Prospective students

The MSc Computational Finance introduces advanced modules focused on providing quantitative and modelling skills which appeal to 'quant' roles in trading, research, regulation and risk. There is large demand in the financial services industry, the Bank of England and financial regulatory authorities to raise the level of computational knowledge, data manipulation and analytic skills. A notable aspect of this applied MSc programme is that students will be educated to advanced level programming together with a sound mathematical and statistical basis, making it distinct from the large number of courses offered by business schools and also from other finance-oriented masters at UCL. This MSc sits alongside the Centre for Doctoral Training in Financial Computing and grounds its teaching resources on the Financial Computing and Analytics Group.

Students will develop an advanced knowledge of computational methods in finance enabling them to develop a successful career in the financial industry within ‘quant’ teams.


Upcoming Events and Open Days

Virtual Open Day - 28th April 2017, Time TBC

 

Find out more about the benefits of studying the MSc Computational Finance at UCL, the top-rated university in the UK for research in Computer Science.

Take the opportunity to (virtually) meet the team behind the Masters, with a chance to chat with them via dedicated online forums. 

For further information and to register your attendance, please click here.

MSc Computational Finance consists of 8 taught modules (4 core, 4 optional) and a dissertation. 

Core Modules Term 1

COMPG004 Market Risk Measures and Portfolio Theory

COMPG004 Market Risk Measures and Portfolio Theory

The module aims to familiarise students with key concepts and models in general asset pricing, portfolio theory, and risk measurement. Those concepts and models include risk aversion, utility functions as a representation of preferences, efficient frontiers, Markowitz Portfolio theory, the Capital Asset Pricing model, Value at Risk, and Expected Shortfall.

 

Further syllabus information can be found here.

COMPG005 Numerical Analysis for Finance

COMPG005 Numerical Analysis for Finance

The module aims to give students an introduction to numerical/computational methods and techniques with code examples in Matlab and an emphasis on applications in finance.

 

Further syllabus information can be found here.

Core Modules Term 2

COMPG001 Financial Data and Statistics

COMPG001 Financial Data and Statistics

The course is aimed at introducing to financial data analytics. The course is primarily focused on the observation of financial market dynamics of both individual assets and collective group of assets and the individuation of regularities, patterns and laws from a statistical perspective. Instruments to analyse, characterize, validate, parameterize and model complex financial datasets will be introduced. Practical issues on data analysis and statistics of high frequency and low frequency financial data will be covered.


Further syllabus information can be found here.

COMPGF04 Financial Market Modelling and Analysis

COMPGF04 Financial Market Modelling and Analysis

This module will introduce students to the field of modelling and analysing financial markets with emphasis on (i) the wide variety of deterministic and discrete-time methods that are available; and (ii) numerical simulation of the financial markets, including agent-based modeling.

 

Further syllabus information can be found here.

Core Modules Dissertation

COMPGF99 MSc Computational Finance Project

COMPGF99 MSc Computational Finance Project

Between June and August students do a research project resulting in a thesis of about 10,000 words or 50 pages. This is usually undertaken within a summer placement in an industry environment organised by one of the Programme Directors, Donald Lawrence, with both an academic and an industrial supervisor. This gives students experience of conducting project work in a real-life setting and may lead to the offer of a permanent job at the end of the project; so far this happened in 20-30% of the cases.

In recent years, commercial partners have included AlgoDynamix, Algo Trading, Almanis, AXA, Banking Science, BNP Paribas, Chapelle Consulting, Citibank, Commerzbank, Credit Suisse, Deutsche Bank, Ernst&Young, Fund Apps, Gain Capital, Intel, LCH.Clearnet, Liberis, Morgan Stanley, Mysis, Message Automation, Nomura, Oasis AWS, OptiRisk, Principal Financial Group, PricewaterhouseCooper, Royal Bank of Scotland, Santander, Société Générale, Thomson Reuters and TSB Bank. Every year there are changes to this list and, although all students have been placed in previous years, there is no guarantee for the future, so that it cannot be excluded that, especially in the case of an economic downturn, students may need to resort to a research project internal to UCL with only an academic supervisor.

Optional Modules Term 1

COMPG007 Operational Risk Measurement for Financial Institutions

COMPG007 Operational Risk Measurement for Financial Institutions

The module aims to familiarise students with key concepts in the measurement and management of operational risk in the financial services. It will help them to understand the current issues and challenges faced by the sector, from a methodological, regulatory and financial standpoint. By detailing the most current debates in the field, the course aims at allowing the students to subsequently become positive agents of solutions in the market place and in research in operational risk.

 

Further syllabus information can be found here.

COMPG008 Stochastic Processes for Finance

COMPG008 Stochastic Processes for Finance

Rehearse/survey probability theory and give a systematic introduction to stochastic processes and their applications without stressing too much the measure-theoretical aspects and other mathematical formalisms. The module is aimed at students with an undergraduate degree in engineering, physics, computer science and the like, who have a good basis in calculus and have already come into contact with aspects of probability and statistics for ad hoc applications like transport equations, laboratory data treatment, and quantum mechanics, but have not attended yet a dedicated course on stochastic processes. The course material will unfold with references to its historical development and early applications in physics/engineering the students may already heard of, ending with current-day applications in finance.

 

Further syllabus information can be found here.

COMPG012 Financial Engineering

COMPG012 Financial Engineering

An introduction to the applied mathematical and computational aspects of Quantitative Finance.

 

Further syllabus information can be found here.

COMPG013 Market Microstructure

COMPG013 Market Microstructure

This course provides the student with a structured overview over both the main empirical facts and major theoretical approaches in market microstructure. It will comprise of five main parts:

1) An introduction to limit order markets.

2) Empirical investigation of financial data.

