Postgraduate menu

Financial Computing

MSc ( 1 year Full-time / 2 years Part-time )

Overview

This unique programme provides numerate graduates with the requisite expertise for the development of a professional career in the profitable and intellectually exciting triangle formed by mathematics, technology and finance.

Financial institutions rely on a functional blend of Mathematics, Technology and Finance to develop, enhance and sustain their competitive edge. The financial industry is undergoing a second wave of technological transformation related in particular to¿: the establishment of electronic trading platforms; improved risk management and pricing accuracy; the high performance computing implications of expanding regulatory requirements.

As a result there is increasing demand for numerate and technologically capable personnel from a wide range of top employers including investment banks, hedge funds, financial software companies, brokerage firms and consultancy firms. Other business lines are now developing similar paradigms where numerate, technologically able personnel are part of business innovation and decision-making.

The Financial Computing MSc is run jointly by the School of Mathematical Sciences and the School of Electronic Engineering and Computer Science. It is aimed at science and engineering graduates with mathematical exposure and some experience in computer programming. The content of the programme is a combination of technology and financial mathematics. It contains modules related to up-to-the-minute industry challenges such as high performance and GPU development.

Why study with us?

  • Queen Mary is a member of the prestigious Russell Group of leading UK universities, combining world-class research and teaching excellence.
  • You will be taught by distinguished academics and experienced practitioners who blend advanced theory with practical applications.
  • You will study in recently refurbished MSc student offices, with state-of-the-art computers and software.
  • We are conveniently located in central London, in close proximity to the two world renowned financial districts of the City of London and Canary Wharf.

Structure

Programme Outline

The study programme consists of four compulsory and four elective modules. The modules offered by the School of Mathematical Sciences will provide a solid understanding of the principles of mathematical finance. The modules offered within the Schools of Electronic Engineering and Computer Sciences will focus on key aspects of technological implementation.

Full time Study

You will study eight modules in total with an even split across semesters one and two. You will complete a 10,000 word dissertation/research project during semester three.

Full time Study with Industrial Experience

You will study eight modules in total with an even split across semesters one and two. You will complete a 10,000 word dissertation/research project during semester three. Expert staff will support the arrangement of your industrial placement, which will be carried out in the second year of your programme and assessed through the completion of the Industrial Placement Project.

The industrial placement takes place from the September following the taught part of the MSc and is for a maximum of 12 months. It is a student's responsibility to secure their own placement, but the EECS Placement Team will provide support. The Placement Team source and promote suitable opportunities, assist with applications, and with interview preparation.

The industrial placement consists of 8-12 months spent working with an appropriate employer in a role that relates directly to your field of study. The placement is currently undertaken after you have completed and passed the taught component of the degree and submitted your MSc project. The placement will provide you with the opportunity to apply the key technical knowledge and skills that you have learnt in your taught modules, and will enable you to gain a better understanding of your own abilities, aptitudes, attitudes and employment potential. The module is only open to students enrolled on a programme of study with integrated placement.

In the event that you are unable to secure a placement we will transfer you onto the 1 year FT taught programme without the Industrial Experience. This change will also apply to any student visa you hold at the time.

Part time Study

Your programme is delivered across two academic years. You will study four modules in each year of the programme, registering upon two modules per semester to balance your workload.

Our modules are assessed by a mixture of in-term assessment and final examinations. Examinations are held between late April and early June. Dissertations are evaluated in September. Successful completion of the MSc programme will result in the award of the MSc Financial Computing (possibly with Merit or with Distinction).

