Course Overview

Highlights

  • 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

Course Overview

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

If you have any questions, please contact us by email at admissions@icmacentre.ac.uk or by phone on +44 (0)118 378 6497

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 

Fees & funding

Full-time course fees (2019/20)

UK & EU students: £16,500 / International students: £23,750

Please note there is a one-off £30 application fee (one charge regardless of how many courses you apply for). You can pay this by credit/debit card at this link (please contact us if you require details of alternative payment methods).

Living expenses are in addition to the above fees. Overseas full-time participants can expect to spend approximately £9,400 on additional living expenses during the course of their studies. Home/EU full-time participants can expect to spend approximately £8,000 on additional living expenses during the course of their studies.

Please note that a non-refundable deposit is payable when confirming your acceptance of an offer of a place. This is part of your tuition fee and will be deducted from the total amount upon enrolment.

Scholarships

We offer a number of scholarships for EU/UK and international applicants with excellent academic performance as well as for international applicants with work experience, covering from £5,000 to 60% of the cost of the programme.

For a full list of scholarships, visit our scholarships webpage.

UK/European Union Postgraduate Loans

Loans of up to £10,000 are available to eligible students studying for postgraduate Master’s courses from the 2019-2020 Academic year. To be eligible, students will need to be English domiciled.  EU students, and individuals falling within certain specified categories, may also be eligible.

Full details of the loan, including how to apply, are due to be published this year.  Read more at Introduction of loans for postgraduate students and Government response to the Consultation on Support for Postgraduate Study.

Careers & professional accreditation

Careers

Graduates of the ICMA Centre have an enviable record of attainment when it comes to gaining employment in the financial services industry. The global investment banking and securities markets attract the very best applicants and competition for entry-level positions remains intense. Despite the volatile nature of the financial markets, demand for well-qualified recruits remains high. We expect many of our graduates to enter the industry at either the Analyst level, i.e. as graduate trainees, or as Associates who tend to have several years of relevant professional experience.

Our graduates leave us equipped with knowledge and transferable skills that are also prized by employers outside of the traditional banking and finance sectors. Many of our graduates are currently enjoying successful careers with government agencies and regulatory organisations throughout the world. Others are working with specialist IT firms, multinational companies and global consulting organisations.

Increasingly, students join us to obtain specific technical and financial skills to enable them to join small financial boutiques, including venture capitalists, proprietary trading firms and hedge funds. Others are working in the area of e-commerce, especially small, entrepreneurial firms that seek to exploit the opportunities for internet-based securities sales and trading.

 

Learning options

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

Entry Requirements

  • Undergraduate Degree – Minimum 2:1 or the equivalent from an overseas institution*
  • Degree Discipline – Any degree discipline, but must have a satisfactory level of numeracy and have basic knowledge of Mathematics and Statistics.
  • GMAT – We may ask you to submit a GMAT score if we think it appropriate in your individual case. For example, if you have been out of education for more than a few years or have little evidence of any numerical ability. For information on the GMAT and the location of test centres worldwide, please visit www.mba.com

* Please note that due to increasing competition for places on our Masters programmes our entry requirements may change.

We operate a rolling admissions system and you are therefore advised to apply early in order to be sure of your place on our programmes. We experience high levels of demand, and it is possible we might have to close applications to some programmes once places are filled.

English requirements

If English is not your first language, you may be required to take one of the following:

  • TOEFL (Test of English as a foreign language): Overall score of 100 with no less than 20 in Listening, Writing and Reading and 21 in Speaking
  • IELTS (British Council International English Language Test): Score of 6.5 overall with no component less than 6 when attending the 6-week pre-sessional English course offered by the University of Reading. Entry to this pre-sessional course with a score of 6.5 fulfils your English language requirement.

Please note that students not attending a Pre-Sessional course will need to pass IELTS with an overall score of 7 and no component less than 6.0. For more options please see the International Study and Language Website or email a member of the Postgraduate Admissions team.

Tier 4 visa pilot scheme

Find out more about the tier 4 visa pilot scheme on the University of Reading website.

Compulsory Modules

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.
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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
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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.
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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).
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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.
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Compulsory modules

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 patters in the data and its popular finance applications. For instance, we will look at stock price forecasting, company default prediction and market sentiment analysis.

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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.

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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.
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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.

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Optional modules

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

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.
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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.
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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.
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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.
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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.
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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.
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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.
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The module provides an introduction to the basic techniques employed in Financial Engineering. Students will understand how these methods can be applied to design securities with desired payoff characteristics. They will be able to evaluate complex security structures by means of reverse engineering and be aware of possible problems when these methods are applied to real world situations.
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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.
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Optional modules only open to students with computer science or engineering background

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.
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The massively increased uptake of computing in the last decade, with devices at all scales of operation, has driven the development of large-scale distributed systems capable of meeting the demands placed on service providers.  This module provides an insight into the data-centric and service-centric techniques deployed in very large scale robust distributed systems such as those that support the world's largest and most popular websites. The module covers Cloud computing (IaaS, PaaS, SaaS), techniques for processing big data (Map/Reduce), large-scale systems architectures, RESTful systems and an architectural analysis of the Web as a whole, distributed systems utilising message passing (MPI and Erlang), methods for producing robustness in distributed applications, and an overview of the hardware and software technologies underpinning supercomputing. The module also addresses the business confidentiality, socio-legal, security and privacy issues involved in operating and using cloud services and in this context highlights the need to consider the security and privacy requirements and respective risks associated with the various types of cloud services and the design approaches adopted in the distribute systems that support such services.
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Additional optional modules

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.
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or

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.
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Optional modules

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

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). The placement is available only to students on the 12-month version of MSc International Securities, Investment and Banking and MSc International Shipping and Finance. 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
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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.
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