with the major markets and instruments. Emphasizes tools for quantifying, hedging, and speculating on risk. Topics include duration; convexity; modern approaches to modeling the yield curve; interest rate forwards, futures, swaps and options; credit risk and credit derivatives; mortgages; and securitization.

Probably the most dramatic events in a corporation's history involve the decision to acquire another firm or the decision to oppose being acquired. This is also one of the areas of management most thoroughly documented in the financial press and the academic literature. Subject explores three aspects of the merger and acquisition process: the strategic decision to acquire, the valuation decision of how much to pay, and the financing decision on how to fund the acquisition. Class sessions alternate between discussions of academic readings and applied cases. 

Students analyze international financial markets and instruments. Covers topics such as currency markets, exchange rate determination, statistical properties of exchange rates; currency futures and options; hedging foreign exchange risk and managing foreign exchange exposure; international portfolio management; market implications of bubbles, crashes, and crises.  

Provides an introduction to financial engineering, covering topics such as asset pricing theory and applications, optimization, market equilibrium, market frictions, risk management, and advanced topics. Assumes solid undergraduate-level background in calculus, probability, statistics, and programming and includes a substantial coding component. Materials and review sessions use R. Students are encouraged but not required to use R for assignments and projects. 

Applies finance science and financial engineering tools and theory to the design and management of global financial institutions, markets, and the financial system to better understand the dynamics of institutional change and financial product/service design. Focuses on foundational analytical tools - derivative pricing and risk measurement; portfolio analysis and risk accounting; and performance measurement to analyze and implement concepts and new product ideas. Examines the needs of government as user, producer and overseer of the financial system, and how tools are applied to measure and manage risks in financial crises.

Applies finance science and financial engineering tools and theory to asset management, lifecycle investing, and retirement finance. Focuses on foundational analytical tools - derivative pricing and risk measurement, portfolio analysis and risk accounting, and performance measurement to analyze and implement concepts and new product ideas. Students should be familiar with basic portfolio-selection theory, CAPM, options, futures, swaps and other derivative securities.

Covers rational and behavioral aspects of consumer financial decision making; current household financial products and competitive landscape in credit, investment, and advising markets; consumer financial product innovations and regulatory issues; securitization and the design and pricing of financial products derived from consumer debt, such as mortgages, credit card receivables, and student loans. 

Develops a new perspective on the dynamics of financial markets and the roles that human behavior and the business environment play in determining the evolution of behavior and institutions. Draws on a variety of disciplines to develop a more complete understanding of human behavior in the specific context of markets and other economic institutions. Incorporates practical applications from financial markets, the hedge fund industry, private equity, government regulation, and political economy. 

Covers the role of finance in the biotech and pharmaceutical industries; specifically, the application of novel financing methods and business structures to facilitate drug discovery, clinical development, and greater patient access to high-cost therapies. Topics include basic financial analysis for the life-sciences professional; risks and returns in the biopharma industries; the mechanics of biotech startup financing; capital budgeting for biopharma companies; and applications of financial engineering in modern healthcare investment strategies and institutions. Develops a systemic framework for addressing the biggest challenges in the biomedical ecosystem.

Professor(s) who recently taught this course:
Andrew Lo

Covers methods of managing data and extracting insights from real-world financial sources. Topics include machine learning, natural language processing, predictive analytics, regression methods, and time series analysis. Applications include algorithmic trading, portfolio risk management, high-frequency market microstructure, and option pricing.  Studies major sources of financial data, raw data cleaning, data visualization, and data architecture. Provides hands-on instruction in tools used in the financial industry to process massive data sets, including SQL, relational and multidimensional databases. Emphasizes computer implementations throughout.

Professor(s) who recently taught this course:
Paul Mende

Explores the markets for cryptocurrencies, such as Bitcoin. Begins with the basics and economics of crypto assets' underlying blockchain technology and then turns to the trading and markets for cryptocurrencies, initial coin offerings, other tokens and crypto derivatives. Students gain an understanding and comparison to traditional finance of the market structure, participants, regulation and dynamics of this relatively new and volatile asset class.

While machine learning literature is extremely rich and at times overly theoretical, there are still consistently two techniques that win most machine learning competitions: neural networks and gradient boosting.  In this course we will try to provide a bridge from knowledge of learning to a foundational understanding of how those resources are applied to finance. The course provides a very practical approach to applying modern machine learning methods to problems in the financial domain. 

Professor(s) who recently taught this course: Michael Shulman