Who is it for?
To successfully complete this Mathematical Trading and Finance master’s course, you must have a good understanding of mathematics.
You may well have studied finance, economics, engineering or maths or physics as an undergraduate. Or you might have a bachelor’s degree in a science subject, in particular computer science.
You should have a general interest in mathematics and statistics.
You should have a general interest in learning the more quantitative and mathematical techniques used in financial markets, but you don’t need to have a background in finance.
*You might still see us referred to as Cass Business School. Find out more about our name change.
The difference between the MSc Mathematical Trading and Finance to the other two quants courses (MSc Financial Mathematics and MSc Quantitative Finance) are core modules which focus on quantitative trading and structuring.
You’ll study core modules which focus on the theory of finance and different financial assets. You will look at how these assets are priced and used for asset management or risk management purposes.
The second type of core modules cover the mathematical and statistical aspects needed in quantitative finance, including some stochastics. This also includes learning some programming languages, mainly Python, but Matlab and VBA modules are being offered as electives.
Finally, Term three offers you flexibility within your masters; either by writing a dissertation or undertaking a project. You can complete your postgraduate degree entirely choosing electives.
What will you learn
- You will have learned a good understanding of the technical aspects used in financial
markets, starting from the financial theory, looking at different financial instruments and showing various applications of the theoretical concepts.
- You will gain a good understanding of stochastics, mathematical finance and econometrics as well as some programming.
- You will also obtain a very good understanding of different financial assets, in particular derivatives, and how they can be used in different context, such as risk management, asset management or structuring.
- The MSc Mathematical Trading and Finance programme will also help you do understand the financial theory used in financial markets with an emphasis on practical applications.
- You will three different possibilities to complete your degree in the third term, including writing a dissertation or an applied project.
- You can also opt to get all the credits through taught electives. Popular
electives include Behavioural Finance, Trading and Hedging in the FOREX Market, Technical Analysis, Hedge Funds or Python.
All of our MSc courses start with two compulsory induction weeks which include relevant refresher courses, an introduction to the careers services and the annual careers fair.
This module focuses on the introduction of pricing financial securities, which forms the basis for understanding asset pricing behaviour and the cornerstone of many asset pricing models.
The focus is on spot securities, mainly equities and debt instruments. The module also introduces students to the fundamental theory used by practitioners and academics in the wider field of finance, in particular asset management.
That includes portfolio theory, the CAPM, factor models and measuring risk and return. Those concepts are widely used by financial market participants. At the end of this module the various building blocks are being put together in the discussion of performance and persistence of performance of mutual funds.
To introduce derivatives and derivative models in the context of financial risk management. To complement general finance courses with specific instruction in the key derivatives area.
To enable you to use models in this area in practical applications. To transmit to you the fundamental mathematical modelling techniques underpinning the subject.
Foundations of Econometrics
The course provides the essential statistical and econometric techniques needed to conduct quantitative research in finance and economics.
This combination of econometric theory and application will enable you to understand and interpret empirical findings in a range of financial markets, including reading of empirical academic literature and critical assessment econometric applications undertaken by industry practitioners.
Stochastic Modelling Methods in Finance
The module provides the necessary mathematical tools on which the entire programme is based.
- To introduce you to Brownian motion and stochastic calculus
- To provide examples of applications of stochastic calculus in financial areas
- To provide the tools required for a rigorous understanding of financial modelling and pricing techniques
- To learn fundamental numerical methods for simulating trajectories of commonly used stochastic processes.
Applied Research Tools
Strong research skills are a key element of development strategy for companies and institutions large and small. In particular the ability to programme and to automate procedures. This module focuses on Python as a programming language and students will learn the basics in term 1 with some applications to finance being introduced in term 2.
The module introduces the main programming skills which are helpful in the financial industry. Operating on matrices, loops, conditional statements, subroutine/functions/procedures and optimisations are core skills which are being introduced in this module.
To provide a foundation in a crucial area of financial markets and quantitative finance. To complement the general derivatives course with specific instruction in a key derivatives area.
To acquaint you with the main modelling streams in fixed income securities. To enable you to use models in this area in practical applications. To transmit to you the fundamental mathematical modelling techniques underpinning the subject.
