MSc Finance and Financial Technology (FinTech)

Below is a list of programme modules that will be available in 2024/25, please note that some of these modules are new for the academic year. For any other modules which are available for both this academic year and next, follow the “More Information” link to see more.

The module aims to equip students with the main principles of Investment Theory. It begins with an overview of the interaction between users and suppliers of capital and the theory behind investor behaviour towards financial risk and reward.

The module first explores the role of banks, central banks and money in the economy and how technology is reshaping the role of banks and creating new business models for banking services.

This module then provides an introduction to international financial markets and an overview of financial institutions. The main characteristics and risks of a range of financial markets are presented: equity, fixed income, foreign exchange, futures and commodity markets.

This module equips you with the quantitative tools used by market participants. The module uses a mixture of (1) lectures where the theory and concepts are introduced and (2) seminars and workshops where we apply the knowledge to practical cases.

It is an introductory applied econometrics module with an emphasis on finance.

This module introduces students to programming in finance. Programming skills are among the most desired in today’s data driven business landscape. Python and SQL languages have become an industry standard and are 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.

Below is a list of programme modules that will be available in 2024/25, please note that some of these modules are new for the academic year. For any other modules which are available for both this academic year and next, follow the “More Information” link to see more.

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.

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 industry standard machine learning and data science techniques can be applied to solve predictive modelling problems in finance. You will also learn how state of the art big data techniques can be applied in finance at the level of a business problem, a complex project and at the level of a whole organisation.

Students select 20 credits from the following;

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.

This investment portfolio management module covers key topics, techniques and discussions, including investment styles, in relation to constructing and maintaining an optimal investment portfolio. The teaching approaches all topics from academic and market, practical perspectives. More detailed aims and learning outcomes are included below.

The principal aim of the module is to familiarise students with the private equity (PE) and venture capital (VC) fund raising and investment process and with M&A deals. The purpose is to develop students’ appreciation of the practical aspects of investing by PE and VC firms, understanding the dynamics between the PE and VC investor and the entrepreneur, and also the practicalities of raising PE&VC funds from institutional investors and transacting M&A deals.

This real-world module prepares finance students for careers in risk, compliance, audit, regulation and in government / policy positions. Students explore how power, control and autonomy are moderated by regulation, how regulators can preserve or undermine the public interest, how regulators can demand and act in the interest of accountability, and how institutional, procedural and cultural safeguards can be created so the public interest is preserved.

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.

The aim of this module is for students to be able to evaluate the business value in utilising AI and data analytics and develop business technology solutions with an appreciation of AI and data analytics methods and technologies.

This module focusses on the methods and techniques of using AI and data analytics in business. Given the availability of large amounts of data in business and organisation, there is an increasing need for organisations to assess how effectively AI and data analytics can be utilised for business. In this module, students consider how organisations can benefit from AI and data analytics, and analyse business and technological requirements to create value though AI and data analytics.

Only available to students with an undergraduate degree in computer science or engineering;

The massively increased uptake of computing, with devices at all scales of operation, has driven the development of large-scale distributed systems capable of meeting the demands for handling scalable parallel data analysis and processing and supporting the execution of analytical algorithms on computer clusters such as Hadoop. This module aims to introduce the concepts and design principles for big data management and advanced network-centric computing platforms.

This module also encourages students to develop a set of professional skills, such as software development documentation and project management. Students will also be able to demonstrate their abilities in professional and effective writing to communicate data science concepts, solutions and outputs in technical reports and utilising knowledge and skills to continue learning and adapting to new data science technologies.

The aim of the module is to introduce students to current methods in artificial intelligence (AI) and machine learning (ML) covering supervised, unsupervised, reinforcement and deep learning. Students will learn how to apply these methods to real-life problems using Python programming language.

AI is a core component of computer science, aiming at developing intelligent agents that mimic human’s cognitive capability in learning, reasoning, and problem solving. As a branch of AI, ML has recently gained huge attention from technology giants (Google, Facebook, Microsoft, IBM, etc.) and achieved impressive progress in performing many 'human' tasks such as playing games, image recognition, and natural language processing. The recent wave of AI and ML development has already led to significant industrial applications such as self-driving cars and Industry 4.0. Undertaking the module will enable students to contribute to the development of future technologies.

Students will also be able to demonstrate their abilities in:

• formulating research problems;
• writing technical reports; and
• utilising knowledge and skills to continue learning and adapting to new data science technologies.

Students take 20 credits from the following:

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.

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.

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 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.

Industry participants estimate that 70-80% of equity trades are executed through computers. Market-makers in equity, fixed income and currency markets use algorithms to automatically adjust their quotes.

This module aims to provide students with an understanding of financial decision making in the context of alternative investments and to introduce the key principles and techniques of financial engineering used in structured product design.

Sustainability is becoming a priority not only in government policy but also in business strategy. The role of the private sector in achieving sustainable production and consumption models is key to preserve fast depleting natural resources, improve social justice and reduce inequalities, address climate change and loss of biodiversity. This module explores how sustainability can be incorporated in business practices and balanced with corporate growth objectives.