Structure

Semester 1 - Compulsory

  • ECS793P Introduction to Object-Oriented Programming
  • MTH771P Foundations of Mathematical Modelling in Finance
  • MTH739N Topics in Scientific Computing

Semester 1 - Elective

  • ECS765P Functional Programming
  • ECS765P Big Data Processing
  • ECS708P Machine Learning

Semester 2 - Compulsory

  • MTH777P Financial Programming

Semester 2 - Elective

  • MTH773P Advanced Computing in Finance
  • ECS769P Advanced Object-Oriented Programming
  • ECS786P Parallel Computing
  • MTH774P Portfolio Theory and Risk Management
  • MTH772P Stochastic Calculus and Black Scholes Theory

The Project

Each MSc Financial Computing student is required to complete a 60 credit project dissertation. A typical MSc project dissertation consists of about 30 word-processed pages (10,000 words), securely bound, covering a specific research-level topic in financial computing, usually requiring the student to understand, explain and elaborate on results from one or more journal articles and possibly to implement some industry quality code.

Module Outlines

ECS793P Introduction to Object-Oriented Programming

The core of the module is concepts and techniques of object-oriented programming in general and the use of Java in particular. It will consider issues in class and interface design such as immutability, composition versus inheritance, minimising dependency and generalisation. The module will also examine a number of Design Patterns. Exceptions, type variables, iterators and other advanced aspects of the core Java language will be covered. Java's Collections Framework will be considered in detail as an example of a coherent set of Java classes designed to work together, and for its use of generic typing. The more general aim is to consider the requirements for creating understandable, maintainable, and robust classes that can be easily reused by others in a team. There will also be some coverage of software engineering principles: analysis and specification of user requirements, object-oriented design, testing and debugging, refactoring. "Agile" software engineering techniques will be compared with top-down design using specifications.

MTH771P Foundations of Mathematical Modeling in Finance

This module will provide you with an introduction to important concepts from probability theory and stochastic processes that are useful in modelling asset price dynamics. The introduction of more advanced tools will be preceded by a brief review of basic probability theory. Important stochastic processes that underlie many models in finance, such as random walks, Brownian motion, geometric Brownian motion, and the Poisson process, will be discussed. An informal overview on Ito stochastic calculus and its application in finance will be given.

MTH739N Topics in Scientific Computing

This module covers the use of computers for solving applied mathematical problems in general, and problems in network science in particular. Its aim is to provide students with computational tools to solve problems they are likely to encounter in networks (search algorithms, generate network ensembles, ...) and in more generic applied mathematics problems (numerical solution of ordinary differential equations, random number generation) as well as to provide them with a sound understanding of a programming language used in applied sciences.

ECS713P Functional Programming

Recent approaches to systems programming frequently involve functional programming either overtly in the sense that they use modern functional programming languages for rapid prototyping, or more covertly in that they use techniques developed in the functional setting as a way of lending greater structure and clarity to code. This module gives a structured introduction to programming in the modern industrial functional language Haskell, and to techniques such as map-reduce and monadic programming.

ECS739P Big Data Processing

Big Data Processing covers the new large-scale programming models that allow to easily create algorithms that process massive amounts of information with a cluster of computer nodes. These platforms hide the complexity of coordinating complex parallel computations across the cooperating nodes, instead providing to developers a high-level programming model.

The module is based on the MapReduce programming model. Lectures explain how multiple data analysis algorithms can be expressed under this model, and executed automatically over clusters of machines. The module also covers the internal mechanisms that a MapReduce framework uses to coordinate and execute the job among the infrastructure. Finally, additional related topics in the area of Big Data, such as alternative large-scale processing platforms, NoSQL data stores, and Cloud Computing execution infrastructure are presented. In addition to the lectures, weekly lab sessions and coursework exercises present multiple applications where real world datasets are analysed using platforms such as Hadoop.

ECS708P Machine Learning

This course covers methods for machine learning from signals and data, including statistical pattern recognition methods, neural networks, and clustering. The aim of the course is to give you an understanding of machine learning methods, including pattern recognition, clustering and neural networks, and to allow you to apply such methods in a range of areas. By the end of the course you will be able to: Recall a range of machine learning techniques and algorithms, including neural networks and statistical methods; Use concepts from probability theory in machine learning; Derive and analyse properties of machine learning methods; Discuss the relative merits of different machine learning techniques and approaches and apply machine learning methods to the analysis of signals and data.

