Academic Courses

Financial Reports and the Stories They Tell: This bootcamp provides a refresher on all four financial statements (in the context of macro-economic factors—rising inflation, rising interest rates, foreign currency headwinds):

• the statement of operations (P&L)
• balance sheet
• statement of cash flows
• statement of shareholders’ equity

The session reviews how to read a financial statement and to derive stories of the performance of the company including trends in top-line and bottom line growth, earnings per share, dividends, key ratios reflecting financial position of the company. All keys to fundamental analysis. The course also focuses on the impact of special events such as stock splits and supply chain issues on financial statements.

In this course, students will use excel, R, R Studio, and other technology to identify the statistical elements of data. Students will learn how to load data and perform summary statistics and general statistical analysis on the data. Student will learn the coding for mean, median, modes, and variance and will learn how to create frequency distributions, density plots, correlation coefficients, and other commonly used statistical measures. 

In this course, students will review essential concepts of the Python (v.3) language, focusing on data science and financial analytics. Topics will include variables; classes & data structures; functions & methods; control flow; statistics; and simple interaction with SQL databases. Students will practice operations such as regressions, statistical analysis, and interactions with financial data systems (e.g., WRDS). Some topics may be condensed in winter offerings in lieu of adding basic visualization; decision algorithms (clustering, k-means, decision trees, etc.); and other topics

The course begins with an introduction to the financial markets for equity securities. This includes both the primary and secondary markets, and the trading and regulation of equities. Next, the focus shifts to the markets for derivative financial assets based upon equity securities. This includes both the exchange-traded markets and the over-the-counter markets. The primary focus is on option and futures contracts, and their use in both hedging and speculative trading strategies.

This course is intended to give students an understanding of the corporate finance analytic work conducted by major investment banks and boutique advisory firms. Areas reviewed will include business forecasting techniques, valuation analysis, cost of capital estimation, financial ratio benchmarking, and debt capacity & credit analysis. At the conclusion of the course, students will have developed stronger corporate finance analytic skills, insight regarding the drivers of company value, and improved judgement on some of the special technical challenges that often confront bankers and financial advisors.

This course takes place in the Finance Lab, where students experience bridge finance practice and theory through a simulated rotation into the analyst program of a major bank. The action-based workstream explores how our global financial system arises from the need to exchange assets and manage risks. Students analyze real-world news and global events, including those affecting global supply chains, foreign exchange and interest rate markets, and monetary and trading policies, and report distilled findings based on fundamental economic principles. Deliverables include running weekly bank-style "market update calls", creating research reports and dashboards, and pitching ideas.

This course introduces coding for financial analysis using Python. Python is the most popular programming language (globally) due to its simplicity, versatility, and community support and is widely used in computational finance. This course will give students, with little or no prior programming experience, working knowledge of programming in Python and in using the Python data analysis package Pandas to compute analytical solutions for financial insights. The course will also address using Python to obtain data both from databases and the web more generally. The goal is not only to learn about programming, but also to enhance students’ analytical thinking and ability to frame and solve problems. To complement and apply the learning we will use business examples drawn from applications in finance.

In Data Visualization for Finance, we explore the techniques and tools used to create effective visualizations that clearly and efficiently communicate relationships within financial data. The field of data visualization combines the art of graphic design with the science of data analytics. Students perform exploratory analysis through visualization, create professional and engaging visualizations for use in financial decision processes, and design interactive visualizations and dashboards. The course considers the common quantitative messages users attempt to understand or communicate from a set of data and the associated visualizations used to help communicate each message. These include time series, rankings, proportions, deviations, frequencies and distributions, correlations, categorical comparisons, and geospatial plots. Students analyze real data sets and utilize R, Power BI, and other tools to design and prototype their visualizations.

This course provides statistical tools for modeling and forecasting financial data. Topics include:

• Evidence on the predictability of stock and bond indexes
• Forecasting equity returns with dividend yields and other standard variables
• Factor models for the cross-section of equity returns and factor selection techniques
• Simulating and estimating affine models of the term structure
• Time series analysis with applications to volatility modeling

The course presents the primary approach used in the pricing of options and futures contracts. This utilizes the concept of the relative-value arbitrage argument that forms the basis for trading strategies and risk management. A major theme is the calculation of the dynamic characteristics of these derivative assets. Topics include comparisons between statistical and market-based parameter estimators, and between closed-form solutions and numerical methods for valuation. 

This course focuses upon the valuation and uses of fixed income securities. Beginning with the fundamentals of pricing, the course moves through the modeling of the term structure of interest rates and the measurement of interest rate risk. There is broad coverage of the different sectors of the bond markets, and of the role of bond ratings. The course concludes with an analysis of structured mortgage products and fixed income derivative financial assets.

This course is intended to provide students with an overview of merger and acquisition (“M&A”) activity. We will review the broad set of considerations that are addressed in M&A transactions. Emphasis will be placed on the technical aspects of M&A (valuation and transaction analysis). We will also briefly address certain qualitative transaction issues. Both strategic M&A transactions and Leveraged Buyout Outs (LBOs) will be reviewed. At the conclusion of the course, students should have an improved understanding of the M&A process, terminology, and mechanics. Specifically, students should understand key M&A transaction issues relating to: transaction consideration, takeover premium, financing arrangements, and value creation.

