20 credits

This introductory module is aimed at graduates who have not previously studied statistics at university level. It assumes no prior knowledge of statistics and builds from simple concepts to theoretical methods that are required for application to real life data and problems. The module will provide the foundation elements of probability and statistics that are required for the more advanced classes studied later on.

This will include:

• an introduction to probability and probability rules
• random variables and probability distributions
• data visualisation and representation
• hypothesis testing and confidence intervals
• power and sample size calculations
• correlation and simple linear regression

20 credits

This module will provide an introduction to financial decision-making, and much of the relevant analysis will be developed from the standpoint of corporate finance. It will explain how a company should decide on the investments to be undertaken to meet its objectives, generally assumed to be the maximisation of its value. It will be demonstrated that this will require a rate of return on its investments in excess of the return available in the capital market on equally risky financial investments. As a result, it will be necessary to develop an understanding of the capital market risk-return relationship. This will require an appreciation of the nature of risk and how this can be managed by the development of portfolios.

Even though the focus of the module will be on corporate finance, it'll also require an appreciation of how the risk-return tradeoff is determined in the capital market.

20 credits

The aim of the module is to provide you with an understanding of the financial system and the roles and functions of financial markets and institutions. A particular emphasis is placed on understanding the roles of intermediaries such as banks and investment firms.

You'll develop an understanding of the various characteristics and roles of fixed income, equity, and foreign exchange markets. While some attention will be given to the UK financial markets, the global nature of financial markets will be widely discussed. This module aims to equip you with an awareness and understanding of financial markets and institutions in the context of the global economy. Particular emphasis will be placed on the role and contribution of the banking sector.

This module covers the reasons for, and nature of, of financial markets and institutions with a particular focus on banking, the global nature of these markets and their regulation.

20 credits

This module will equip you to:

• apply fundamental Python programming skills to engage with a range of big data technologies and tools
• explain and evaluate the use of classical statistical techniques in modern data analysis contexts
• assess the suitability of different data analysis methods and technologies for specific problem domains, considering both their capabilities and limitations
• describe and compare relational and NoSQL database models, including schema design considerations and associated trade-offs
• evaluate distributed file systems and data processing frameworks in terms of scalability, fault tolerance, and performance
• design and implement a basic data processing pipeline using appropriate tools and technologies

10 credits

This module aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.

You will learn:

• how stochastic models arise
• the concept of a financial options
• how the Black-Scholes equation arises
• how to perform computer simulations based on mathematical models

10 credits

The aim of the module is to introduce you to the rapidly evolving area of behavioural finance and provide you with an understanding of the main ideas of behavioural finance. A particular emphasis is placed on understanding the roles of non-rational actions and the development of new financial models that incorporate these ideas.

You'll engage with up to date research and develop a critical view of existing and new finance theories and models. You'll develop an understanding of behavioural finance and an appreciation of its possible implications and applications.

This module provides you with an understanding of the main flaws of 'traditional' finance theory from a behavioural finance viewpoint. It will allow you to develop the ability to discuss issues arising from violations of the rationality assumption and will enable you to evaluate new theoretical models based on research in psychology.

The course will allow you to appreciate the role of new developments in finance and their possible implications for established views of the functioning of financial markets.

10 credits

While fixed income securities (bonds) have been traded for a far longer time than equities it is only in more recent times that the trading volume of these instruments has exceeded that of equities in many of the economies with highly developed capital markets.  The bond markets for the last 25 years or so have been characterised by rapid innovation and the range of bonds now being traded is quite diverse. Any graduate of a MSc programme in Finance is expected to be familiar with the nature of valuation of bonds.  This module will provide you with the opportunity to analyse bonds and the markets in which they are traded in depth.

You will learn to:

• deal with problems that require forecasting outcomes in the context of incomplete information and uncertainty
• analyse the differences in interest rates on bonds of different maturities
• structure bond portfolio to protect against interest rate risk

10 credits

This module focuses on equity valuation. The aim is to equip you with the knowledge and tools required for analysing the financial performance of firms and measuring their value. You will be skilled in reviewing financial statements, estimating and assessing financial ratios and relevant accounting and economic data, and use this data for making forecasts and perform equity valuations. In addition, the module will cover the theoretical background for each valuation method and their application with real life examples and case studies. Also, the module will examine the advantages and disadvantages of the main valuation models. Finally, the ultimate goal is for you to be able to perform and deliver and equity analysis report for any publicly listed firm.

You will develop:

• a good understanding of the nature of equity investments and their historical returns and risks
• a good understanding of the theory that relates the expected return to the risk exposure of the investments
• the ability to forecast future performance, a good understanding of the various models put forward to assess the value of equity
• understanding of some of the most important quantitative techniques and well-known profitable strategies in equity markets
• the ability to use current and historical information to estimate the value of private or publicly listed firms

10 Credits

The aim of this module is to introduce fundamentals of the model portfolio theory and specifically to examine the Markowitz (1952) approach to optimal portfolio selection. The module explores issues relating to optimal portfolio choice and issues in passive and active fund management through the lens of the nature of variance, covariance, risk and return. The purpose of this module is to introduce you not only to the basic models but also more sophisticated, practical models such as the Black-Litterman model. It also focuses on asset allocation and performance evaluation. The module introduces practical applications and an extension of basic theory.