MTH777P Financial Programming

This course covers the fundamentals of development of financial applications based on a three tiered architecture. It will combine the use of Excel as a front end, VBA as middleware, and C++ as a compute engine to illustrate current practices in the financial industry. The course will emphasize code development best practices and object oriented development.

MTH773P Advanced Computing in Finance

The module will introduce concepts associated with advanced object-oriented programming concepts, such as inheritance and polymorphism, creating templates, advanced working with exception handling, stream input/output management, associative containers, algorithms, stacks, queues and binary trees, different search and sort methods, namespaces, advanced string class methods, and working with libraries, e.g. boost and STL. It also explores some of the contexts in which these techniques are useful.

ECS769P Advanced Object-Oriented Programming

The module will introduce concepts associated with advanced object-oriented programming concepts, such as inheritance and polymorphism, creating templates, advanced working with exception handling, stream input/output management, associative containers, algorithms, stacks, queues and binary trees, different search and sort methods, namespaces, advanced string class methods, and working with libraries, e.g. boost and STL. It also explores some of the contexts in which these techniques are useful.

ECS786P Parallel Computing

The module will introduce concepts associated with high performance computing, such as parallel processing, hardware acceleration, GPU (Graphics Processing Unit) programming and FPGA (Field Programmable Gate Array) programming.

MTH774P Portfolio Theory and Risk Management

A very important general problem in finance is to balance investment risk and return. In this module you will acquire skills and techniques to apply modern risk measures and portfolio management tools. Mathematically this involves the maximization of the expectation of suitable utility functions which characterizes the optimum portfolio. You will learn about the theoretical background of optimization schemes and be able to implement them to solve practical investment problems.

MTH772P Stochastic calculus and Black-Scholes Theory

This module enables you to acquire a deeper knowledge about the Ito stochastic calculus as applied to mathematical finance. You will learn about the role of the Ito integral in solving stochastic differential equations, and its role in developing the Black-Scholes theory for option pricing. You will also obtain a clear understanding of the simplifying assumptions in the Black-Scholes model. The course will develop pricing methodologies for both vanilla options (European call and put options) as well as exotic options such as barrier options.

Entry requirements

The majority of our applicants will have an undergraduate degree with first class or upper second class honours (or international equivalent).  Offers will typically be made at 2.1 level (upper second class) or equivalent.  Students with a good lower second class degree may be considered on an individual basis.  In some cases your offer may include additional conditions, such as minimum grades in specified modules, in order to ensure that you are sufficiently qualified for our MSc programmes.

Students applying to this programme should have studied a subject with a substantial mathematical component at the undergraduate level.  We welcome those from a variety of relevant disciplines, including mathematics, statistics, physics, engineering, economics and computer science.

Students from outside of the UK help form a global community here at QMUL. For detailed country-specific entry requirements please visit the International section of the QMUL website. If your first language is not English, you must provide evidence of your English language proficiency. Non-native English speakers are required to have minimum of IELTS 6.5 or equivalent. Find out more about our English language entry requirements.  If you have not achieved the required English language level yet, you may be eligible to take a Pre-sessional English course, or continue to take English language tests in your country to reach the level needed. Visit: www.sllf.qmul.ac.uk/language-centre/presessionals

Learning and teaching

As a student at QMUL, you will play an active part in your acquisition of skills and knowledge. Teaching is by a mixture of formal lectures and small group seminars. The seminars are designed to generate informed discussion around set topics, and may involve student presentations, group exercise and role-play as well as open discussion. We take pride in the close and friendly working relationship we have with our students. You are assigned an Academic Adviser who will guide you in both academic and pastoral matters throughout your time at QMUL.

Independent study

For every hour spent in formal study you will be expected to complete a further five to six hours of independent study. Your individual study time could be spent preparing for, or following up on formal study sessions, reading, producing written work, completing projects and revising for examinations.

The direction of your individual study will be guided by the formal study sessions you attend, along with your reading lists and assignments. However, we expect you to demonstrate an active role in your own learning by reading widely and expanding your own knowledge, understanding and critical ability.

