Computational Finance Master of science degree

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Overview

Quantitative analysts are in high demand. Become an asset to any company as you learn computational strategies to generate profits and reduce company risks.


The computational finance program produces quantitative analysts who design and implement financial models used by banks and investment companies to generate profits and reduce risk. Computational analytics experts also support other industries and business functions that reach beyond banking and finance. The computational finance masters help you expand your skills into fields that are in great demand and feature high salaries. The program is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of industries.

The MS in computational finance is designed for students interested in computational or quantitative finance careers in banking, finance, and a growing number of additional industries. Professionals in these fields use their strengths in business, modeling, and data analysis to understand and use complex financial models.

The program addresses a vital and growing career field, reaching beyond banking and finance. Typical job titles include risk analyst, research associate, quantitative analyst, quantitative structured credit analyst, credit risk analyst, quantitative investment analyst, quantitative strategist, data analyst, senior data analyst, fixed income quantitative analyst, and financial engineer.

Computational finance is an excellent career option for technically-oriented professionals in the fields of business, math, engineering, economics, statistics, and computer science. Programming knowledge is highly preferred.

Plan of study

The curriculum offers integration of finance, mathematics, and computing. The required mathematics courses have substantial financial content and the experiential computational finance course, which students take during the summer, makes use of skills learned in mathematics, analytics, and finance courses are taken up to that point. The program is a full-time, 12- to 17-month curriculum beginning in the fall or spring. The program ends with a required non-credit comprehensive exam based on the courses completed by the student.

International Students: F-1 OPT STEM 17-Month Work Extension

International students receiving the MS in computational finance degree qualify to apply for a 17-month work extension to their OPT (Optional Practical Training) period. This extension means that students could be eligible for up to two and a half years of work in the United States.

The extension is exclusive to qualifying STEM (science, technology, engineering or math) focused programs. The MS in computational finance program qualifies for an F-1 OPT STEM Extension, under the CIP Code 27.0305-Financial Mathematics in the 2012 STEM-Designated Degree Program List published by the U.S. Immigration and Customs Enforcement (ICE) office. For more information please visit the U.S. Citizenship and Immigration Services (USCIS)webpages: Understanding F-1 OPT Requirements and Questions and Answers: Extension of Optional Practical Training Program for Qualified Students.

Industries


  • Investment/Portfolio Management

  • Commercial Banking and Credit

  • Financial Services

  • Investment Banking

  • Internet and Software

  • Health Care

Curriculum

Computational Finance, MS degree, typical course sequence

Course Sem. Cr. Hrs.
ACCT-603
Accounting for Decision Makers
A graduate-level introduction to the use of accounting information by decision makers. The focus of the course is on two subject areas: (1) financial reporting concepts/issues and the use of general-purpose financial statements by internal and external decision makers and (2) the development and use of special-purpose financial information intended to assist managers in planning and controlling an organization's activities. Generally accepted accounting principles and issues related to International Financial Reporting Standards are considered while studying the first subject area and ethical issues impacting accounting are considered throughout.
3
FINC-671
Survey of Finance
This course introduces students to the field of finance and prepares them to undertake a study of advanced topics in other courses. Students learn about financial markets, regulation, and the fundamentals of corporate finance in areas such as investment and financing decisions. A brief overview of financial reporting allowing students to understand firm performance is also provided.
3
FINC-772
Equity Analysis
Students learn about various equity markets, trading, and valuation. The focus of this course is on valuing equities using widely used methods and in forming and analyzing equity portfolios. Students also learn portfolio optimization methods.
3
FINC-773
Debt Analysis
Students learn about various debt markets, trading, and valuation. The focus of this course is on valuing debt instruments using widely used methods and in forming and analyzing debt portfolios.
3
FINC-774
Advanced Derivatives
Students learn about derivatives contracts, their pricing, and uses. The course will cover advanced financial engineering topics such as the engineering of fixed-income contracts, volatility positions, credit default swaps, and structured products.
3
FINC-791
Computational Finance Exam Preparatory
Computational finance students take a field exam at the end of their program. This course provides basic help to students taking this exam. (all required finance courses in the computational finance program)
0
FINC-795
Computational Finance Experience
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.
3
MATH-735
Mathematics of Finance I
This is the first course in a sequence that examines mathematical and statistical models in finance. By taking a mathematical viewpoint the course provides students with a comprehensive understanding of the assumptions and limitations of the quantitative models used in finance. Topics include probability rules and distributions, the binomial and Black-Scholes models of derivative pricing, interest and present value, and ARCH and GARCH time series techniques. The course is mathematical in nature and assumes a background in calculus (including Taylor series), linear algebra and basic probability. Other mathematical concepts and numerical methods are introduced as needed.
3
MATH-736
Mathematics of Finance II
This is the second course in a sequence that examines mathematical and statistical models in finance. By taking a mathematical viewpoint the course provides students with a comprehensive understanding of the assumptions and limitations of the quantitative models used in finance. Topics include delta hedging, introduction to Ito calculus, interest rate models and Monte Carlo simulations. The course is mathematical in nature and assumes a background in calculus (including Taylor series), linear algebra and basic probability. Other mathematical concepts and numerical methods are introduced as needed.
3
 
