MSc Quantitative Finance

The educational intention of this class is to bring all of the students up to the same level of mathematical and statistical training so that they can then undertake the financial theory classes in semester 2.

This class provides an introduction to the basic principles of financial decision taking and the theory of finance. It will develop the basic principles of valuation, the nature of risk and uncertainty, and the relationship between risk and returns. While the analysis will be developed in the context of corporate finance, looking at companies’ decisions on the investments in assets and how these investments will be funded and the nature of the markets in which they take place, it will also consider the principles underlying financial reasoning that can be applied on a more general basis.

Find out more in the class outline for Principles of Finance.

The aim of the class 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 class 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 class 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.

Find out more in the course outline for International Financial Markets & Banking.

In this class 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

The aim of the class 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 class 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 class outline for Behavioural Finance.

The aim of this class is to develop an appreciation of the investment characteristics of different types of securities, particularly bonds and shares, and to develop an understanding of how such securities are valued. This class will build on the analysis developed in the first semester class, Principles of Finance. It will consider the determination of interest rates, the valuation of bonds, the management of bond portfolios, and the valuation of equities.

Find out more in the class outline for Security Analysis.

The aim of this class is to examine the Markowitz (1952) approach to optimal portfolio selection. The class explores issues relating to optimal portfolio choice and issues in practical fund management.

Find out more in the class outline for Portfolio Theory & Management.

The aim of this class are 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.

Find out more in the class outline for Derivatives & Treasury Management.

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.

The aim of this class 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 class students 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

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.

The class 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'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.

This module will introduce you to a number of diverse topics in game theory and its applications to financial problems as well as giving a sound background on network theory at both theoretical and applied level.