Graduate Mathematics Courses

Courses

Pre- or corequisite(s): ST 625

Examines methods for analyzing financial time series. In many times series, observations from different times periods are correlated, which implies a treatment that is different from usual regression analysis methods. The course reviews regression, smoothing and decomposition time-series models, introduces Box-Jenkins analysis and its extensions, and other modeling techniques commonly used in finance, such as quantile estimation and value at risk, duration models and the analysis of panel data.

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Prerequisite(s): ST 625 or ST 635 or Instructor Permission

This course will introduce participants to the most recent data mining techniques, with an emphasis on: getting a general understanding of how the method works, understanding how to perform the analysis using suitable available software, understanding how to interpret the results in a business research context, and developing the capacity to critically read published research articles which make use of the technique. Contents may vary according to the interest of participants.

Topics may include decision trees, an introduction to neural nets and to self organizing (Kohonen) maps, multiple adaptive regression splines (MARS), an introduction to genetic algorithms, to association (also known as market basket) analysis, to web mining and text mining, and to social networks.

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Prerequisite(s): BF 501 or PPF 501 or GR 521

Provides students with a set of mathematical and statistical tools necessary for advanced work in financial analysis. Topics include the use of differential calculus, Lagrange multipliers, and linear programming. Statistical methods will include parametric and non-parametric hypothesis tests, multiple regression analysis including discussion of model building techniques, autocorrelation, and heteroskedasticity. Examples of practical applications in finance will be used, and there will be extensive use of the Trading Room and computer applications.

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Prerequisite(s): ST 625 or Instructor Permission

Focuses on modeling situations dependent on multiple variables, as commonly found in many business applications. The issues addressed include: How do we predict who is more likely to respond to a direct mail offer? How can we identify important segments in our customer base? How do we summarize large sets of variables? Is there a significant association between brand preference and gender? A central objective of the course is for participants to actually perform their own statistical analyses and be able to position themselves as strong quantitative persons in their work environment. The topics covered are logistic regression, clustering, factor analysis, analysis of categorical variables, canonical correlation analysis, multivariate analysis of variance, discriminant analysis, conjoint analysis, and modeling with neural nets. Uses the statistical packages SAS and SPSS, along with more specialized software.

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Prerequisite(s): ST 625 and at least one other Quantitative Methods for Business Decisions or Business Analytics concentration course

Provides an opportunity for students to apply quantitative and data analysis skills in a live employment environment, serving as a quantitative analyst. With help from the internship coordinator, students identify a suitable internship and meet regularly with the internship coordinator. Students prepare a paper that discusses the internship experience and demonstrates at least one specific case analyzed during the internship period.

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