This module offers you a standard introduction of the field of corporate finance at postgraduate level. You consider the classical areas of Modigliani-Miller irrelevance, Taxes and capital structure, Trade-off theory and Pecking order theory of capital structure, before exploring the more modern areas, which are essentially based on contract theory.

Master the pricing of financial derivatives and their use for hedging financial risks. You study the basics of futures and options, analyse the Black-Scholes and binomial option pricing models, and consider various numerical techniques for pricing financial derivatives. Futures and options are then utilised in the context of hedging financial risks, and you are introduced to the concept of volatility trading and the treatment of volatility as an asset class.

Understand the process of portfolio management. You cover the main concepts such as efficient diversification, managing risk exposures, and the valuation of financial assets that are at the core of managing investment portfolios, and pay special attention to the practicalities of the implementation of these concepts.

The recent financial crisis and credit crunch have demonstrated that risk management was too narrowly defined. In this course you examine the Value at Risk (VAR) measure of financial risk developed in the 1990s, before discussing the new post-crisis Regulatory environment.

Big data - where datasets are so large they cannot be processed using traditional techniques – is useful to financial organisations. This module explores how to analyse big data and covers areas such as predictive analytics, risk modelling and corporate finance. You also learn about the application of data analytics in high frequency finance, fraud and personal finance.

This module provides you with an understanding of non-time-series data analytic approaches in finance. It covers methods for cross-sectional, panel and qualitative analysis and their applications whereby all topics are illustrated with relevant examples. Cross-sectional data are organised over individual groups (eg households, firms or countries) and have no time dimension. They may include discontinuous data (eg binary), qualitative or categorical data and are essentially non-numerical. Examples include: survey responses, textual analysis of social media or interviews. Panel data or longitudinal data are multi-dimensional data streams involving measurements over time. As such, panel data consists of researcher's observations of numerous phenomena that were collected over several time periods for the same group of units or entities. For example, a panel data set may be one that follows a given sample of individuals over time and records observations or information on each individual in the sample. The nature and advantages of panel data has led to numerous applications in finance and economics research.

Financial technology (FinTech) is rapidly changing the financial services industries. The module provides an introduction to FinTech by analysing its impact on major banking and financial activities. Specifically, the module illustrates the effect of FinTech on lending activities (P2P lending, crowdfunding) and credit evaluation tools (big data, machine learning and artificial intelligence tools), investment activities (trading and robo-advising), payments (crypto currencies and new payment tools), insurance (InsureTech). In final, the module analyses the topic of ethics and regulation of FinTech.

Explore the basics of the structure and environment of banking, and selected aspects of the applied economics of the modern banking firm. You study structure-conduct-performance, competition, bank efficiency, regulation, international banking and bank failures and crises.

Study the concepts of risk and return in equity markets, both in the context of asset pricing, and in the management of equity portfolios. You will start by focusing on the analysis of the stylised facts of asset returns, and will then review the theoretical foundations of modern finance, covering expected utility theory and risk aversion.

How do individuals make decisions? When does classic economic theory not predict empirically observed behaviour? And how do you then use behavioural economics to reconcile your empirical findings with theoretical models? Learn about empirical and theoretical research in behavioural economics that can be used to explain individual decision making.

What are the issues regarding different types of panel datasets? Or problems with survey methodology? Understand longitudinal data analysis by using micro-econometric techniques and critically examine survey methodology issues, like response rate and sampling frames. Apply panel data methods to study labour markets, focusing on marriage, unemployment and wages.