### MATH 5800: Investigation of Special Topics

**Description:** Students who have well defined mathematical problems worthy of investigation and advanced reading should submit to the department a semester work plan.

**Prerequisites:** Instructor consent required.

**Credits:** 1-6

### MATH 5800 - Section 5: Investigation of Special Topics. Topic: Financial Programming and Modeling

**Description:** This class will focus on the use of VBA in EXCEL for programming financial models but will also include brief introductions to the use of R, MATLAB, SAS and C++ in financial modeling. Programming basics such as matrix algebra, control structures, data structures, data types, function and sub procedures will be reviewed. Students pick up advanced programming materials such as Excel object programming, SQL, database operations using ODBC, file operations, graphical user interface design and programming, object-oriented programming, add-ins, XML, web queries, reports, and automation. Students learn modeling techniques such as Monte-Carlo simulation, binomial and trinomial trees, Black-Scholes, finite difference methods, constrained and unconstrained optimization, linear and non-linear programming, heuristic optimization, mean-variance, Value at Risk, data envelopment analysis, and data mining techniques applied in risk management, and apply these in financial contexts. More specifically, students construct various applications, for example portfolio optimization with live data from the internet using various methods, option pricing using Monte-Carlo, binomial trees, trinomial trees, Black-Scholes, and finite difference methods, capital budgeting, finding betas of stocks, option strategies, risk evaluation using data mining techniques, stock-trading simulation, planning, product mix, efficiency evaluation, etc., across several programming languages.

**Credits:** 3

### MATH 5800 - Section 31: Investigation of Special Topics. Topic: Financial Data Mining

**Description:** Data mining helps companies in digging valuable information from large amounts of data and making business decisions. This course covers data mining techniques for data mining tasks such as classification, regression, cluster analysis, association analysis, and anomaly detection. Specialized data mining software from SAS, Oracle, Microsoft as well as open source software are introduced. Students will develop code using MATLAB, R, and Python in their assignments and work on group projects with financial and insurance applications. An intermediate level of database administration, data modeling, SQL, PL/SQL, development tools is introduced as well.

**Credits:** 3

**Sections: **Fall 2014 on Storrs Campus

PSCourseID | Course | Sec | Comp | Time | Room | Instructor |
---|---|---|---|---|---|---|

04506 | 5800 | 005 | Lecture | W 5:00 PM-7:45 PM | ATWRA001 | Do, Cuong |

02922 | 5800 | 006 | Lecture | F 10:00 AM-12:00 PM | MSB110 | Cardetti, Fabiana |

04887 | 5800 | 018 | Lecture | W 5:45 PM-8:15 PM | MSB403 | Bridgeman, James |

05288 | 5800 | 031 | Lecture | Th 5:00 PM-7:45 PM | MSB407 | Do, Cuong |