2020 applicants
Postgraduate Course

MSc Financial Technology

(Computer Science)

MSc Financial Technology

Overview

The details
Financial Technology (Computer Science)
October 2021
Full-time
1 year
Colchester Campus

To be competitive in our modern financial industry, it is not enough for a scientist to know how to write software, nor is it enough that one can mathematically analyse markets based on simplifying assumption by ignoring the computation required.

Our MSc Financial Technology (Computer Science) course give you the understanding of the specifics and the intricacies of financial and economics markets, the ability to apply advanced computing methods and the ability to design and program solutions that successfully exploit the synergy between finance, economics, and computation.

Our course will cover areas including:

  • financial markets
  • big data analytics
  • blockchain
  • AI
  • python programming

Our course will give you both the theoretical and technical skills that are specific for the finance industry, as well as transferable skills such as the ability to develop and present an argument, and the ability to work independently and in groups.

The MSc Financial Technology suite of courses has three unique variants for you to explore so you can tailor your Masters in financial technology to suit your interests and goals. In addition to our MSc Financial Technology (Computer Science) course, there is also the option of MSc Financial Technology (Finance) and MSc Financial Technology (Economics), giving you the chance to specialise in your preferred area of study.

Why we're great.
  • Essex has excellent research and teaching in computer science, economics, and business – this course is an interdisciplinary degree benefiting from the expertise in three departments.
  • Study a variety of topics including microeconomics and big data, software development and the underpinnings of the financial system.
  • Benefit from our proximity to London as well as our connections with employers.
THE Awards 2018 - Winner University of the Year

Our expert staff

Our course is taught in collaboration with the Centre for Computational Finance and Economic Agents (CCFEA) within the School of Computer Science and Electronic Engineering, the Essex Finance Centre (EFiC) within Essex Business School, and the Department of Economics, all of which have an international reputation.

We bring together experts with both academic and industrial expertise in the financial and IT sectors. Our staff are currently researching the development of real-time trading platforms, new financial econometric models for real-time data, the use of artificially intelligent agents in the study of risk and market-based institutions, operational aspects of financial markets, financial engineering, portfolio and risk management, among others.

Key academic members of staff you are likely to have contact with:

  • John O’Hara, who is a member of the London Mathematical Society, is the Director of our Centre for Computational Finance and Economic Agents. He has previously worked at a number of universities in South Africa and his PhD was in the theory of differential equations. He’s also completed an MSc in probability theory and a PGCE in education. His undergraduate degree was in mathematics.
  • Michael Kampouridis is a Lecturer in Computational Finance at Essex. His main research activities are in the use of machine learning algorithms for solving real-world problems from the fields of economics and finance. He has done extensive work in algorithmic trading and financial forecasting, and also in weather derivatives.
  • Panagiotis Kanellopoulos is a Lecturer in Computational Finance at Essex. His research mainly focuses on algorithmic game theory, an area in the intersection of economics (game theory) and computer science (analysis of algorithms) that aims to understand and design algorithms in strategic environments, such as auctions and markets.
  • Dr Maria Kyropoulou is a Lecturer at the School of Computer Science and Electronic Engineering, University of Essex. Her research interests lie in the areas of algorithmic game theory, algorithmic mechanism design, blockchain, and the design and analysis of algorithms. She is also the academic supervisor in a Knowledge Transfer Partnership (funded by InnovateUK) with a London-based law firm.
  • Alexandros Voudouris is a Lecturer at the School of Computer Science and Electronic Engineering. His research interests lie at the intersection of theoretical computer science, artificial intelligence, and microeconomic theory. In particular, he works on problems related to algorithmic game theory, algorithmic mechanism design, computational social choice, and fair resource allocation.

Specialist facilities

You will acquire practical and "hands-on" experience in our labs by using:

  • Matlab to implement quantitative methods in finance and economics, and their application to investment, risk management and trading
  • Python to model and develop machine learning algorithms with emphasis on the financial industry
  • Optimisation Solvers (such as Gurobi) in financial optimisation problems

Our work is supported by extensive networked computer facilities and software aids, together with a wide range of test and instrumentation equipment.

