The principal focus of this course is students completing several projects provided by members of CPA firms and industry employers. Employers provide assignments, which may include data or require students to gather relevant data, and students use defined technology, which may include a variety of applications common in technological accounting practice, to complete projects in teams. Students also write comprehensive individual reports and analyses related to the projects. Peripheral work in the course includes examination of theoretical concepts, definitions, and models espoused in the accounting literature and relevant to analyzing various contemporary issues in financial accounting and reporting. The historical development of accounting standards and contemporary issues in financial reporting are integrated. The course requires writing and student presentations. Subject to approval by the Program Director, an individual student internship/coop followed by an in-depth report may obtain equivalent credit.

This course introduces students to data management and analytics in a business setting. Students learn how to formulate hypotheses, collect and manage relevant data, and use standard tools such as Python and R in their analyses. The course exposes students to structured data as well as semi-structured and unstructured data. There are no pre or co-requisites; however, instructor permission is required for students not belonging to the MS-Business Analytics or other quantitative programs such as the MS-Computational Finance which have program-level pre-requisites in the areas of calculus, linear algebra, and programming.

Examines financial decisions people must make in their personal lives. Covers personal taxation, housing and mortgages, consumer credit, insurance (including life, health, property and casualty), and retirement and estate planning. Also reviews the common financial investments made by individuals, including stocks, bonds, money market instruments and mutual funds. This class involves extensive use of the internet for access to information. (Students in the Finance Program may use this course only as a free elective, not as a course creditable towards the Finance Program.)

Financial analytics is the use of business analytics methods and tools on financial data to solve problems such as investment and risk analysis, portfolio optimization, valuation, default modeling, and so on. This course introduces a contemporary tool (R or Python) and its use in solving these problems. In this hands-on course, students also learn about the field of fintech.

This course provides a survey of financial analytics applications in contexts such as investment analysis, portfolio construction, risk management, and security valuation. Students are introduced to financial models used in these applications and their implementation using popular languages such as R, Matlab, and Python, and packages such as Quantlib. A variety of data sources are used: financial websites such as www.finance.yahoo.com, government sites such as www.sec.gov, finance research databases such as WRDS, and especially Bloomberg terminals. Students will complete projects using real-world data and make effective use of visualization methods in reporting results. There are no pre or co-requisites; however, instructor permission is required – student aptitude for quantitative work will be assessed; waived for students enrolled in quantitative programs such as the MS-Computational Finance which have pre-requisites in the areas of calculus, linear algebra, and programming.

Students apply their mathematical, data analytic, and integrative finance skills in a complex project involving real or simulated data. Under the supervision of an advisor, students work in teams to perform a stipulated task/project and write a comprehensive report at the end of the experience. Subject to approval by the program director, an individual student internship/co-op followed by an in-depth report may obtain equivalent credit.

This Ph.D. research seminar focuses on the two roles of technology in accounting and finance research in particular, and business research generally. First, the world of technology which includes information technology and analytics, has influenced research methods with techniques such as sentiment analysis and machine learning. Second, technology has transformed the practice of accounting and finance, through innovations such as the blockchain and has led to distinct areas of research such as fintech. This seminar will cover both aspects and has the objective of (a) allowing access to cutting edge research techniques and (b) developing research questions in tech related areas.

The course introduces financial concepts of risk, return and valuation. The main application studied in this course, Capital Budgeting, arises in the corporate setting where managers allocate scarce resources to projects. Basic issues of capital budgeting covered include cash flow estimation and valuation techniques. Advanced issues include sensitivity analysis and the consideration of real options.