Smart city 3

Master's in Finance and Financial Technology (FinTech)

The aim of the programme is to prepare graduates for a career in the rapidly expanding FinTech sector.

At a glance

  • Specialise in one of the most exciting areas of Finance
  • Gain cutting edge knowledge on finance applications of blockchain technology, digital currencies, Big Data and machine learning techniques
  • Develop programming skills and put them into practice to solve real world problems
  • Experience world-class student facilities and a practice-centred delivery approach
  • Choose from over 10 elective modules to gain specialisation in a wide array of finance topics
  • Explore how to regulate new financial products and services to prevent fraud and ensure personal data protection
  • Work Placement & Project module available for students studying the 12-month course
Level
Master's
Award
MSc
Duration
9 months / 12 months
Attendance
Full-time
Locations
Whiteknights campus (Reading)
Programme Directors
Simone Varotto small
Dr Simone Varotto
Alfonso Dufour
Dr Alfonso Dufour

The advances in financial technology have brought about transformational changes in many branches of the finance industry. The introduction of blockchain technology and digital currencies have created a revolution in payment systems and generated widespread disintermediation via peer-to-peer lending and crowdfunding for firms, and online stockbroking and robo-advisory services for individual investors. Many of these innovations are based on the processing of large amounts of data (or “Big Data”) via machine learning algorithms.

Through this course you will specialise in digital banking and payment systems, Python programming, finance applications of machine learning algorithms. You will also gain an in-depth understanding of big data in finance and insurance, along with emerging technologies including blockchain, cryptocurrencies. Through trading simulations, you will learn about securities, futures and options and algorithmic trading. You will also explore FinTech regulation and the challenges posed by data protection and cyber security.

Join our webinar 'Why Study MSc Finance and Financial Technology at Reading?' on Thursday 12th December 2019 to find out more about the programme structure, modules and facilities within the ICMA Centre from Dr Simone Varotto, Programme Director. See here for more details and to register for the webinar.

Part 1 Modules

Compulsory modules Credits

In this module, you will learn how banking is transitioning toward digital forms of financial intermediation and how payment systems are evolving with the deployment of new technologies. We will explore the role of banks, central banks and money in the economy, how technology is reshaping the role of banks and how money is becoming decentralised. New business models for banking services and their regulatory implications will be discussed. Examples and case studies will be used to illustrate the key aspects of digital banking.

Teaching Staff
Brian Scott Quinn
Emeritus Professor Brian Scott-Quinn

10

The module provides an economic framework for the understanding of global financial markets, organised exchanges, market players and the importance of liquidity and price efficiency. Participants will gain an understanding of the international stock and bond markets, ‘repo’ markets (for borrowing/ lending on a secured basis); an introduction to foreign exchange and money markets, and to futures markets; finally specific markets for commodity and energy are studied in more detail.

Outline: General introduction to world financial markets, Liquidity, the distinction between exchange versus OTC markets and the role of intermediaries in their various forms, Primary and secondary markets, Market players and effect on liquidity and price efficiency, Market microstructure theory, Fixed income markets, Foreign exchange market, Futures and option markets, Commodity and energy markets

Teaching Staff
Alfonso Dufour
Dr Alfonso Dufour

10

In this module you will be introduced to Python, a programming language that has become an industry standard and is widely used to produce innovative financial products and services. Common applications include big data analysis and manipulation, algorithmic trading, portfolio analysis, and machine learning algorithms. Students who complete this course will be able to write programming functions in Python, process data files including reading, modifying and writing data to external files. Specifically, students will be able to read and write to Excel, CSV and Text files, connect to databases, obtain and process data from the Web, as well as use Python for Finance and Econometrics applications including developing event based trading strategies and back testing with historical data. By the end of the module students are expected to produce a simple Python application to solve real world financial problems. No prior programming experience is required.

20

This module provides an overview of the key building blocks in modern finance theory and introduces techniques for analysing and valuing different classes of risky assets such as equities and derivatives contracts. It also develops ways of optimally selecting portfolios of such assets and develops models of how these portfolios can be priced in financial markets. The techniques introduced in this module are widely applied in other elements of the programme. The module includes simulated trading sessions in our state of the art dealing rooms, where participants are introduced to real world pricing and trading strategies (INVEST sessions).