  • We have six laboratories that are exclusively for computer science and electronic engineering students. Three are open 24/7, and you have free access to the labs except when there is a scheduled practical class in progress
  • All computers run either Windows 10 or are dual boot with Linux
  • Software includes Java, Prolog, C++, Perl, Mysql, Matlab, DB2, Microsoft Office, Visual Studio, and Project
  • Students have access to CAD tools and simulators for chip design (Xilinx) and computer networks (OPNET)

Your future

Our MSc Financial Technology (Computer Science) course will equip you with the skillset at the intersection of computer science, economics and finance. You’ll gain expertise that is in high demand in the finance industry in the UK, and will become competitive in one of the primary recruiting sectors in the country and beyond.

With a skillset that combines both technical and analytical know-how from internationally known hubs within our University, our graduates will be able to find employment in very competitive financial institutions.

Entry requirements

UK entry requirements

A minimum of a 2.2 (or equivalent) degree in Computer Science or a related discipline such as Engineering, Finance, Economics, Maths, Statistics, Physics. We will accept graduates of any other degree that includes modules relating to Mathematics (calculus) or Econometrics (probability, statistics).

International & EU entry requirements

We accept a wide range of qualifications from applicants studying in the EU and other countries. Get in touch with any questions you may have about the qualifications we accept. Remember to tell us about the qualifications you have already completed or are currently taking.

Sorry, the entry requirements for the country that you have selected are not available here. Please select your country page where you'll find this information.

English language requirements

IELTS 6.0 overall with a minimum component score of 5.5

If you do not meet our IELTS requirements then you may be able to complete a pre-sessional English pathway that enables you to start your course without retaking IELTS.

Structure

Example structure

Most of our courses combine compulsory and optional modules, giving you freedom to pursue your own interests. All of the modules listed below provide an example of what is on offer from the current academic year. Our Programme Specification provides further details of the course structure for the current academic year.

Our research-led teaching is continually evolving to address the latest challenges and breakthroughs in the field, therefore to ensure your course is as relevant and up-to-date as possible your core module structure may be subject to change.

The example structure below is representative of this course if taken full-time. If you choose to study part-time, the modules will be split across 2 years.

Introduction to Programming in Python

The aim of this module is to provide an introduction to computer programming for students with little or no previous experience. The Python language is used in the Linux environment, and students are given a comprehensive introduction to both during the module. The emphasis is on developing the practical skills necessary to write effective programs, with examples taken principally from the realm of data processing and analysis. You will learn how to manipulate and analyse data, graph them and fit models to them. Teaching takes place in workshop-style sessions in a software laboratory, so you can try things out as soon as you learn about them.

View Introduction to Programming in Python on our Module Directory

Data Analytics in Finance

Big data - where datasets are so large they cannot be processed using traditional techniques – is useful to financial organisations. This module explores how to analyse big data and covers areas such as predictive analytics, risk modelling and corporate finance. You also learn about the application of data analytics in high frequency finance, fraud and personal finance.

View Data Analytics in Finance on our Module Directory

Big-Data for Computational Finance

This module is a mix of theory and practice with big data cases in finance. Algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structures and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.

View Big-Data for Computational Finance on our Module Directory

Computational Market Microstructure for FinTech and the Digital Economy

Equip yourself with principles of allocation and mechanism design from an operational perspective. Auction design and market microstructure of the stock market, liquidity provision in electronic financial markets such as dark pools, and capital adequacy of centralized clearing platforms are some of the specific applications that will be studied in the first part of this module. During the second part, you will be introduced to complexity economics of self-organisation, network modules, and strategic proteanism. Finally, you'll use network models to study economic interactions.

View Computational Market Microstructure for FinTech and the Digital Economy on our Module Directory

CCFEA MSc Dissertation

This dissertation is worth% and is submitted to FASer and the school in week 48. The presentation is worth 10% and takes place in weeks 49/50.

