20 credits

The course and thus this introductory module is aimed at graduates who have not previously studied statistics at university level. 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 distributions
• introductory hypothesis testing
• non-parametric hypothesis testing
• linear regression
• introductory power and sample size calculations

20 credits

Teaching: 33 hours of lectures supplemented by 10 one-hour workshops
Assessment: class test (30%) and final examination (70%)

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'll 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
Teaching: 43 hours, which is split between lectures and workshops
Assessment: class test (30%) and final examination (70%)

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

In this module you will learn to:

• understand the fundamentals of Python to enable the use of various big data technologies
• understand how classical statistical techniques are applied in modern data analysis
• understand the potential application of data analysis tools for various problems and appreciate their limitations
• be familiar with a number of different cloud NoSQL systems and their design and implementation, showing how they can achieve efficiency and scalability while also addressing
• design trade-offs and their impacts
• be familiar with the Map-Reduce programming paradigm

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.

Topics covered will include:

• how stochastic models arise
• financial options
• the Black-Scholes equation
• simulation of financial mathematical models

10 credits

The aim of the module is to 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. It aims to introduce you to the rapidly evolving area of behavioural finance. You'll develop an understanding of behavioural finance and an appreciation of its possible implications and applications.

This module introduces you to behavioural finance and 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.

Find out more in the module outline for Behavioural Finance.

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.

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.

10 Credits
Teaching: 15 hours of lectures
Assessment: assignment (30%) and final examination (70%)

The module aims 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 module introduces practical applications and an extension of basic theory.

20 Credits

Teaching: 44 hours, which is split between lectures and workshops

Assessment: two class tests (15% each) and final examination (70%)

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.

This module introduces you to these topics and will provide a good basis studying for professional examinations in the area. It 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

The module will introduce you to a number of diverse topics in network science and its applications to financial problems. It covers the basics of network theory and network dynamical modelling, how network analysis may be applied to real-world problems, and advanced concepts such as geographical embedding, consensus dynamics, diffusion and epidemics and control. You will be able to implement network models numerically, using tools such as R or Python, model finance networks and be able to solve real-world problems in finance using networks.

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.

Topics covered will include:

• basic statistics in finance
• Time Series modelling
• financial volatility modelling
• 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.

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.

20 credits

The aim of this module is to equip students 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. On completion of this module you will:

• understand the aims and fundamental principles of machine learning
• understand a range of the key 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

20 credits

This module will help you to understand (a) nature of evolutionary computing (b) suitability of evolutionary computing for financial applications. It will provide practical experience in developing and operating evolutionary computing approaches for financial applications.

40 credits

The assessment will consist of both a written report and a mini-viva.