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

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 modules 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 aims to equip you with the essential technical knowledge in probability and statistics required for the Semester 2 modules which then apply these ideas. It builds on the Foundations in Probability & Statistics module and is more technical and advanced.

Specifically, the aims are to:

• introduce and provide understanding of the least squares multiple regression model, general linear model, transformations and variable selection procedures
• use R functions for multiple regression and interpretation of R output
• review the concepts of probability distributions and how to work with these
• present approaches to parameter estimation, focusing on maximum likelihood estimation, properties of estimators and hypothesis testing

10 credits

This module aims to provide students with clear frameworks they can use to understand macroeconomic events. The skills developed will enable graduates to understand how policy makers and businesses can react to, cope with, and anticipate changes in the macroeconomic environment including business cycles, inflation, commodity price shocks, changes in interest rates, taxation, government spending and government debt.

The modules aims to provide you with the ability to:

• understand macroeconomic events in an integrated world;
• communicate the implications of changes in the macroeconomic environment for business managers;
• present macroeconomic analysis in clear and concise yet informative reports

You'll learn about:

• macroeconomic policies in an open economy
• the factors guiding the choice of a given exchange rate regime
• the economics of a monetary union
• the pros and cons of globalisation, and its interactions with economic development, inequality, and political economy
• the nature and impacts of financial crises
• the interactions between exchange rate crises, banking crises and sovereign debt crises
• the long-run determinants of economic growth in an open economy

10 credits

The main aim of this module is to introduce students to theoretical microeconomics. A further aim is to provide an understanding of how micreconomics knowledge can be applied to practical problems faced by businesses that compete in both competitive and regulated markets.

You'll meet some key concepts and principles of economics, focusing particularly on consumer theory and the theory of the firm. You’ll be shown how these concepts and theories are applied to the analysis of a range of contemporary real-world issues.

You'll be learn to:

• understand the economic way of thinking
• appreciate the relevance of economics to business
• understand the operation of markets and the interaction of supply and demand
• appreciate the relationship between revenue, costs and profits
• acquire an analytical understanding of profit maximisation
• understand the economic role of marketing and advertising
• develop the ability to describe market outcomes and how these change when the market environment changes
• understand alternative theories of the firm and different pricing strategies
• understand the impact that governments and demand/supply side policies have on business decisions

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

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

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

Most people have an intuitive understanding of what risk is. The aim of this module is to formalise this understanding and develop models to quantify risk. Quantification of risk relies on many statistical methods. The emphasis in this course is the practical use of such methods.

You'll develop skills in communicating risk to risk managers as well as formulating practical risk questions that can influence policy decisions.

You can expect to learn about:

• the difference between uncertainty and variability
• quantifying uncertainty using methods such as bootstrapping and Bayesian inference
• selecting appropriate probability distributions based on given scenarios
• fitting probability distributions to data
• building risk models
• communicating your results as written reports

All theory will be implemented practically via computing sessions using the statistical software R. You'll learn to create bespoke functions in R to implement your models and use summary statistics and plots to communicate your results. 

10 credits

Risk analysis plays an important role in most business operations. This module is concerned with assessment and measurement of risk, and with the development of risk mitigation strategies that address stakeholder concerns.

Through this module we'll explore the entire process of structuring a risk problem, modelling it, supporting and communicating recommendations, both theoretically and in practice.

Risk management is linked with decision analysis in so far as we explore decision making under uncertainty and it has links with quantitative business analysis as we explore the use of statistics in understanding risk. However, the topic has some unique attributes such as risk communication and the role that experts play in risk assessment.

The module is taught in two parts. The first part is concerned with developing a solid grounding in the techniques and methods used for supporting risk management. The second part explores the application of the basic techniques through a series of case studies.

10 credits

This module provides an introduction to the theory and application of spatial statistics. No prior knowledge of spatial statistical modelling is assumed, and the class builds on techniques and theory encountered in Semester 1. It aims to develop an understanding of statistical methods for visualising and analysing different types of spatial data, and introduces Bayesian approaches for constructing more sophisticated hierarchical models. The module emphasises practical inference and application.

You'll learn to:

• understand the importance of spatial modelling in geographical contexts
• recognise different types of spatial data and methods for appropriate visualisation and analysis
• identify the presence of spatial correlation and association using spatial summary statistics including variograms and Moran’s I statistic
• understand the fundamentals of mapping, point process modelling, areal unit modelling and geostatistical modelling
• understand the modern implementations of Bayesian statistics, model fitting, and model checking, in building hierarchical spatial models

10 credits

By the end of this module, you're expected to understand the basic theories of machine learning and know how to construct a machine model for a real dataset using R. You're 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

40 credits

The aim is to develop your research, communication and time management skills. The project will also allow you to deepen your knowledge and understanding of a specific area of actuarial science and develop an ability to problem solve using a range of analytic and computational tools.

The project supervision will normally involve working closely with an academic supervisor who will help shape the project description, aims and objectives. You'll 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.