View CCFEA MSc Dissertation on our Module Directory

Corporate Finance

This module offers you a standard introduction of the field of corporate finance at postgraduate level. You consider the classical areas of Modigliani-Miller irrelevance, Taxes and capital structure, Trade-off theory and Pecking order theory of capital structure, before exploring the more modern areas, which are essentially based on contract theory.

View Corporate Finance on our Module Directory

Derivative Securities

Master the pricing of financial derivatives and their use for hedging financial risks. You study the basics of futures and options, analyse the Black-Scholes and binomial option pricing models, and consider various numerical techniques for pricing financial derivatives. Futures and options are then utilised in the context of hedging financial risks, and you are introduced to the concept of volatility trading and the treatment of volatility as an asset class.

View Derivative Securities on our Module Directory

Portfolio Management

Understand the process of portfolio management. You cover the main concepts such as efficient diversification, managing risk exposures, and the valuation of financial assets that are at the core of managing investment portfolios, and pay special attention to the practicalities of the implementation of these concepts.

View Portfolio Management on our Module Directory

Financial Modelling

Consider the use of modern econometric techniques in the analysis of financial time series. You cover multivariate models for stationary and non-stationary processes, such as Vector Autoregressive models, consider appropriate models for volatility, and study Markov processes and simulation methods used for financial modelling.

View Financial Modelling on our Module Directory

Risk Management

The recent financial crisis and credit crunch have demonstrated that risk management was too narrowly defined. In this course you examine the Value at Risk (VAR) measure of financial risk developed in the 1990s, before discussing the new post-crisis Regulatory environment.

View Risk Management on our Module Directory

Data Analysis: Cross Sectional, Panel and Qualitative Data Methods
Modern Banking

Explore the basics of the structure and environment of banking, and selected aspects of the applied economics of the modern banking firm. You study structure-conduct-performance, competition, bank efficiency, regulation, international banking and bank failures and crises.

View Modern Banking on our Module Directory

Bank Strategy and Risk

Analyse the key strategic developments in banking and the main aspects of risk management in modern banks. You are introduced to the concept of shareholder value in banking, the main banking strategies to create shareholder value, the key risks in banking, and the most important tools required to manage bank risks.

View Bank Strategy and Risk on our Module Directory

Introduction to Financial Market Analysis

The module introduces students to financial markets as well as providing a detailed introduction to the quantitative methods that are a pre-requisite to other CCFEA modules. Students will be introduced to financial markets such as equities, bonds, interest rates, forwards, futures and foreign exchange. Applications of calculus and statistical methods to finance are also presented.

View Introduction to Financial Market Analysis on our Module Directory

Computational Models in Economics and Finance

The modules introduces students to computational thinking in economics and finance by looking at different relevant models and theories, such as agent-based modelling and game theory. Students will also be introduced to various applications, such as financial forecasting, automated bargaining and mechanism design.

View Computational Models in Economics and Finance on our Module Directory

Economics of Financial Markets

Study the concepts of risk and return in equity markets, both in the context of asset pricing, and in the management of equity portfolios. You will start by focusing on the analysis of the stylised facts of asset returns, and will then review the theoretical foundations of modern finance, covering expected utility theory and risk aversion.

View Economics of Financial Markets on our Module Directory

Behavioural Economics I: Individual Decision Making

How do individuals make decisions? When does classic economic theory not predict empirically observed behaviour? And how do you then use behavioural economics to reconcile your empirical findings with theoretical models? Learn about empirical and theoretical research in behavioural economics that can be used to explain individual decision making.

View Behavioural Economics I: Individual Decision Making on our Module Directory

Computational Agent-Based Macro-Economics, Financial Markets and Policy Design

What challenges do macro-economists face since the 2007 financial crisis? Acquire the cutting-edge tools to address issues in macro-economic and financial markets. Learn to contrast multi-agent macro-modeling tools with more standard ones, and how to contribute to regulatory and policy design issues that have arisen since the financial crisis.