This course utilizes the Finance Lab with an action-based workstream exploring the 24/7 pace of global trading products and how they work. Levering current market conditions, students source and pitch trading ideas in accordance with best practices of ethics and values. Students will start to build an actively managed investment vehicle in compliance with current financial regulations.

This course explores the global currency markets and how companies use analytical strategies to manage their FX exposure across different market conditions. Students will also learn about the commodity futures markets and explore the energy markets, including short-term trading strategies, long-term investments and financing, and emerging products such as carbon emissions credits.

This course is an elective option.

This course introduces key concepts and computations that relate to investment portfolios. Topics include the efficient frontier, asset allocation, CAPM and multi-factor models, style analysis, types of positions, performance analysis, risk, and the movement of stock prices. Computations will be based on real world data and a variety of standard software tools.

This course will cover quantitative models for stock selection. Students will use data from financial statements, stock prices, and trading volume to develop quantitative models for profitable stock investments. Students will get hands-on experience in building models that best suit their investment horizons.

This is an analytical course on financial technology (FinTech) for MAF students. The course exposes students to the methodologies, use cases, and hands-on experiences with FinTech in financial intermediation (e.g., banking, credit, payments). The topics include big data, machine learning, automation, digital payments, peer-to-peer (P2P) lending, and equity crowdfunding. In addition to learning about the foundations of these technologies, students will write scripts for collecting and processing big data, learn how to build classification trees, and use machine-learning techniques for predictive modeling of FinTech loan defaults. The course is delivered through interactive lecturettes, in-class activities, and group projects. The course is most relevant for consulting, investment banking, private equity, entrepreneurship, and corporate finance roles.

This course is an elective option.

In the Finance Lab, teams of students will work with professional coaches to research, develop and pitch an original investment strategy to institutional investors. Drawing on insights from across other classes and using cutting-edge innovation and professional trading platforms, students define and present their proprietary investment strategy to a panel of institutional investors. Students have the flexibility to specialize in strategies including ESG, emerging markets or digital currencies.

This practicum courses bridge theory and practice in the Finance Lab. For Term 3, students will learn about contemporary practices for modeling relevant financial metrics. Methodologies to be discussed will include statistical approaches, simulation models, large data-driven techniques, and other analyses as appropriate.

FIN 673B is an alternate course students can take in place of FIN 673 Practicum 3: Asset Management Team.

This course examines the markets associated with equity and other financial investments. Topics include basic and advanced order types, stock and option trading styles, market timing, trading costs, brokers, volatility, noteworthy financial market events such as the flash crash, and the investment management industry. The topics are applied rather than theoretical, and many will be illustrated with online tools and via real world data analyzed on Excel or via other standard software packages.

This course explores the concepts and tools in which traders and investors measure and manage the challenges of trading in the global markets, including market, counterparty credit and operational risks. Students will experiment with methods to quantify and hedge portfolio risks including calculating Value-at-Risk, analyzing a portfolio’s sensitivity to key risk factors, scenario analysis and historical stress testing. Students will learn how CROs use credit risk metrics such as current and potential future exposure to limit exposure to trading counterparties, and how these risks can be mitigated by taking initial and variance margin as negotiated in credit support annex of the ISDA.

This course is an elective option.

This course is designed for MAF students to better understand

1. how the venture capital & private equity industries work
2. how to structure the acquisition of a business
3. how to leverage key value drivers in a business

There is an emphasis on the technical aspects of venture capital and private equity transactions. This course provides an actionable framework to acquire a business, including raising capital if you have little or none, identifying a business to buy, and structuring a transaction. Course elements includes case studies and guest lectures by industry professionals.

This is an analytical course on financial technology (FinTech) for MAF students. The course exposes students to the methodologies, use cases, and hands-on experiences with FinTech in financial intermediation (e.g., banking, credit, payments). The topics include private and public blockchains, cryptocurrencies, tokens, and ICOs. In addition to learning about the foundations of these technologies, students will learn the basics of blockchain coding and develop ICO valuation models. The course is delivered through interactive lecturettes, in-class activities, and group projects. The course is most relevant for consulting, investment banking, private equity, entrepreneurship, and corporate finance roles.

This course is an elective option.

This practicum course bridges theory and practice in the Finance Lab. Under the mentorship of corporate coaches, student teams will work as analysts to combine capital with conscience and develop an investment opportunity that makes the world a better place. Using the capital markets to address a pressing social, economic or environment issue (such as water, energy, food or education) students draw on their analytical skills, financial acumen and professional expertise. Teams may develop their ideas using a combination of investment styles and tools and will showcase their investment pitches to a panel of corporate leaders.

Under the mentorship of corporate coaches, student teams will be assigned projects to analyze and model, in order to develop conclusions that will drive industry decisions and profits. Students would be able to take on data collection and sanitization, statistical model approximation, simulation development, and construction of ML/AI-based models to estimate various relevant quantifications of risk (e.g., interest rate risk, liquidity risk, default risk, optimal price points, and similar values of strategic interest). Students will document their models, analyses, and conclusions, and present them to industry advisors at the end of the term; presentations will illustrate profitability and other related measures of success. Volunteer corporate coaches would be sought from available financial institutions, credit agencies, insurance providers, and other enterprises that support these institutions.

FIN 674B is an alternate course students can take in place of FIN 674 Practicum 4: Global Leadership Team.