Financial disasters are a constant reminder of the relationship between financial risk and reward.
The quantitative approach to this relationship is ever more dominant in the market and subject to constant innovation.
As market participants need to keep abreast of new developments, the Risk Analysis module provides a good path of study in this field.
The aim of this module is to help you develop a solid background for evaluating, managing and researching financial risk. To this end you will learn to analyse and quantify risk according to current best practice in the markets.
This trading focused module introduces students to the world of computer-based trading and dealing with high frequency data.
Some relevant market microstructure concepts, such as bid-ask spreads, liquidity and other concepts are being introduced before the focus moves to high frequency trading strategies and other automated trading concepts.
This module provides on overview of machine learning concepts, techniques and algorithms used in practice to describe and analyse complex data, and design predictive analytics methods. You should expect to engage with the main idea and intuition behind modern machine learning tools from a practical perspective. Standard computing skills in R,Python and Matlab will be used to put in practice the theory discussed during the lectures.
You may choose from the three options in your final term.
- Option 1: Students can take five specialist elective modules (5 x 10 credits).
- Option 2: Students can opt to write a 10,000-word Business Research Project (40 credits) and take one specialist elective module (1 x 10 credits).
- Option 3: Students can opt to write a 3,000-5,000-word Applied Research Project (20 credits) and take three specialist elective modules (3 x 10 credits)
Business Research Project
It is important for aspiring professionals to demonstrate, on an individual basis, their ability to apply concepts and techniques they have learned in an in-depth study of a topic of their choice and to organise their findings in a report, all conducted within a given time limit.
To train you to undertake individual research and provide you with an opportunity to specialise in a contemporary business or finance topic related to your future career aspirations.
You are required to submit a project of approximately 10,000 words on any subject area covered in the rest of the programme.
Typical projects can involve any of the following: extracting data from electronic databases or by hand; statistical analysis of large or small populations; interviews; case studies of an industry or a sector or of a business / finance issue in a particular country setting.
Applied Research Project
The aim of this module is to enable you to demonstrate how to integrate your learning in core and elective modules and then apply this to the formulation and completion of an applied research project.
You will be required to demonstrate the skills and knowledge that you have acquired throughout your MSc study.
You will undertake a short piece of applied research on a question of academic and/or practical relevance.
Guidelines will be provided in order to help you identify the research question.
Based on your chosen topic, you must write a report of around 3,000–5,000 words that summarises and critically evaluates your method and your findings.
Electives offered in 2019
- Advanced Financial Engineering and Credit Derivatives
- Advanced Financial Modelling and Forecasting
- Credit Risk Management
- Hedge Funds
- Intro to Python
- Trading and Hedging in the Forex Market
- Trading and Market Microstructure
- Ethics, Society and the Finance Sector
- Introduction to Fintech (taught in Italy)
We review all our courses regularly to keep them up-to-date on issues of both theory and practice.
To satisfy the requirements of the degree course students must complete:
- nine core courses (Eight at 15 credits each, one at 10 credits)
- five electives (10 credits each)
- three electives (10 credits each) and an Applied Research Project (20 credits)
- one elective (10 credits) and a Business Research Project (40 credits)
Assessment of modules on the MSc in Mathematical Trading and Finance degree in most cases, is by means of coursework and unseen examination.
Coursework may consist of standard essays, individual and group presentations, group reports, classwork, unseen tests and problem sets. Please note that any group work may include an element of peer assessment.
Term dates 2021/22
- Induction: 20th September 2021 - 1st October 2021
- Term one: 4th October 2021 - 17th December
- Term one exams: 10th January 2022 - 21st January 2022
- Term two: 24th January 2022 - 8th April 2022
- Term two exams: 25th April 2022 - 6th May 2022
- Term three - international: 9th May 2022 - 20th May 2022
- Term three: 23rd May 2022 - 8th July 2022
- Term three assessments: 11th July 2022 - 22nd July 2022
- Resits: 15th August 2022 - 26th August 2022
- Additional resit week – tests only: 29th August 2022 - 2nd September 2022
- Research Project Submissions: Thursday 1st September 2022
Note: term one resit tests for the Energy / Shipping Trade and Finance degrees will normally take place in the week immediately prior to the August resit period.
