Financial Mathematics and Machine Learning

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

Overview

Overview

Learn to apply a wide range of mathematical and statistical techniques to model the behaviour of the financial markets. The MSc in Financial Mathematics is for you if you are planning a career in the more quantitative areas of banking and financial markets, or if you hope to undertake academic research in financial mathematics.

  • Ideal for students with a good undergraduate degree in mathematics or a subject with significant mathematics content. No prior knowledge of finance is expected
  • Learn from staff with considerable commercial experience in investment banking and financial markets
  • Undertake two modules on machine learning, a field that has become immensely important over the last few years (in finance, and beyond)
  • Attend practitioner seminars and network with experts from banking and finance

What you’ll study

You will gain a clear understanding of modern financial mathematics and machine learning, together with a range of numerical and computational techniques (many state-of-the-art) that form an important part of the toolkit of a typical practitioner.

The programme structure is flexible, so you can choose to focus on computational or mathematical modules, depending on your background, interests and future plans.

You will receive a comprehensive introduction to the field, including the structure of financial instruments and the operations of the markets, foundations of mathematical modelling in finance, tools for machine learning using Python, and introductory computer programming in C++.

You will go on to explore subjects such as pricing complex financial derivatives using a variety of mathematical models and techniques for managing risk, as well as advanced computational finance, including writing programs to price exotic derivatives using stochastic volatility and jump-diffusion models. 

You can also broaden your skills with unassessed courses onLaTeX (for document preparation), as well as extra-curricular workshops on programming in Excel/VBA and "modern" C++.

We offer all students the opportunity to take useful additional qualifications, including Microsoft Office Specialist, as well as qualifications in Excel and Bloomberg Market Concepts.

Structure

Programme Structure

Our course finder pages contain all the most up-to-date information about the Financial Mathematics with Machine Learning MSc, including details of the programme structure, compulsory and elective modules and study options.

Below is a full list of all modules which are expected to be available to students on this programme across the semesters.  Please note that this is for information only and may be subject to change. 

For further details of module contents, please see here.

MSc Financial Mathematics and Machine Learning (full-time structure)

Semester A

  • MTH761P:  Financial Instruments and Markets
  • MTH771P:  Foundations of Mathematical Modelling in Finance
  • MTH786P   Machine Learning with Python

Choose one from:

  • MTH712P:  Topics in Probability and Stochastic Processes
  • MTH790P:  Programming in C++ for Finance

Semester B

  • MTH762P:  Continuous-time Models in Finance
  • MTH787P:  Advanced Derivatives Pricing and Risk Management
  • MTH793P:  Advanced Machine Learning

Choose one from:

  • MTHM007:  Measure Theory and Probability
  • MTH773P:  Advanced Computing in Finance (MTH790P, or equivalent, is a prerequisite)

Semesters A, B and C

  • MTH763P:  MSc Financial Mathematics Project and Dissertation

Part-time students take four taught modules per year over a two-year period. The dissertation is started in Semester C of Year 1, and will be completed in Semester C of Year 2.

Entry requirements

Entry requirements

The majority of our applicants will have an undergraduate degree with first class (1st) or upper second class (2.1) honours, or international equivalent.  This should be in mathematics, or in a subject with a strong mathematics component such as physics, engineering or computer science. Offers will typically be made at 2.1 level (upper second class) or equivalent.

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 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

Teaching 

You will learn primarily through a combination of lectures and tutorials, in addition to a significant amount of independent study and research. 

Modules which cover computer programming will be taught in our dedicated computing lab, which is equipped with Bloomberg terminals. 

You are assigned an Academic Adviser who will guide you throughout your time at Queen Mary. The School of Mathematical Sciences also has a dedicated Student Support Officer to provide you with advice and guidance, with a focus on non-academic issues.

Assessment

33% project dissertation, 67% taught modules.

You will be assessed through a combination of:

  • tests (some computer-based) 
  • written examinations
  • coursework 
  • a final project and written dissertation - you may also be required to present your work and attend a viva (oral examination)

Dissertation topics or Research projects

Examples of possible projects include: 

  • the application of a 3-factor HJM model for pricing inflation-linked bonds
  • credit valuation adjustment (CVA) for interest rate swaps:  Investigation of wrong-way risk using Monte Carlo / OpenCL
  • the Heston model and its numerical implementation on a GPU using CUDA C/C++
  • jump-diffusion models for equity prices
  • the LIBOR market model for interest rate derivatives
  • option pricing using finite-difference methods on CPUs and GPUs

Fees

Tuition fees for Home and EU students

2020/21 Academic Year

Full time £19,850
Part time £9,950

Tuition fees for International students

2020/21 Academic Year

Full time £23,950
Part time £12,000

Part time fees are charged per annum over two years for a two year programme and per annum over three years for a three year programme. A percentage increase may be applied to the fees in years two and three.

This increase is defined each year and published on the intranet and in the Tuition Fee Regulations. A 3% increase was applied to the unregulated university fees in 2019/20. Further information can be viewed on our University Fees webpage, including details about annual increases.

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 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

Careers 

You will be equipped to undertake a wide range of careers in the banking and finance sector in roles that require a high level of numeracy, problem-solving and computing expertise, as well as in marketing, public services, consultancy, industry and commerce.

Typical roles would be in areas such as quantitative analysis, software development, derivatives trading, risk management, investment management, sales, marketing and consultancy.

Graduates may also go on to:

  • undertake PhD studies
  • work for major investment banks
  • use their programming skills (especially C++) in information technology companies and fintech start-ups

Profiles

News

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