Analytics Electives
6
 
University Electives
6
Total Semester Credit Hours
36

Analytics electives*

ACCT-645
Accounting Information and Analytics
The objective for this course is helping students develop a data mindset which prepare them to interact with data scientists from an accountant perspective. This course enables students to develop analytics skills to conduct descriptive, diagnostic, predictive, and prescriptive analysis for accounting information. This course focuses on such topics as data modeling, relational databases, blockchain, visualization, unstructured data, web scraping, and data extraction.
BANA-780
Advanced Business Analytics
This course provides foundational, advanced knowledge in the realm of business analytics. Advanced topics such as machine learning, analysis of structured data, text mining, and network analysis are covered. Industry standard tools such as R and Python are extensively used in completing student projects.
FINC-780
Financial Analytics
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.
MGIS-650
Introduction to Data Analytics and Business Intelligence
This course serves as an introduction to data analysis including both descriptive and inferential statistical techniques. Contemporary data analytics and business intelligence tools will be explored through realistic problem assignments.
MGIS-725
Data Management and Analytics
This course discusses issues associated with data capture, organization, storage, extraction, and modeling for planned and ad hoc reporting. Enables student to model data by developing conceptual and semantic data models. Techniques taught for managing the design and development of large database systems including logical data models, concurrent processing, data distributions, database administration, data warehousing, data cleansing, and data mining.
MGIS-760
Integrated Business Systems
This course focuses on the concepts and technologies associated with Integrated Business Information Systems and the managerial decisions related to the implementation and ongoing application of these systems. Topics include business integration and common patterns of systems integration technology including enterprise resource planning (ERP), enterprise application integration (EAI) and data integration. The key managerial and organizational issues in selecting the appropriate technology and successful implementation are discussed. Hands-on experience with the SAP R/3 system is utilized to enable students to demonstrate concepts related to integrated business systems. (familiarity with MS Office suite and Internet browsers)
MKTG-768
Marketing Analytics
This course provides an overview of marketing analytics in the context of marketing research, product portfolios, social media monitoring, sentiment analysis, customer retention, clustering techniques, and customer lifetime value calculation. Students will be introduced to, mathematical and statistical models used in these applications and their implementation using statistical tools and programming languages such as SAS, SPSS, Python and R. Multiple data sources will be used ranging from structured data from company databases, scanner data, social media data, text data in the form of customer reviews, and research databases. Students will complete guided projects using real time data and make effective use of visualization to add impact to their reports. There are no listed 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.
STAT-611
Statistical Software
This course is an introduction to two statistical-software packages, SAS and R, which are often used in professional practice. Some comparisons with other statistical-software packages will also be made. Topics include: data structures; reading and writing data; data manipulation, subsetting, reshaping, sorting, and merging; conditional execution and looping; built-in functions; creation of new functions or macros; graphics; matrices and arrays; simulations; select statistical applications.
STAT-747
Principles of Statistical Data Mining
This course covers topics such as clustering, classification and regression trees, multiple linear regression under various conditions, logistic regression, PCA and kernel PCA, model-based clustering via mixture of gaussians, spectral clustering, text mining, neural networks, support vector machines, multidimensional scaling, variable selection, model selection, k-means clustering, k-nearest neighbors classifiers, statistical tools for modern machine learning and data mining, naïve Bayes classifiers, variance reduction methods (bagging) and ensemble methods for predictive optimality.
STAT-773
Time Series Analysis and Forecasting
This course is designed to provide the student with a solid practical hands-on introduction to the fundamentals of time series analysis and forecasting. Topics include stationarity, filtering, differencing, time series decomposition, time series regression, exponential smoothing, and Box-Jenkins techniques. Within each of these we will discuss seasonal and nonseasonal models.
STAT-784
Categorical Data Analysis
The course develops statistical methods for modeling and analysis of data for which the response variable is categorical. Topics include: contingency tables, matched pair analysis, Fisher's exact test, logistic regression, analysis of odds ratios, log linear models, multi-categorical logit models, ordinal and paired response analysis.