Teaching Staff
Nadia Kappou
Dr Konstantina Kappou

20

The module covers the building blocks of econometrics and analytical techniques used in finance. Via case studies and computer modelling exercises, students learn how to apply these techniques to real data. Emphasis is placed on practical applications of the techniques in the global financial markets.

Teaching Staff
Chardin medium 1
Dr Chardin Wese Simen

20

This module provides an introduction to commercial and investment banks and an overview of the main characteristics and risks of a range of international financial markets: equity, fixed income, foreign exchange, futures and commodity markets. An interactive workshop is used to simulate trading and compare alternative market structures.

Teaching Staff
Brian Scott Quinn
Emeritus Professor Brian Scott-Quinn

0

Part 2 Modules

Compulsory modules Credits

In this module you will learn how machine learning techniques borrowed from artificial intelligence can be used to solve common big data problems in finance. We will first explore the issues related to the collection, organisation and visualisation of large sets of structured and unstructured data. With the use of Python we then explore ways in which a computer can be trained to recognise patterns in the data and its popular finance applications.

20

Blockchain technology is rapidly changing the financial industry and beyond. Countless applications are being explored in payments, insurance, lending, fund raising, settlement of securities transactions and contract execution. In this course we will explore what a blockchain is and how you can create one with simple Python codes. Cryptocurrencies, one of the most popular uses of blockchain, are explored in detail. The module will present technical concepts at a high level suitable for students whose main interest is finance and the financial applications of blockchain.

Teaching Staff
Andrew U
Dr Andrew Urquhart

20

The availability of unprecedented amounts of data made available by ever increasing computing power and data storage capacity, widespread use of Internet of Things devices, and powerful communications networks, have led to major changes in the insurance industry. The services it provides, how they are offered to its customers and at what price, and insurance risk analysis are undergoing rapid innovations. In this module you will learn how the evolution of the sector is unfolding and gain insights to the challenges and opportunities that it is generating.

10

Regulation is often seen as an obstacle to innovation, or innovation as a way of avoiding regulations. However, successful Fintech products and services must comply with the letter and spirit of financial regulations, and effective regulation of Fintech is essential to protect wider public interests. This module will place the relationship between financial innovation and regulation at the centre of students' understanding of Fintech.

Teaching Staff
Tony Moore
Dr Tony Moore

10

Students on the 9-month (12-month) programme can select 40 (20) credits from the following modules:

Optional modules Credits

Building on the material introduced in Quantitative Methods for Finance, this module covers a number of more advanced techniques that are relevant for financial applications, and in particular for modelling and forecasting financial time series. These include an introduction to maximum likelihood estimation and two-stage least squares, models of volatility, simulation techniques, and multivariate models. Case studies from the academic finance literature are employed to demonstrate potential uses of each approach. Extensive use is also made of financial econometrics software to demonstrate how the techniques are applied in practice.

Teaching Staff
Michael Clements
Professor Michael Clements

20

The module consists of theoretical foundation with practical focus for the topic of IT project management. The assessment uses a real life case scenario asking the students to apply theories and techniques learned from this module.

20

The main aim of the module is to provide a rigorous grounding of the theory and practice of corporate finance and more specifically the long-term financial management decisions of the firm pertaining to investments, financing and payout and how they affect its value. It deals with how corporations are governed, their financing structures, payout policies, the processes involved in the issuance of public and private equity, as well growing through inorganic investment (mergers and acquisitions). The module also extensively deals with advanced financial analysis and enterprise valuation methods employed by financial advisors/investment banks as part of advising corporations. Students on this module take part in a bespoke investment banking pitch-book simulation challenge whereby they have to work with their team and produce a real life pitch-book including financial analysis on a real transaction as part of assessing the company’s strategic alternatives.

Teaching Staff
George Alexandridis
Professor George Alexandridis

20

The module aims to build on the techniques for portfolio selection that have been introduced in the Securities, Futures and Options module. The module examines the issues involved in understanding the investment market, constructing a competitive investment portfolio (of an active, passive or smart beta style), evaluating the performance of that portfolio, and adjusting its composition through time. It will also consider issues revolving around the management of risk. The compulsory, practical project of the course will provide students with hands-on experience in constructing and managing a realistic investment portfolio.