Course timetables are normally available from July and can be accessed from our timetabling pages. These pages also provide timetables for the current academic year, though this information should be viewed as indicative and details may vary from year to year.
Please note that all academic timetables are subject to change.
The teaching staff on the MSc in Mathematical Trading & Finance have many years of practical experience working in the financial services sector and are also active researchers in their fields
This knowledge and experience inform the highly interactive lectures that make up the MSc in Mathematical Trading & Finance.
Module Leaders include:
How to apply
We only accept online applications.
The application deadline is 1st August 2021: Late applications may be assessed on a case-by-case basis.
Documents required for decision-making
- Transcript/interim transcript
- Current module list if still studying
- Personal statement - this should be 500-600 words in length and answer the following:
- Why have you selected this course? What are your motivating factors?
- What are your areas of interest within the course?
- What contributions do you feel you can make to the course?
- How do you see the course affecting your career plans?
Documents which may follow at a later date
- English language test result if applicable
- Confirmation of professional qualification examinations/exemptions/passes, if applicable
- Two references
- For a successful application to receive an unconditional status all documents must be verified, so an original or certified copy of the degree transcript must be uploaded to the application form or e-mailed to the relevant Admissions Officer upon request
We cannot comment on individual eligibility before you apply and we can only process your application once it is fully complete, with all requested information received.
Unfortunately, as a result of the evolving situation regarding the coronavirus (COVID-19) pandemic, we can only offer online appointments to discuss your application. To book one, please get in touch with the relevant Admission Officer.
Please note; these are subject to availability.
Terms and conditions
Students applying to study at Bayes Business School are subject to City, University of London's terms and conditions.
- A UK upper second class degree or above, or the equivalent from an overseas institution.
- Your academic background should be in a highly quantitative subject such as mathematics, physics, engineering, economics or computer science and having covered areas such as statistics, linear algebra and calculus.
- Work experience is not a requirement for this course.
You may be requested to provide a syllabus of specific modules undertaken during your studies as part of the assessment process. This is not required at the point of submitting an application and will be requested directly by the admissions team only if required as part of the assessment.
English language requirements
If you have been studying in the UK for the last three years it is unlikely that you will have to take the test.
If you have studied a 2+2 degree with just two years in the UK you will be required to provide IELTS results and possibly to resit the tests to meet our requirements.
- The required IELTS level is an average of 7.0 with a minimum of 6.5 in the writing section and no less than 6.0 in any other section.
Fees in each subsequent year of study (where applicable) will be subject to an annual increase of 2%. We will confirm any change to the annual tuition fee to you in writing prior to you commencing each subsequent year of study (where applicable).
Deposit: £2,000 (usually paid within 1 month of receiving offer and non-refundable unless conditions of offer are not met)
First installment: Half fees less deposit (payable during on-line registration which should be completed at least 5 days before the start of the Induction period)
Second installment: Half fees (paid in January following start of course)
The job opportunities for students from the three quants master's programmes are very similar. They usually find employment with large investment banks, but also some smaller boutique finance firms, hedge funds or other specialist companies.
Working as quantitative analysts, in risk management, on fixed income security desks or in the asset management industry including hedge funds are typical jobs for students from the MSc Mathematical Trading and Finance degree.
Some students also secure positions on trading desks.
You will also have the skills to study for a PhD in the area of quantitative finance and financial markets.
Recent graduates have secured positions such as:
- Technology Associate
- Operations & Investor Relations Analyst
- Credit Trading Market Risk
Graduates from the MSc in Mathematical Trading & Finance degree go on to work in a wide variety of organisations. Some examples of destinations of recent graduates from the course include:
- J. P. Morgan
- VTB Capital
- Credit Agricole
- Global Market Solutions
- Dromeus Capital Management
- Quaternion Risk Management
Where they are working now
- UK: 56%
- Africa and Middle East: 11%
- Asia: 33%
Data provided from alumni who completed the annual destination data survey 2017/18