* Additional electives are available with approval.

University electives

ACCT-7##
Any 700-level ACCT course
BANA-6##
Any 600-level BANA course
BANA-7##
Any 700-level BANA course
CSCI-654
Foundations of Parallel Computing
This course is a study of the hardware and software issues in parallel computing. Topics include an introduction to the basic concepts, parallel architectures and network topologies, parallel algorithms, parallel metrics, parallel languages, granularity, applications, parallel programming design and debugging. Students will become familiar with various types of parallel architectures and programming environments.
CSCI-721
Data Cleaning and Preparation
This course provides an introduction to the concepts and techniques used in preparing data for subsequent data mining. Topics include the knowledge discovery process; data exploration and its role; data extraction, cleaning, integration and transformation; handling numeric, unstructured, text, web, and other forms of data; and ethical issues underlying data preparation and mining. Data cleaning projects, a term paper, and presentations are required.
DECS-7##
Any 700-level DECS course
MATH-601
Methods of Applied Mathematics
This course is an introduction to classical techniques used in applied mathematics. Models arising in physics and engineering are introduced. Topics include dimensional analysis, scaling techniques, regular and singular perturbation theory, and calculus of variations.
MATH-605
Stochastic Processes
This course is an introduction to stochastic processes and their various applications. It covers the development of basic properties and applications of Poisson processes and Markov chains in discrete and continuous time. Extensive use is made of conditional probability and conditional expectation. Further topics such as renewal processes, reliability and Brownian motion may be discussed as time allows.
MATH-711
Advanced Methods in Scientific Computing
MATH-712
Numerical Methods for Partial Differential Equations
This is an advanced course in numerical methods that introduces students to computational techniques for solving partial differential equations, especially those arising in applications. Topics include: finite difference methods for hyperbolic, parabolic, and elliptic partial differential equations, consistency, stability and convergence of finite difference schemes.
MATH-741
Partial Differential Equations I
This course uses methods of applied mathematics in the solution of problems in physics and engineering. Models such as heat flow and vibrating strings will be formulated from physical principles. Characteristics methods, maximum principles, Green's functions, D'Alembert formulas, weak solutions and distributions will be studied.
MATH-742
Partial Differential Equations II
This is a continuation of Partial Differential Equations I and deals with advanced methods for solving partial differential equations arising in physics and engineering problems. Topics to be covered include second order equations, Cauchy-Kovalevskaya theorem, the method of descent, spherical means, Duhamels principle, and Greens function in higher dimensions.
MGIS-6##
Any 600-level MGIS course
MGIS-7##
Any 700-level MGIS course
STAT-756
Multivariate Analysis
Multivariate data are characterized by multiple responses. This course concentrates on the mathematical and statistical theory that underlies the analysis of multivariate data. Some important applied methods are covered. Topics include matrix algebra, the multivariate normal model, multivariate t-tests, repeated measures, MANOVA principal components, factor analysis, clustering, and discriminant analysis.

Admission Requirements

To be considered for admission to the MS program in computational finance, candidates must fulfill the following requirements:

  • Complete a graduate application
  • Hold a baccalaureate degree (or equivalent) from an accredited university or college.
  • Submit official transcripts (in English) of all previously completed undergraduate and graduate course work.
  • Submit a personal statement of educational objectives. Statement should indicate any mathematical and programming knowledge held by the candidate as well as their professional interests, and why these make the candidate suitable for the program.
  • Submit a current resume or curriculum vitae.
  • International applicants whose native language is not English must submit scores from the TOEFL, IELTS, or PTE. A minimum TOEFL score of 92 (internet-based) is required. A minimum IELTS score of 7.0 is required. The English language test score requirement is waived for native speakers of English or for those submitting transcripts from degrees earned at American institutions.

For further information about tips on personal statements and additional guidance on how to submit a successful application, please visit Saunders College of Business Admissions Requirements.

 

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