MSc Finance: Electives

This elective will develop the ideas introduced in the earlier core module Corporate Finance. Particular emphasis will be placed on linking the conceptual tools taught earlier to real business decisions through the use of case studies.

If you aspire to be a quantitative analyst in the equity derivative area, this elective is a must. It will challenge you to expand your knowledge beyond the Black-Scholes model and apply quantitative tools to the pricing of exotic options. The elective also introduces some of the more technical and theoretical aspects of option pricing.

This module provides insight into financial trading strategies from an industry practitioner’s perspective. The module covers the wide spectrum of strategies across asset classes and hedge fund styles with an emphasis on investment /arbitrage opportunity and risk management. The module also includes quantitative pricing models with backtesting in Python across different market regimes. The module aims to study trading strategies in a non-technical intuitive manner using a “first principles” approach.

This is an advanced elective in investments and portfolio management. You will discuss the key trading strategies used by hedge funds and demystify the secret world of active investing. The elective combines the latest research with real-world examples and explores several different strategies in depth, including fundamental tools for investment management, dynamic portfolio choice, equity strategies, macro strategies, yield curve logic and arbitrage strategies.

In this elective you will analyse banks’ main risks and activities on both their assets and liabilities, including off-balance sheet risks and financial globalisation, with special emphasis on the effects and implications of bank regulation and monetary policy. You will also study issues such as the determinants and consequences of financial crises and come to understand interactions between financial globalisation and banks.

This elective provides you with a broad perspective of credit risk. You will study how to assess credit risk associated with individual exposures, and discuss major literature in the field and some related applications. The elective also covers aspects of univariate or single-exposure risk and investigates the pricing of defaultable bonds and single named credit derivatives.

Fixed income securities make up a very substantial proportion of all investments and financing strategies in today’s financial markets. The need to price and hedge this array of products accurately has led to a prolific literature in the area. This elective covers the main continuous-time term structure models and valuation techniques.

This elective aims to provide students with more advanced tools of time series and econometrics than the Financial Statistics module. Applications to asset pricing and risk management are also covered.

Over the past few years, there has been an explosion of interest in the use of large datasets and new empirical techniques to make financial decisions of all kinds. In this elective we examine how the combination of large datasets, empirical techniques including machine learning, and insights from behavioural finance are helping in making more efficient financial decisions. Two areas in which progress has been especially rapid are credit analytics (predicting default in personal loans, mortgages, and firms) and asset management. This elective focuses on these specific markets, considering them from supply, demand, and regulatory perspectives. You will build empirical models to illustrate important concepts throughout the elective.

This module will empower you with an understanding of blockchain / distributed ledger technology from first principles, opportunities and challenges the innovation presents. At the end of the module, you will be equipped with the skills necessary to use blockchain technology to optimise existing processes, innovate business models and create new markets.

This module will first provide an overall review and estimates of global climate finance requirements based on a range of sources going from global estimates to national, sectoral and project levels. The module looks at different types of climate mitigation and adaptation projects to understand how they address specific climate challenges. Looking at the range of financing instruments including both traditional and innovative instruments for large and small projects. The module will include a review of financing sources, both private and public, the project cycle and complementary activities to financing such as policy advice and technical assistance.

This module introduces students to those skills by examining the entire deal making process: from the initial stage of identifying a suitable target to undertaking due diligence on it, and from the legal structuring of a transaction to how its terms are both negotiated and then documented. Best practice and common pitfalls to avoid are explored. The module is heavily focused on “how to” make a deal happen.

The objective of the module is threefold. First, to give students an introduction to current corporate governance practice. The main issues in corporate governance and stewardship that are discussed among policymakers, corporations, investors and scholars from law, finance and economics will also be covered. The topics will be discussed from an international comparative perspective. Second, to familiarize students with analytical tools used by corporate governance analysts. Third, to illustrate how practical corporate governance and stewardship challenges, like crises, mismanagement or activist shareholder interventions can be addressed and resolved.

The course will introduce some basic economic concepts and tools for analysing the interplay of conflicting interests of management, the board, different types of shareholders and other interested parties; in particular, agency theory and the economics of financial contracting. Empirical tools include event study analyses, regression discontinuity design (RDD) and metrics of firm performance (Q, returns, etc.).

Entrepreneurial Finance is designed primarily for students who plan to get involved with a new venture at some point in their career - as a founder, early employee, advisor or investor. This elective is also appropriate for students interested in gaining a broader view of the financing landscape for young firms, going beyond the basics of venture capital and angel financing.

Entrepreneurial Finance introduces students to the myriad complexities of evaluating and financing young, high potential ventures, with specific introduction of frameworks, tools, deal terms, and varying sources of capital. Through a combination of lectures, case studies, and mock negotiations, this course will help demystify the fund-raising process by addressing key questions facing all entrepreneurs: When should I raise money? How much? From whom? Under what terms? And what are the longer-term implications of my chosen financing strategy?

Financial technology, also known as FinTech, is an emerging economic industry composed of companies that use technologies such as blockchain, machine learning and AI, to make financial services more efficient, secure and transparent. The FinTech ecosystem includes not only start-up challengers but also incumbent financial institutions seeking to innovate, as well as technology firms entering from outside of the financial industry. This course aims to provide insights into the FinTech revolution, including the nature of the disruption, innovation opportunities and strategic options. We will explore the FinTech landscape, at the same time delve into specific FinTech cases through the case teaching methodology. In this course we will also invite practitioners as guest speakers from varying FinTech sectors to shed light on first-hand industry developments and challenges.

Foreign exchange (FX) is not only the most heavily traded of all financial assets, it has the clearest interface between macroeconomics and finance. This elective will introduce you to the main theoretical models used to understand FX markets as well as in-depth analysis of their work.

This elective provides a basic framework for analysing corporate acquisitions, mergers and restructurings in an international setting. You will analyse all the essential elements of the acquisition process.

This module will introduce students to big data analysis using machine learning techniques. Students will utilise machine learning methods to use computational textual analysis and empirical modelling to quantify trends and sentiment in big data.

Trading is a crucial part of the investment process, The topic of the module is liquidity and price formation on securities markets

This elective allows you to apply key principles of private equity and venture capital to the financing of leveraged buyouts and early-stage ventures. The elective teaches students how to apply what they have learned in class to real life work situations by inviting inspiring speakers to present on campus throughout the module. The guest speakers come from various areas in the industry and discuss how they make transactions in real-life and what their roles entail on a daily basis.

"I have acquired a strong basis of Private Equity & Venture Capital and a comprehensive view on the topic. I learned that it’s not always a matter of technical skills in this field but that the human element is very important." Maria Vittoria Moschini, MSc Finance & Accounting 2018

This elective is an introduction to real estate investment analysis from the perspective of an investor. It emphasises both the teaching of the theory of real estate investment and real estate markets as well as teaching the practical methods and their implementation as used in a modern professional investment context.

This elective provides an in-depth analysis of credit and equity derivative products. We focus on corporate derivatives and cover the most important products, which serve as building blocks for structuring customised and sophisticated products.

This course focusses on methods for quantitatively analyzing text data, such as newspaper articles, social media posts, political speeches, and company product descriptions. The amount and availability of such data is growing rapidly, and extracting valuable information from it is an important challenge. In recent years, numerous machine learning methods have been developed for text. This course will introduce students to these methods, but of equal importance will be to discuss their application to problems in economics and finance.