View Computational Agent-Based Macro-Economics, Financial Markets and Policy Design on our Module Directory

Applications of Data Analysis

What are the issues regarding different types of panel datasets? Or problems with survey methodology? Understand longitudinal data analysis by using micro-econometric techniques and critically examine survey methodology issues, like response rate and sampling frames. Apply panel data methods to study labour markets, focusing on marriage, unemployment and wages.

View Applications of Data Analysis on our Module Directory

Teaching

  • Lectures introduce you to cutting-edge computational and evolutionary methods to analyse and simulate markets and to design real-time trading and risk management systems.
  • Lab sessions allow you to gain experience in implementing quantitative methods in finance and economics, modelling and developing machine learning algorithms as well as optimisation algorithms in financial settings.
  • Industry expert lectures give you a unique opportunity to appreciate the latest developments and issues faced by leading practitioners in the areas of quantitative finance and risk management.

Assessment

You will be assessed throughout the course by means of written exams and coursework, including the dissertation towards the end of the course. You will also be asked to give presentations on your own work.

Dissertation

In order to complete your dissertation, you will consider and address a pertinent question in the field of financial technology. You’ll make use of the skills you’ve acquired during the course in order to tackle an important topic of practical importance. The dissertation is aimed to be of publishable quality.

Fees and funding

Home/UK fee

£9,980

International fee

£19,380

EU students commencing their course in the 2021-22 academic year will be liable for the International fee.

Fees will increase for each academic year of study.

What's next

Open Days

We hold Open Days for all our applicants throughout the year. Our Colchester Campus events are a great way to find out more about studying at Essex, and give you the chance to:

  • tour our campus and accommodation
  • find out answers to your questions about our courses, student finance, graduate employability, student support and more
  • meet our students and staff

If the dates of our organised events aren’t suitable for you, feel free to get in touch by emailing tours@essex.ac.uk and we’ll arrange an individual campus tour for you.

Applying

You can apply for this postgraduate course online. Before you apply, please check our information about necessary documents that we’ll ask you to provide as part of your application.

We aim to respond to applications within two weeks. If we are able to offer you a place, you will be contacted via email.

For information on our deadline to apply for this course, please see our ‘how to apply’ information.

Colchester Campus

Visit Colchester Campus

Home to 15,000 students from more than 130 countries, our Colchester Campus is the largest of our three sites, making us one of the most internationally diverse campuses on the planet - we like to think of ourselves as the world in one place.

The Campus is set within 200 acres of beautiful parkland, located two miles from the historic town centre of Colchester – England's oldest recorded town. Our Colchester Campus is also easily reached from London and Stansted Airport in under one hour.

 

Virtual tours

If you live too far away to come to Essex (or have a busy lifestyle), no problem. Our 360 degree virtual tour allows you to explore the Colchester Campus from the comfort of your home. Check out our accommodation options, facilities and social spaces.

Exhibitions

Our staff travel the world to speak to people about the courses on offer at Essex. Take a look at our list of exhibition dates to see if we’ll be near you in the future.

At Essex we pride ourselves on being a welcoming and inclusive student community. We offer a wide range of support to individuals and groups of student members who may have specific requirements, interests or responsibilities.


Find out more

The University makes every effort to ensure that this information on its programme specification is accurate and up-to-date. Exceptionally it can be necessary to make changes, for example to courses, facilities or fees. Examples of such reasons might include, but are not limited to: strikes, other industrial action, staff illness, severe weather, fire, civil commotion, riot, invasion, terrorist attack or threat of terrorist attack (whether declared or not), natural disaster, restrictions imposed by government or public authorities, epidemic or pandemic disease, failure of public utilities or transport systems or the withdrawal/reduction of funding. Changes to courses may for example consist of variations to the content and method of delivery of programmes, courses and other services, to discontinue programmes, courses and other services and to merge or combine programmes or courses. The University will endeavour to keep such changes to a minimum, and will also keep students informed appropriately by updating our programme specifications.

The full Procedures, Rules and Regulations of the University governing how it operates are set out in the Charter, Statutes and Ordinances and in the University Regulations, Policy and Procedures.

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