MFin Curriculum

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

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 regulation.

Required Action Learning Experience: Our signature finance Action Learning courses allow students to apply theory to practice, testing their skills in research methodologies and data analysis 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.

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.

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

All MFins will enroll in a pass/fail course with supplemental recitations during the summer term which is designed to develop skills in applying basic methods from the programming language Python (with additional references from R) to financial problems. Topics include data manipulation, visualization and reporting, and an overview of programming ethics. MFin students will apply and build upon these skills in 15.433 Financial Markets and 15.450/15.457 Analytics and Advanced Analytics of Finance.

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. 

This course is mandatory for all MFin students and will take place during H1 of your first fall term.

Explores a range of ethical issues and challenges that arise in organizations and financial practice. Provides fundamental theories typically used to evaluate ethical dilemmas and references both real situations and hypothetical examples. Highlights the importance of ethical values and their impact on financial regulation for professional practice. Discusses the various factors that influence ethical behavior, such as family, religious values, personal standards and needs, senior leadership behavior, norms among colleagues, organizational expressed and implicit standards, and broader community values. Restricted to students in the Master of Finance Program.

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.

This course focuses on the financial theories and empirical evidence 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.

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 extracting 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.

Explores blockchain technology's potential use - by entrepreneurs and incumbents - to change the world of money and finance. Begins with a review of the technology's initial application, the cryptocurrency Bitcoin, giving students an understanding of the commercial, technical, and public policy fundamentals of blockchain technology, distributed ledgers and smart contracts in both open-sourced and private applications. Focuses on current and potential blockchain applications in the financial sector. Includes reviews of potential use cases for payment systems, central banking, venture capital, secondary market trading, trade finance, commercial banking, post-trade possessing, and digital ID. Also explores the markets and regulatory landscape for cryptocurrencies, initial coin offerings, other tokens, and crypto derivatives. Open to undergraduates with permission of instructor.

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. 

Exposes students to the cutting edge of financial engineering.  Includes a deep immersion into 'how things work,' where students develop and test sophisticated computational models and solve highly complex financial problems.  Covers stochastic modeling, dynamic optimization, stochastic calculus, and Monte Carlo simulation through topics such as dynamic asset pricing and investment management, market equilibrium and portfolio choice with frictions and constraints, and risk management.  Assumes a solid undergraduate-level background in calculus, probability, statistics, and programming and includes a substantial coding component.  Students are encouraged but not required to use R for coursework.

Provides a strategic framework for current and future finance leaders - with domestic or multinational startups, established companies, investment banks or asset management firms - for investing and operating in international capital markets.  Includes currency markets; measuring and managing exchange rate exposure; exchange rate determination and forecasting; international financial instruments and institutions; international trading strategies; and the dynamics of global financial crises.  Incorporates real-world events into interactive discussions.

Professor(s) who recently taught this course: Jonathan Parker

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. 

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.

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.

Explores consumer finance and the ways in which financial innovation and new technologies disrupt the financial services industry, leading to material change in business models and product design in financial markets. Provides a solid understanding of rational and behavioral aspects of consumer decision-making and how the players, products, funding markets, regulatory frameworks, and fundamentals all interact to shape ever-changing consumer financial markets, including consumer debt, investment, transactions, and advising markets. Covers past and current innovations and technologies ranging from peer-to-peer lending, AI, deep learning, cryptocurrencies, blockchain technology, and open API's, to the role of FinTech startups. A combination of case studies, guest speakers and group discussion provide real-world insight and interactivity, while special review sessions help hone technical skills.

Professor(s) who recently taught this course: Jonathan Parker

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. 

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.

Focuses on implementing successful investment strategies - blending academic finance with the practice of investment management employed by the world's most sophisticated (quantitative) investors. Covers the dynamics of behavioral finance and their effects on markets; investment strategies in current use, and how to build and test your own quantitative strategies; portfolio construction and trading, considering transaction costs, risk management, and efficient trade execution; and current trends and regulatory changes. Includes guest lecturers. Requires an understanding of basic statistical and financial concepts.

Professor(s) who recently taught this course: Matthew Rothman

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. 

Reviews the merits and trade-offs of public versus private capital markets, which have witnessed tremendous growth over the last decade, from a corporate governance standpoint. Specific phenomena affecting public companies, such as shareholder activism and passive investing, are also considered. Uses corporate case studies for extensive analysis and discussion.

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.

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. 

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.

Deep dive into social impact investing -- an approach intentionally seeking to create a financial return and positive social impact that is actively measured. Imparts a solid analytical framework for evaluating the spectrum of social impact investments, including mission-related investing. Includes a project that provides practical experience in evaluating an impact enterprise or a public markets ESG strategy. Students gain experience in structuring different types of investments, and critically compare and contrast these investments with traditional mainstream investments, with a view to understanding structural constraints. Designed for students interested in the intersection of finance and social impact. Provides career guidance and networking opportunities.