University of Texas at Austin
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Financial Analytics

McCombs School of Business McCombs School of Business


UT Austin’s MSBA Financial Analytics Track is designed to provide students with a rigorous program of study that enables them to: (i) develop an understanding of the theoretical and analytical foundations upon which the practice of finance is based; (ii) acquire and demonstrate the key competencies and skills in business analytics, finance and investments sought by industry; and (iii) cultivate a solutions-oriented mindset that recognizes the growing integration of finance, data science, and technology. This track positions graduates to be well qualified for both financial analysis positions and data analytic positions.

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Career Outcomes

The track responds to the need for highly trained professionals in the field. Prospective students interested in learning more about careers in financial analytics can reach out to our Career Management team. Expected financial analytics job titles include:

  • Financial Analyst
  • Quantitative Analyst
  • Business Analyst
  • Portfolio Analyst
  • Investment Consultant

Target employers include: corporate finance and financial management departments, financial institutions, consulting firms, and asset managers.

Objective of the Financial Analytics Track

The MSBA Financial Analytics Track has a distinctive focus that combines education in empirical methods in finance and advanced data science. In so doing, our graduates are capable of acquiring and analyzing multiple data sources and data types to produce actionable insights for applications ranging from corporate finance to asset management.

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Texas MS Business Analytics Admissions

Drop in Hours: Tuesdays 3pm-4:30pm, Thursdays 12pm-1pm

The Financial Analytics Curriculum

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.