Careers all students who secured a placement during their studies were offered a job after graduation
Why this course?
All our students who secured a placement while studying for their MSc in Quantitative Finance were offered a job after graduation
Globally, financial tools, products and software are becoming increasingly complex and sophisticated. There's a demand for market-aware graduates who can demonstrate an understanding of mathematical models used in these products. The MSc in Quantitative Finance has been developed to address this need.
This Masters degree is an innovative cross-faculty alliance between Strathclyde Business School and the Faculty of Science. It's been designed for students with a strong aptitude for mathematics, statistics and computing who've not studied these topics in detail in their undergraduate degree. The course allows students with different degree backgrounds to learn the necessary skills to move into the financial industry.
Our Masters in Quantitative Finance programme will prepare you for a career in financial engineering and risk management. Careers include roles as a hedge fund manager or financial analyst.
a good understanding of financial theory and analysis
an appreciation of financial markets
a practical understanding of numerical methods in finance
an introduction to programming for financial applications
an understanding of the role of computers in business processes
What you’ll study
The curriculum in this postgraduate degree provides a balance between finance and mathematical theory, computer implementations of this theory, and practical skills and knowledge.
Core classes are undertaken in the first semester, and address themes such as principles of finance and quantitative methods for finance, as well as foundation classes in mathematics, statistics and computing science.
You may be able to undertake an industrial based MSc project. This takes place in the third semester of the course between June and September. The placement can be based in the UK or in another country and are typically paid internships.
The Department of Mathematics & Statistics has teaching rooms which provide you with access to modern teaching equipment and access to University computing laboratories with all necessary software available.
You'll also have access to a common room facility which gives you a modern and flexible area which can be used for individual and group study work and is also a relaxing social space.
The educational intention of this class is to bring all of the students up to the same level of mathematical and statistical training so that they can then undertake the financial theory classes in semester 2.
This class provides an introduction to the basic principles of financial decision taking and the theory of finance. It will develop the basic principles of valuation, the nature of risk and uncertainty, and the relationship between risk and returns. While the analysis will be developed in the context of corporate finance, looking at companies’ decisions on the investments in assets and how these investments will be funded and the nature of the markets in which they take place, it will also consider the principles underlying financial reasoning that can be applied on a more general basis.
The aim of the class is to provide you with an understanding of the financial system and the roles and functions of financial markets and institutions. A particular emphasis is placed on understanding the roles of intermediaries such as banks and investment firms.
You'll develop an understanding of the various characteristics and roles of fixed income, equity, and foreign exchange markets. While some attention will be given to the UK financial markets, the global nature of financial markets will be widely discussed. This class aims to equip you with an awareness and understanding of financial markets and institutions in the context of the global economy. Particular emphasis will be placed on the role and contribution of the banking sector.
This class covers the reasons for, and nature of, of financial markets and institutions with a particular focus on banking, the global nature of these markets and their regulation.
understand the fundamentals of Python to enable the use of various big data technologies
understand how classical statistical techniques are applied in modern data analysis
understand the potential application of data analysis tools for various problems and appreciate their limitations;
be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability, while also addressing design trade-offs and their impacts
be familiar with the Map-Reduce programming paradigm, to enable students to write programs which can execute in massively parallel cloud-based infrastructures
You're required to take 60 credits of optional classes from those listed below to ensuring that your curriculum contains 20 credits from each list. You should refer to relevant programme regulations in the University calendar regarding class selection requirements and credits in order to meet the programme requirements.
The aim of the class is to provide you with an understanding of the main ideas of behavioural finance. A particular emphasis is placed on understanding the roles of non-rational actions and the development of new financial models that incorporate these ideas.
You'll engage with up to date research and develop a critical view of existing and new finance theories and models. It aims to introduce you to the rapidly evolving area of behavioural finance. You'll develop an understanding of behavioural finance and an appreciation of its possible implications and applications.
This class introduces you to behavioural finance and provides you with an understanding of the main flaws of 'traditional' finance theory from a behavioural finance viewpoint. It will allow you to develop the ability to discuss issues arising from violations of the rationality assumption and will enable you to evaluate new theoretical models based on research in psychology.
The course will allow you to appreciate the role of new developments in finance and their possible implications for established views of the functioning of financial markets.
