Financial Analytics

To qualify for the track, students must demonstrate basic proficiency in accounting and finance that can be satisfied in one of two ways:

1. Successful completion of a 3-credit hour semester course in Corporate Finance or Business Finance (or it equivalent) at the undergraduate or graduate level.
2. Successful completion of the CFA Institute’s Investment Foundations Certificate Program (which requires an estimated 100 hours of self-study time).

In addition, students are expected to have a strong foundation in elementary probability and statistics, linear regression methods, and some proficiency in programming (ideally, Python and R).

In addition to the regular MSBA summer courses, all Financial Analytics students begin by also taking a 1 credit hour course entitled, Introduction to Financial Analytics. This course is an intensive introduction that is intended to build students’ institutional vocabulary, statistical and mathematical sophistication, and familiarity with financial procedures and methods.

In the Fall semester, students enroll in a 6-credit hour course that covers 5 separate disciplines drawn from both investment theory and corporate finance:

• Valuation Theory
• Advanced Corporate Finance
• Applied Valuation
• Asset Management
• Empirical Finance

Each domain is taught by specially handpicked faculty from the Department of Finance who specialize in each particular field.

In the Spring semester, students enroll in a 3-credit hour course in Financial Modeling & Testing that integrates and builds upon what they have learned across their courses in finance, data science, and R/Python programming. Once again, the course is team-taught by subject specialists, this time drawn from both the Finance Dept. and the IROM Dept.

Students develop their skills in gathering data from a variety of sources: including both traditional structured types (e.g., corporate filings and financial statements, stock price and return data, economic indicators) and alternative unstructured types (e.g., corporate investor conference calls, internet message boards and sentiment data, imagery data). Students are exposed to a variety of databases (e.g., Compustat, CRSP, SEC, Bloomberg, and Factset) and state-of-the-art techniques and procedures for synthesizing and analyzing the data—ultimately building financial models to produce actionable insights.

Throughout the semester, student-teams present their ongoing research, work, and findings to the rest of the class and the faculty.

This course—as part of a two-course sequence with Investment Theory—focuses on issues concerning raising capital (designing corporate capital structure, issuing equity, debt, convertibles, etc.) and investing it (project evaluation, budgeting, and acquisitions). The course provides an opportunity to acquire or improve useful financial skills such as computing the cost of capital, pricing options, assessing and managing financial risk, and valuing acquisitions.

This course—as part of a two-course sequence with Advanced Corporate Finance—is designed to provide students with a solid grounding in major topics in investment management, including modern portfolio theory, asset allocation, and the valuation and use of equities, fixed income, derivatives, and mutual funds. Students will learn the relationship between risk and return, how decisions are made with respect to putting together a portfolio and how investors select and use different types of securities. During the course, students will get hand-on experience in designing, building, and evaluating their own investment and valuation models. The course consists of three modules: Asset Management, Empirical Finance, and Applied Valuation.

This course deals with optimization methods that help in decision-making. It will cover a broad range of relevant quantitative techniques for decision-making under both certainty and uncertainty. Each technique will be motivated using important applications and discussed along with some relevant theory. The focus however will be on formulating and solving problems. Specific topics/techniques will include linear, quadratic, nonlinear, integer programming, advanced simulation methods and dynamic programming.

This advanced course integrates and builds upon what students have learned across their other MSBA coursework in finance, data science, and R/Python programming. The course is team-taught by subject specialists with expertise in empirical finance, applied valuation, and data science.

Students develop their skills in gathering data from a variety of sources: including both traditional structured types (e.g., corporate filings and financial statements, stock price and return data, economic indicators) and alternative unstructured types (e.g., corporate investor conference calls, internet message boards and sentiment data, imagery data). Students are exposed to a variety of databases (e.g., Compustat, CRSP, SEC, Bloomberg, and Factset) and state-of-the-art techniques and procedures for synthesizing and analyzing the data—ultimately building financial models to produce actionable insights.

Throughout the semester, student-teams present their ongoing research, work, and findings to the rest of the class and the faculty.

The course provides an overview of the most recent technological advances that are radically changing the financial services industry. Technological breakthroughs offer new ways for people to save, invest, borrow, and transact. We will analyze how new technologies create value in the financial industry, from reducing unit cost, increasing transparency, increasing competition, creating network effects, leveraging economies of scales, and lowering asymmetric information. We will also study the competitive landscape and the market opportunities and threats for incumbents and new entrants.

Among the benefits of membership in the Financial Analytics track is the ability to take additional approved graduate finance electives from the Master of Science in Finance (MSF) program on a space-available basis. In addition, students in the Financial Analytics track are given priority for finance-related Capstone projects.

Throughout the year, FA Students are eligible for special training sessions on tools that are valued in the industry:

• Bloomberg
• Factset
• SEC Edgar
• WRDS (Compustat/WRDS/CRSP)