3) Price impact.

4) The limit order book as a queuing system.

5) The relationship between impact, the bid-ask spread, the tick size, and liquidity.

 

Further syllabus information can be found here.

COMPGS06 Financial Institutions and Markets

COMPGS06 Financial Institutions and Markets

The module exposes participants to an overview of the financial information sector and interaction with global financial markets, which constitute an important application domain of computer science in the southeast UK as well as main global financial centers. The module facilitates transfer of substantial domain knowledge based on IB Analyst training program the lecturer delivers in major international firms.

 

Further syllabus information can be found here.

 

 

 

Optional Modules Term 2

COMPG009 Networks and Systemic Risk

COMPG009 Networks and Systemic Risk

The first part of the course presents a general introduction to complex networks and dynamical processes. The second part is focused on specific applications to the study of contagion in financial networks. Overall, the course represents an introduction to the topic of systemic risk and stress propagation in networked systems.

 

Further syllabus information can be found here.

COMPG014 Machine Learning with Applications in Finance

COMPG014 Machine Learning with Applications in Finance

The module introduces students to the field of Machine Learning with a focus on supervised and unsupervised learning, presenting specific applications in Finance for each subtopic.

 

Further syllabus information can be found here.

COMPGC05 Algorithmics

COMPGC05 Algorithmics

This module aims to introduce more formal aspects of algorithms and data structures than those in the first term. It covers properties of data types such as queues and search trees; techniques for analysing the complexity and decidability of algorithms; and formal models of computation.

 

Further syllabus information can be found here.

COMPGC06 Database and Information Management Systems

COMPGC06 Database and Information Management Systems

This module builds on the introduction to relational databases found in the Systems Infrastructure module. It covers advanced data modelling and database development methodology, the techniques exploited by relational database technologies relating in particular to query processing and transaction management, and post relational database technologies including object oriented databases and web databases. The coursework is an interesting group project lasting the duration of the term and building a web facing database system using very contemporary technologies.

 

Further syllabus information can be found here.

COMPGC22 Software Engineering

COMPGC22 Software Engineering

This module covers a range of Software Engineering material, following on from that introduced in the Architecture & Hardware module. The emphasis is on the knowledge needed to be able to model, design, implement and evaluate larger software systems effectively. The content starts with development lifecycle models, such as agile development, and then continues to cover requirements specification, the Unified Modelling Language (UML), software architecture, object-oriented analysis and design, design patterns and testing. Its is an inherently practical subject; students undertake a substantial group project, working through a number of stages of the development of a larger software application.

 

Further syllabus information can be found here.

MATHGF06 Applied Computational Finance

MATHGF06 Applied Computational Finance

Success in mathematical finance requires confidence and expertise in applying numerical analysis and programming to solve a wide range of pricing and risk management problems. This course presents numerical schemes for topics in derivative pricing together with programming in C++ and Python.

 

Further syllabus information can be found here.

Usually students choose their 4 optional modules (60 credits) from the programme diet above. If the timetables are compatible and upon authorisation by the Programme Director and the module lead, up to two optional modules may come from outside the programme diet. Modules taught in the UCL Departments of Computer Science (list), Mathematics (list), Physics (list) and Statistics (list) have good chances to be approved by the Programme Director.

An upper-second class UK bachelor's degree (or equivalent overseas qualification) in computer science, mathematics, statistics, physics, engineering or another similar quantitative subject. Graduates in economics, finance, business administration, actuarial science or similar are considered if their transcripts show a fair number of modules in mathematics, probability, statistics and econometrics with high marks. Programming experience is a plus, but not mandatory. Relevant work experience may also be taken into account.

 

English Language Requirements

If your education has not been conducted in the English language, you will be expected to demonstrate evidence of an adequate level of English proficiency.

The English language level for this programme is: Good

Further information can be found on our English language requirements page.

 

International students

Country-specific information, including details of when UCL representatives are visiting your part of the world, can be obtained from the International Students website.

UK/EU fees (FT):     £18,580 for 2017/18

Overseas fees (FT): £27,540 for 2017/18

For a comprehensive list of the funding opportunities available at UCL, including funding relevant to your nationality, please visit the Scholarship and Funding website.

Tuition Fee Deposit

This programme requires that applicants firmly accepting their offer pay a deposit. This allows UCL to effectively plan student numbers, as students are more demonstrably committed towards commencing their studies with us.

For full details about the UCL tuition fee deposit, please see the central UCL pages.

Tuition fee deposits within the Department of Computer Science are currently listed as:

UK/EUOverseas
Full-time*Part-timeFull-time*Part-time
£2000£1000£2000£1000
*where part-time is an available mode of study

The Department's graduates are particularly valued as a result of the our international reputation, strong links with industry, and ideal location close to the City of London. Graduates are especially sought after by leading global finance companies and organisations. The top 20-30% of our graduates receive a job offer from the host of their summer work placement.

Top graduate destinations:                 

  • Credit Suisse
  • Goldman Sachs
  • Morgan Stanley
  • KPMG

Top graduate roles:                                

  • Financial Account Manager
  • Management Consultant
  • Investment Analyst
  • Chartered Accountant

Top further study destinations:

  • University of Cambridge
  • UCL

Average starting salary £35,000 (all data from Destinations of Leavers from Higher Education (DLHE) survey of 2015 Graduates).

Programme Administrator
Dr Saini Manninen
Office 5.22, Malet Place Engineering Building 
0207 679 7937
advancedmsc-admissions@cs.ucl.ac.uk

More information

To apply now click here.

Students are advised to apply as early as possible due to competition for places. Those applying for scholarship funding (particularly overseas applicants) should take note of application deadlines.

Deadline 17 June 2017.