The aim of this class is to develop an appreciation of the investment characteristics of different types of securities, particularly bonds and shares, and to develop an understanding of how such securities are valued. This class will build on the analysis developed in the first semester class, Principles of Finance. It will consider the determination of interest rates, the valuation of bonds, the management of bond portfolios, and the valuation of equities.
The aim of this class is to examine the Markowitz (1952) approach to optimal portfolio selection. The class explores issues relating to optimal portfolio choice and issues in practical fund management.
The aim of this class are to provide a strong grounding in derivatives that may be used to manage the financial risks faced by individuals, financial institutions and business corporations. It places an emphasis on corporate treasury management and the role of derivatives in managing treasury risk.
This module aims to provide conceptual and practical understanding of data modelling, database design and database technology. It also aims to give practical experience of developing web-based applications that integrate database server interaction.
The aim of this class is to equip students with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. On completion of this class students will:
understand the aims and fundamental principles of machine learning
understand a range of the key algorithms and approaches to machine learning
be able to apply the algorithms covered and interpret the outcomes
understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations
This module will help you to understand (a) nature of evolutionary computing (b) suitability of evolutionary computing for financial applications. It will provide practical experience in developing and operating evolutionary computing approaches for financial applications.
The class aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.
You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.
This module will introduce you to a number of diverse topics in game theory and its applications to financial problems as well as giving a sound background on network theory at both theoretical and applied level.
Compulsory module for students enrolled on the MSc.
The MSc in Quantitative Finance scored 100% in overall student satisfaction in 2016/17 (Postgraduate Taught Experience Survey)
Teaching is student-focused, with students encouraged to take responsibility for their own learning and development. Classes are supported by web-based materials.
Class sizes are kept at a level which allows for good contact between students and teaching staff. In the 2016/17 Postgraduate Taught Experience Survey, the course scored 100% in overall student satisfaction.
Classes are delivered by a number of teaching methods:
lectures (using a variety of media including electronic presentations and computer demonstrations)
The form of assessment varies from class to class. For most classes the assessment involves both coursework and examinations.
Minimum second-class honours degree or international equivalent in:
science subjects (eg physics, chemistry or computing science)
business subjects (eg business studies, accounting or economics)
Mathematics/statistics graduates should contact the course director to discuss applying for this course.
Prospective students with relevant experience or appropriate professional qualifications are also welcome to apply.
For Australia and Canada, normal degrees in relevant disciplines are accepted.
This MSc requires some prior mathematical knowledge for example:
A level or equivalent or undergraduate classes in:
If possible, please provide evidence of this in your application (eg transcript, certificate etc)
Upon successful completion, you will be able to progress to this degree course at the
University of Strathclyde.
Fees & funding
All fees quoted are for full-time courses and per academic year unless stated otherwise.
Rest of UK
How can I fund my course?
A number of scholarships are available for outstanding UK, EU and international applicants. For details, please visit our scholarship search.
Scottish and non-UK EU postgraduate students
Scottish and non-UK EU postgraduate students may be able to apply for support from the Student Awards Agency Scotland (SAAS). The support is in the form of a tuition fee loan and for eligible students, a living cost loan. Find out more about the support and how to apply.
Students ordinarily resident in England may be to apply for postgraduate support from Student Finance England. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Students ordinarily resident in Wales may be to apply for postgraduate support from Student Finance Wales. The support is a loan of up to £10,280 which can be used for both tuition fees and living costs. Find out more about the support and how to apply.
Postgraduate students who are ordinarily resident in Northern Ireland may be able to apply for support from Student Finance Northern Ireland. The support is a tuition fee loan of up to £5,500. Find out more about the support and how to apply.
We work closely with the University's Careers Service. They offer advice and guidance on career planning and looking for and applying for jobs. In addition they administer and publicise graduate and work experience opportunities.
Our graduates have gone on to find careers with reputable companies such as Deloitte, PWC, Bank of Ireland and BlackRock to name a few.
Job titles include:
Business Change Consultant
Advisor to CEO
Manager Transfer Pricing Economist
Trainee Actuarial Analyst
To date, all of our students who secured a placement during their studies were offered a job after graduation.
We encourage Quantitative Finance students to join our closed group on LinkedIn. Graduates who are part of this group benefit from having a readily built professional network to take advantage of straight away. We post jobs on the page, encourage interactions within the group and keep them up to date with relevant industry news.
This is a fantastic platform for someone starting out in the finance industry, with advice and networking opportunities from like-minded, established professionals at their fingertips. It's also a great way for you to keep in contact with others from your course.