20

The evolution of algorithmic trading, the proliferation of alternative trading platforms for trading the same security and the development of new products and assets with limited liquidity have contributed to raising the awareness of academics and traders on the importance of understanding and properly managing liquidity and execution risks. The objective of this course is to give students an introduction to the concepts of market and asset liquidity, trade execution risk and an overview of the methods for managing these types of liquidity risk. This module will not discuss about funding liquidity and managing liquidity in a bank. The issues discussed in this course are important when developing trading strategies, valuing portfolios, liquidating large positions and transitioning assets to new investments.

Teaching Staff
Alfonso Dufour
Dr Alfonso Dufour

20

The objective of the module is to develop students’ appreciation of the practical aspects of raising venture capital finance for a private company, working with the venture capital investor in growing the business and achieving a successful exit. The focus of the module is on venture capital and high-growth ventures from the viewpoints of both the entrepreneur or management team and of the investing institution (general partner), although private equity as a whole is covered as well as the relationship between the private equity or venture capital firm and its own investors (limited partner institutions). Extensive use will be made of case studies and a business plan project in addition to guest lectures from invited experienced practitioners.

Teaching Staff
Keith Arundale
Dr Keith Arundale

10

In order to raise capital from financiers or secure funding via government grants it is always necessary to produce a robust financial plan. This plan will then be used as a yardstick by all parties to monitor the performance of the business. Typically a detailed twelve month plan is required in addition to a summary level five year plan. Typically these plans will include comprehensive sections on revenue forecasts, profit and loss, cash flows, balance sheet, capex, financing and a commentary with further detail to help explain the figures. The aim of the module is to introduce students to the practicalities of developing and utilising financial models with a view to achieving the overall strategic objectives of an organisation. Students will also gain a thorough understanding of the interaction between the separate statements within a financial model.

Teaching Staff
Linda Arch
Dr Linda Arch

10

This module aims to provide students with a detailed knowledge of the commodity derivatives markets. It examines the aspects of pricing and trading physical derivatives, with emphasis on the energy and shipping (freight) sectors. The course is designed using real-life trading examples, stimulating students, who wish to follow a sales and trading career, to approach derivatives pricing from first principles.

Teaching Staff
Nadia Kappou
Dr Konstantina Kappou

10

The aim of the research project is to allow students to define and execute a piece of research in finance on a topic of their choice, with direction from an academic supervisor and with assistance from a doctoral student support supervisor.

Teaching Staff
Charles Sutcliffe
Professor Charles Sutcliffe

20

Small and medium-sized enterprises (SMEs) represent a large share of our economies and a leading force in economic development. This module focuses on such enterprises and explores the distinctive features of management in the SME environment. An important component of this module is the preparation of a Business Plan.

Teaching Staff
Dr Yipeng Liu

20

Additional optional modules (choose 1 only)

Optional modules Credits

This module focusses on the methods and techniques of using Big Data in business. Given the availability of large amount of data in business and organisation, there is an increasing need for organisations to assess how effectively Big Data can be utilised for business. In this module, students consider how organisations can benefit from Big Data, and analyse business and technological requirements to create value though Big Data and business analytics. Students will also explore recent developments in technologies surrounding Big Data such as text analytics, cognitive analytics and visualisation, and assess types of tools that can be utilised, including the use of state-of-the-art analytics tools.

Teaching Staff
Prof Keiichi Nakata

20

This module is concerned with using business intelligence and data mining techniques for managerial decision making. Data mining is the process of selection, exploration and analysis of large quantities of data, in order to discover meaningful patterns and rules which in turn structure business intelligence in context. In another words, data mining converts the raw data into useful knowledge required to support decision-making.

Teaching Staff
Dr Yin Leng Tan

20

Optional modules only open to students with computer science or engineering background

Optional modules Credits

The analysis of Big Data is not just the analysis of very large data sources, even though this is part of it. Typically data comprises four aspects, Volume, Velocity, Variety, and Veracity. This view on Big Data is commonly accepted. Volume refers to the actual size of the data, here computationally well scaling methods are needed; Velocity refers to the very fast generation of data, here data stream processing methods are needed for time critical applications; Variety refers to the different types of data, possibly unstructured data such as video streams, click streams or audio files; Veracity refers to the challenge of establishing the trust of decision makers in the Knowledge extracted from Big Data Analytics techniques.