Independent study will foster in you the ability to identify your own learning needs and determine which areas you need to focus on to become proficient in your subject area. This is an important transferable skill for people who envisage going on to a research career in academia or elsewhere.

Assessment

Students are assessed by a combination of coursework and exams. A few modules are assessed by coursework only. If a module is assessed by means of coursework alone, this is usually in the form of a research project or dissertation, and the tutor project supervisor offers guidance and support in the researching and writing of this piece of assessment.

Each MSc Financial Computing student is required to complete a 60 credit project dissertation. A typical MSc project dissertation consists of about 30 word-processed pages (10,000 words), securely bound, covering a specific research-level topic in financial computing, usually requiring the student to understand, explain and elaborate on results from one or more journal articles and possibly to implement some industry quality code.

Student Support

The School of Mathematical Sciences is committed to supporting you through your studies and there is a wide range of support services available both in the School and within QMUL to assist you during your time here.

You will be assigned an Academic Advisor when you enrol with us who will usually stay with you during your time at QMUL. Your Academic Advisor can help to guide you through any academic issues, such as choosing which modules to study.

The School of Mathematical Sciences has a dedicated Student Support Officer to provide you with advice and guidance on any issues that are not primarily academic. The Student Support Officer oversees the i2 - Keepin' It Real initiative which exists to promote and support a positive student experience and is also able to direct you to appropriate QMUL support services. For more information about central student support services, please see Advice and Counselling.

Fees

Tuition fees for Home and EU students

2018/19 Academic Year
Full time £18,050

Tuition fees for International students

2018/19 Academic Year
Full time £20,900

Funding

There are a number of sources of funding available for Masters students.

These include a significant package of competitive Queen Mary University of London (QMUL) bursaries and scholarships in a range of subject areas, as well as external sources of funding.

Queen Mary bursaries and scholarships

We offer a range of bursaries and scholarships for Masters students including competitive scholarships, bursaries and awards, some of which are for applicants studying specific subjects.

Find out more about QMUL bursaries and scholarships.

Alternative sources of funding

Home/EU students can apply for a range of other funding, such as Professional and Career Development Loans, and Employer Sponsorship, depending on their circumstances and the specific programme of study.

Overseas students may be eligible to apply for a range of external scholarships and we also provide information about relevant funding providers in your home country on our country web pages.

Download our Postgraduate Funding Guide [PDF] for detailed information about postgraduate funding options for Home/EU students.

Read more about alternative sources of funding for Home/EU students and for Overseas students.

Tel: +44 (0)20 7882 5079
email bursaries@qmul.ac.uk

Other financial help on offer at Queen Mary

We offer one to one specialist support on all financial and welfare issues through our Advice and Counselling Service, which you can access as soon as you have applied for a place at Queen Mary.

Our Advice and Counselling Service also has lots of Student Advice Guides on all aspects of finance including:

Tel: +44 (0)20 7882 8717

Graduate employment

The staff involved in the MSc of Financial Computing have strong links and research collaborations with industrial partners including Citigroup, Nomura, Bank of England, Morgan Stanley, UBS, RBS, Lloyds, Moodys, IBM, HP, BBC, and Tech City IT start-ups. Several of these companies will be involved in the teaching activities, providing guest lectures, as well as business use cases for applying Financial Computing techniques. Additionally, several of the MSc projects offered to the students will be performed in collaboration with an industry partner, including summer placement opportunities.

The MSc in the Financial Computing programme will prepare students for a wide range of careers, especially in the banking and finance sector, consultancy, industry and commerce. The computing skills acquired throughout the programme are highly valued in the financial sector.

Queen Mary’s careers service is run by a team of dedicated and professional staff. They offer advice through drop-in sessions and in-depth interviews, and run an extensive programme of seminars covering topics such as: interview skills; how to deal with psychometric tests; and surviving assessment centres. You will also be able to use the College’s extensive Careers Library. To find out more, please visit www.careers.qmul.ac.uk

Profiles

News

Bookmark and Share
Return to top