Learning outcomes include: 

• understanding the practical applications of Modern Portfolio Theory
• being able to use Excel in the areas covered by the module that are also applicable to other areas of finance
• using analytical skills in interpreting empirical findings
• learning to use Bloomberg and other databases for construction of portfolios

20 Credits

The aim of module is to provide a strong grounding in derivatives that may be used to manage the financial risks faced by individuals, financial institutions and business corporations. It places an emphasis on corporate treasury management and the role of derivatives in managing treasury risk.

In the first part of the module the focus is on the basic principles of derivatives. In particular, we examine futures and forward contracts, options, swaps and credit derivatives, and how these may be used for speculation, hedging, and arbitrage purposes. The emphasis is on understanding the pricing of these derivatives and the strategies devised to hedge long and short positions in underlying assets such as equities, bonds, and interest rates. The role of derivatives in the global financial market is also covered, including a discussion of the difficulties that can arise due to the regulatory framework of derivatives and the (partial) lack of regulation of derivatives.

The aim of the second part of the module is to provide a rigorous introduction to the activities of treasury managers and the use of financial derivatives. We develop the treasury aspect of foreign exchange management into the broader area of multinational finance and consider the basic operation of the foreign exchange market. Emphasis is placed on practical techniques and the solution of problems, though not to the exclusion of theory. It will develop understanding of international finance and capital markets, foreign exchange risk management and derivatives. Derivatives, treasury management and risk management are important growth areas in finance. 

This module will introduce these topics and will provide a good basis for studying for professional examinations in the area. The module provides access to a derivatives trading simulation platform and will have a number of connections to industry and the treasury profession. This module provides an introduction to the role of a corporate treasurer in a multinational company in the management of risk in an international environment, using a range of financial products including derivatives.

10 credits

You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.

You will learn:

• to analyse various financial time series data
• to undertake statistical analysis of financial risk
• to use R for econometric modelling of real financial data
• to use time series models to do forecasting

10 credits

By the end of this module, you are expected to understand the basic theories of machine learning and know how to construct a machine model for a real dataset using R. You are expected to understand ethical issues regarding data processing and management.

You will learn to: 

• clean data using RStudio and the tidyverse
• understand missing data and the role it plays
• understand ethical issues regarding data processing and management
• carry out single value imputation
• carry out multiple imputed chained equations in R
• understand and implement artificial neural networks 
• understand and implement support vector machines
• understand and implement tree based classification and regression techniques 
• understand and implement ensemble methods

20 credits

This module aims to provide conceptual and practical understanding of data modelling, database design and database technology. It also aims to give practical experience of developing web-based applications that integrate database server interaction.

You will gain:

• knowledge of the process of designing a database system, starting from an informal specification
• skill in formulating database queries using SQL
• an appreciation of the facilities and services which should be provided by a fully featured database management system
• knowledge of commonly occurring data models
• experience of using a relational database management system in a client-server environment
• knowledge of potential future developments in database technology
• understanding of the typical topology of internet applications
• ability to develop simple internet-based applications that make use of: server-side technologies and back-end technologies for storing server-side data

20 credits

The aim of this module is to equip you with a sound understanding of the principles of machine learning and a range of popular approaches, along with the knowledge of how and when to apply the techniques. The class balances a solid theoretical knowledge of the techniques with practical application via Python (and associated libraries). 

On completion of this module you will:

• understand the aims and fundamental principles of machine learning
• understand a range of the essential core algorithms and approaches to machine learning
• be able to apply the algorithms covered and interpret the outcomes
• understand the applicability of the algorithms to different types of data and problems along with their strengths and limitations
• understand the limitations of basic approaches to deep learning and the need for advanced strategies
• understand and apply a range of the advanced algorithms and approaches to deep learning and machine learning using artificial neural networks and interpret the outcomes

20 credits

This class aims to provide an overview of the application of evolutionary computation techniques – those which mimic natural evolutionary processes (genetic algorithms, genetic programming and neural networks in particular) – to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.

This module will help you to: 

• understand the benefits and opportunities for evolutionary computing in the context of financial applications
• understand the principles of evolutionary computation: genetic programming and genetic algorithms in particular, and also neural networks (particularly those configurations most suited to time series data)
• understand how the computational approaches covered in the class may be applied to financial problem solving and understand their limitations
• develop and evaluate practical solutions to finance-based problems

40 credits

The aim is to develop your  the research, communication and time management skills.  The project will also allow you to deepen their knowledge and understanding of a specific area of quantitative finance and develop an ability to problem solve using a range of analytic and computational tools.  The project supervision will normally involve you working closely with an academic supervisor who will help to shape the project description, aims and objectives. You will meet regularly with the supervisor on a one to one basis. Where an appropriate industrial project can be established within the finance sector, then the project will involve close liaison with the industrial partner, may be based at the industrial partner’s site, and will be jointly supervised by the industrial and academic supervisors.

The assessment will consist of both a written report and a presentation. For industrial projects there is also an assessed project plan.