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

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 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

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 passive and active fund management through the lens of the nature of variance, covariance, risk and return. The class introduces practical applications and an extension of basic theory.

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.

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

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

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.