Financial Engineering Masters/MSc

Program Overview

The University of Birmingham's Financial Engineering Masters/MSc program prepares graduates to become experts in mathematical finance through advanced computational and programming techniques. Students develop skills in integrating knowledge from mathematics, statistics, and computer science to solve complex financial problems. The program features a research project, equipping graduates with independent study and research capabilities crucial for quantitative analysis careers in economic and financial sectors.

Program Outline

Degree Overview:

The Financial Engineering Masters/MSc program at the University of Birmingham is designed to equip graduates with advanced computational and programming techniques in mathematical finance. The program aims to develop students' skills in:

• Integrating knowledge of mathematics, statistics, and computer science to analyze and solve financial problems.
• Using software packages for statistical analysis and empirical studies.
• Finding optimal solutions to financial problems in complex environments.
• Independent study and research, an essential skill for quantitative analysts.
The program is ideal for graduates with strong backgrounds in mathematics or programs with advanced mathematical components who aspire to careers in quantitative analysis within economic or financial sectors.

Outline:

The Financial Engineering Masters/MSc program consists of 180 credits, divided into two-thirds taught modules and one-third research project.

Core Modules:

• Mathematical Finance (20 credits): This module covers fundamental concepts in mathematical finance, including asset pricing, option pricing, and risk management.
• Advanced Mathematical Finance (20 credits): This module delves deeper into advanced topics in mathematical finance, such as stochastic calculus, derivative pricing models, and portfolio optimization.
• Numerical Methods and Numerical Linear Algebra (20 credits): This module focuses on computational techniques for solving mathematical problems in finance, including numerical integration, optimization algorithms, and linear algebra methods.
• Financial Engineering Project (60 credits): This module involves a substantial research project, allowing students to apply their knowledge and skills to a real-world financial problem.
Students work with research leaders in mathematics and computer sciences, gaining valuable experience in independent study and research.

Optional Modules:

Students choose 60 credits from a range of optional modules, including:

• Algorithmic and High Frequency Trading (10 credits): This module explores the use of algorithms and high-frequency trading strategies in financial markets.
• Deep Learning 1 (10 credits): This module introduces the fundamentals of deep learning, a powerful machine learning technique with applications in finance.
• Deep Learning 2 (10 credits): This module delves deeper into advanced topics in deep learning, including convolutional neural networks and recurrent neural networks.
• Financial Mathematics (20 credits): This module covers advanced mathematical concepts relevant to finance, such as stochastic processes, interest rate models, and credit risk modeling.
• Foundations of Statistical Inference (20 credits): This module provides a rigorous foundation in statistical inference, essential for data analysis and decision-making in finance.
• Integer Programming and Combinatorial Optimization (20 credits): This module explores optimization techniques for solving problems with discrete variables, relevant to financial planning and resource allocation.
• Interest Rate and Credit Risk Modelling (10 credits): This module focuses on modeling interest rates and credit risk, crucial for managing financial assets and liabilities.
• Largescale Optimization for Machine Learning (10 credits): This module covers optimization algorithms for large-scale machine learning problems, relevant to financial data analysis and prediction.
• Mathematical Securitisation (10 credits): This module explores the mathematical principles behind securitization, a process of packaging and trading financial assets.
• Quantitative Funds Management (10 credits): This module explores the principles and practices of quantitative investment management.
• Statistical Methods in Finance and Economics (20 credits): This module covers statistical methods commonly used in finance and economics, including regression analysis, time series analysis, and hypothesis testing.
• Time Series and Prediction (10 credits): This module explores time series analysis and forecasting techniques, relevant to financial data analysis and prediction.

Assessment:

The assessment methods for the Financial Engineering Masters/MSc program typically include:

• Exams: Written exams assess students' understanding of core concepts and theories.
• Assignments: Coursework assignments allow students to apply their knowledge and skills to practical problems.
• Project: The research project is assessed based on its originality, methodology, analysis, and presentation.

Teaching:

The Financial Engineering Masters/MSc program employs a variety of teaching methods, including:

• Lectures: Lectures provide a structured overview of key concepts and theories.
• Seminars: Seminars offer opportunities for interactive discussions and critical analysis of topics.
• Workshops: Workshops provide hands-on experience with software packages and computational tools.

Careers:

The Financial Engineering Masters/MSc program prepares graduates for a wide range of careers in quantitative analysis within economic and financial sectors, including:

• Quantitative Analyst (Quant): Quants develop and apply mathematical models to analyze financial markets and make investment decisions.
• Financial Engineer: Financial engineers design and implement financial products and strategies using mathematical and computational techniques.
• Risk Manager: Risk managers assess and manage financial risks for institutions and individuals.
• Actuary: Actuaries use mathematical and statistical methods to assess and manage financial risks in insurance and other industries.
The program also provides a strong foundation for postgraduate research, either for academic pursuits or to further qualify graduates for specialized roles in financial institutions.

Other:

The University of Birmingham's Careers Network offers comprehensive support to students, including:

• Tailored careers guidance: Expert advice from specialized teams within each academic college.
• Exclusive work experience opportunities: Mentoring, global internships, and placements.
• One-to-one support: Assistance with CVs and job applications.
The program emphasizes the importance of independent study and research, a crucial skill for quantitative analysts and researchers. Graduates are highly sought after by employers in various sectors, including finance, banking, insurance, and industrial management.

Annual tuition fee for 2024/25

£13,770 - UK students £27,270 - International students Are you an international applicant? All international applicants to this course will be required to pay a non-refundable deposit of £2,000 on receipt of an offer, to secure their place. Find out more about the deposit >>. Postgraduate Loans (PGL) for Masters students UK and EU students (with settled or pre-settled status) looking to pursue a Masters programme in the UK can apply for a non-means-tested loan from the British government via the Student Loans Company (SLC). The loan will be paid directly to you, into a UK bank account. It is intended to provide a contribution towards the costs of Masters study and whether the loan is used towards fees, maintenance or other costs is at your own discretion.