Tailor your learning with electives
Our range of electives enable you to tailor your learning to meet your career ambitions and match your areas of interests. Many of the electives are shared with our other finance Master’s programmes, giving you the opportunity to expand your network.
Applied Trading Strategies
This elective provides insight into financial trading strategies from an industry practitioner’s perspective. The elective covers the wide spectrum of strategies across asset classes and hedge fund styles with an emphasis on investment, arbitrage opportunity and risk management. The content also includes quantitative pricing models with back-testing in Python across different market regimes. You will study trading strategies in a non-technical intuitive manner using a ‘first principles’ approach. Real-life ‘war stories’ from the hedge fund and banking worlds will be used to supplement conventional textbook analysis.
Advanced Options Theory
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. You will also be introduced to some of the more technical and theoretical aspects of option pricing.
Asset Allocation & Investment Strategies
This topic provides key knowledge in investments and portfolio management. You will examine the key trading strategies used by hedge funds and demystify the secret world of active investing. The module combines the latest research with real-world examples to show how certain trading strategies make money–and why they sometimes don’t. You will use machine learning to look at how market price of stocks and bonds can differ from the model prices, leading to new perspectives on the relationship between trading results and finance theory. Several different strategies will be explored in depth, including fundamental tools for investment management, dynamic portfolio choice, equity strategies, macro strategies, yield curve logic and arbitrage strategies.
Big Data in Finance II*
Big Data in Finance II builds on and complements insights from the previous module. The module will focus on three key techniques in Big Data analysis and machine learning, and their applications to finance. First, you will explore unsupervised machine learning models (e.g. clustering algorithms) and their applications to recommendation algorithms in finance. Second, expanding the introductory material on neural networks in Big Data in Finance I, the module will develop this material further to cover Deep Learning techniques, which will then, as in Big Data in Finance I, be applied to credit scoring and/or portfolio choice problems. Third, the module will introduce and discuss reinforcement learning models, with potential applications to portfolio selection and trading strategies.
“The skills students learn on this module are directly applicable for anyone wanting to work in a fintech business that involves credit scoring or asset management.”
*Subject to approval
Blockchain and Crypto Assets
This module is designed to explore the key mechanisms and features of blockchains and distributed ledgers. Students will also learn how to implement business processes and represent traditional financial assets on a distributed platform.
The module will cover core aspects of the technology—cryptography, consensus protocols, peer-to-peer networking—with particular reference to Bitcoin. Enhancements of the Bitcoin core protocol and features of other crypto assets will also be discussed.
A more practical part of the course will feature hands-on sessions on how to implement smart contracts and code business logic on a permissioned blockchain.
The module will make extensive use of demos for a deeper understanding of the platforms and may feature guest speakers. Prior experience in programming is recommended but not required.
Computational Finance with C++
This elective takes the key mathematical models in finance and develops the numerical methods used to solve them using C++. The numerical methods you will study are: Monte Carlo simulation, binomial trees, finite differences, convey optimisation and eigenvalue methods. You will learn about the financial models and discuss how to effectively design a numerical method using object oriented techniques.
You will have the opportunity to directly work on practical examples requiring hands-on interaction with the programming language.
Corporate Governance and Stewardship
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.).
In recent years there has been considerable growth in markets for derivatives contracts, such as futures, swaps, and options on financial assets. Derivatives are used by individuals and institutions to meet a variety of objectives. Firms and portfolio managers can use derivatives to hedge particular kinds of risk or alter the distribution of the returns on their portfolios in certain ways. Some institutions may use derivatives to speculate. There is a large literature on derivatives valuation. At first the theory might appear advanced and difficult, but it is in fact quite accessible. The purpose of the elective is to provide you with the necessary skills to value and to use derivatives instruments in a purposeful way. In order to provide a useful treatment of these topics in an environment that is changing rapidly, it is necessary to stress fundamentals and to explore topics at a technical level.
In this elective you will learn to develop and apply the techniques learnt in the Investments and Portfolio Management module to the pricing of a range of financial derivatives and to the determination of interest rates at different maturities. By the end of the module you’ll have an in-depth understanding of valuation techniques applicable in a variety of financial markets and sought after by prospective employers.
Entrepreneurial Finance (EF) 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. The course 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.
EF 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?
Innovation and Strategy in FinTech
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.
Introduction to Quantitative Investing (international elective)
This elective offers an introduction to analytical techniques and quantitative methods relevant for algorithmic trading. Topics include the basics of automated execution, pairs trading and long-short equity trading strategies. The elective is taught in two parts with the first part delivered online and the second part is an international study trip. During the trip, traditional lectures are complemented by guest speakers, company visits and experiential learning activities. The class of 2017-18 travelled to New York for an intensive study experience.
There will be an additional cost for taking this elective, which is reviewed on an annual basis.
Machine Learning and Finance
This elective will enable you to master advanced machine learning techniques, to handle noisy datasets and extract value from them. We will go together through a large variety of methodologies : supervised learning, HMM, LSTM, etc. In the end, the objective is that you become practitioners in the field, able to tackle decision making challenges related finance, using quantitative and qualitative data.
Structured Credit and Equity Products
This elective provides an in-depth analysis of credit and equity derivative products. You will study corporate derivatives and cover the most important products, which serve as building blocks for structuring customised and sophisticated products. For each product you will analyse its rationale and mechanics, its pricing and modelling, and, most importantly, the intuition behind the trading strategies investors use them for. The range of products covered includes Credit Default Swaps, indices and options; equity dividend swaps and variance swaps as well as credit correlation products.
Text Mining for Economics and Finance
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.
Electives available and module outlines are subject to change. Imperial College Business School reserves the right to alter modules whenever they need to be amended or improved. Faculty may also change as and when required.