Teaching Staff
Dr Frederic Stahl

10

Part 3 Modules (12-month only)

Students on the 12-months programme should take 20 credits from the following:

Optional modules Credits

This module gives students the opportunity to pursue a work placement with an external organisation broadly related to the general sphere of their degree studies. The aim of the module is to allow participants to gain work experience in a career path of interest, develop a wide range of employability skills, build their network and enhance market awareness. The maximum duration of the placement is 3 months and it takes place during the summer vacation period (June-August). Placements should be secured by students independently. The Centre’s career development office can support students in their search and application process. Placements secured by students are subject to the approval of the module convenor. The module is assessed by a 3,000-word project based on the work experience gained.

Teaching Staff
Michael smith
Dr Michael Smith

20

This module is designed for advanced Master’s students and doctoral students. It has a very high technical content. It aims to equip the students with the foundations of theoretical asset pricing and with the relevant skills for performing empirical tests. Additionally, a few important corporate finance topics will be covered in the format of student presentations. The objective of the module is to prepare students to become independent and quality researchers.

Teaching Staff
Marcel Prokopczuk
Professor Marcel Prokopczuk

20

This module aims to provide students with an understanding of financial decision making in the context of the energy industry. The course will combine theoretical models with practical applications. It focuses on energy markets (products, companies, production and consumption), capital budgeting in energy companies, financing of energy companies, energy derivatives and trading in energy markets. A number of case studies in energy finances are utilised.

Teaching Staff
Marcel Prokopczuk
Professor Marcel Prokopczuk

20

The module is less quantitative option open to all MSc students that builds on the coverage of futures contracts from term 1. By the end of the module it is expected that students will be aware of the different ways of constructing stock market indices and the implications of these differences, how futures contracts are traded and the identity of some of the close substitutes for trading index futures, how futures can be priced using an arbitrage relationship, how futures can be used for hedging the price risk of the underlying, and the various uses that fund managers make of these instruments.

Teaching Staff
Charles Sutcliffe
Professor Charles Sutcliffe

20

Fintech brings together Finance and Technology. Successful students who study this course could find themselves working in exciting fintech start-ups, at established banks, or in related organisations such as consultancies and regulators. Depending upon the career path, you could be specialising in the technology side delivering new software and handling big data and machine learning algorithms, or working on the finance side to win business, set up deals and deliver essential financial services to clients. For students who want to stay and work locally, London is not only a global finance hub but also a global fintech hub, with a lively start-up scene and hundreds of mid-sized fintechs as well as major banks and asset managers. The ICMA Centre is a pioneer in finance education and renowned for its strong links with the financial services industry. For students who plan to work elsewhere, acquiring this masters qualification will impress potential employers.

Find out more about graduate destinations and career opportunities on our Henley Careers page

Our master's in finance courses are available only on a full-time basis with the option of studying for 9 or 12 months.

Learning options

Full-time: 9 months
Full-time: 12 months
Students will be resident and undertake full-time study in the UK. Under both, the 9 and 12-month programmes students take compulsory and/or elective modules in Part 2.
The 12 month option involves taking an elective 20 credit module between July and August, which would also mean a 20 credit reduction in the number of taught modules taken in the spring term.

Course structure

October – December:Part 1 Autumn Term
January:Part 1 Exams
January-April:Part 2 Spring Term
May – June:Part 2 Exams
June – August (12 month programme only):Part 3
August/Sep (12 month programme only):Part 3 Coursework deadlines

Speak to a current student

Contact us

For any questions of academic nature about this programme you are welcome to contact directly the Programme Directors, Dr Alfonso Dufour at a.dufour@icmacentre.ac.uk and Dr Simone Varotto at s.varotto@icmacentre.ac.uk

Admissions

If you have any questions about admissions, please don't hesitate to contact us.

Email: admissions@icmacentre.ac.uk
Telephone: +44 (0)118 378 6497