MFin Curriculum

Required Core Courses
Students build a rigorous foundation through courses in modern finance, financial mathematics, accounting, programming, ethics, corporate finance, financial markets, analytics, and communication.

Required Action Learning Experience
All MFin students complete at least one finance Action Learning course. Working in teams with industry practitioners, students apply finance theory, research methods, and data analysis to real-world challenges and present their findings to corporate decision-makers.

Electives
Students choose from topics including financial technology, quantitative methods, economics, healthcare finance, fixed income, mergers and acquisitions, and asset management. MFin students may also take courses elsewhere at MIT or cross-register at Harvard to broaden their academic experience.

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.

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.

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 one of these courses.

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 Financial Markets and 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 H3 of the spring 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.

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.

Advanced Analytics of Finance
This course is the advanced version. 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 must take one of these courses.

Designed to help students develop and refine the communication skills necessary for success as finance professionals. The course emphasizes presentation, teamwork, and writing skills that are essential to the finance profession. Restricted to MIT Sloan Master of Finance students.

Note: 12-month students will take Communicating with Data in the spring term, while 18-month students will take Strategic Communication for Finance Professionals.

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 the finance faculty, provides a problem and materials to a team of students. Each team then prepares a report and presents its 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.

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. 

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 a 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. 

Explores consumer finance and how financial innovation and new technologies disrupt the financial services industry, leading to material changes 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 provides 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. 

'AI & Money' examines the evolving impact of artificial intelligence on finance, money, and risk. The goal is to help students gain critical reasoning skills on how to seize commercial opportunities - and maintain relevance - at the intersection of these two dynamically changing fields. One - AI - a set of rapidly changing tools. The other - finance - is a dynamic network for the pricing and allocation of money and risk.

Students will explore how machine learning, generative AI, and advanced analytics are redefining asset management, trading, underwriting, finance functions, customer interactions, and compliance. The course will also touch on real-world commercial implications related to AI supply chain decisions, AI tech stacks, cyber risk, data centers, and regulatory frameworks.

We also explore how these developments in AI and finance could potentially affect economics, markets, and monetary policy around the world.

Opportunity for group study by graduate students on current topics related to management not otherwise included in the curriculum. This course is intended for 2nd-year MFin students. 

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. The 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 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.

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.

The course introduces financial models that balance risk and reward, paired with machine learning tools that can uncover and analyze financial patterns that traditional approaches might miss. Applications span the full spectrum of modern finance: valuation, credit analysis, proprietary trading and hedge-fund strategies, portfolio management, market structure, risk management and stress testing, natural language processing, and personal finance.