Masters in Finance and Financial Technology (FinTech)

This pre-study interactive course is designed by ICMA Centre faculty to prepare you for your learning journey at Henley if you are undertaking postgraduate study in any finance-oriented subject. Through the course, you will appreciate the purpose of finance and investment, the main types of investment and financial markets, as well as the process of valuing investments and the role of risk and return in investment performance. These are all important fundamentals in understanding the workings of financial markets and being able to apply them to real and practical problems as part of your course. We encourage you to take the course even if you have studied finance before. The course is delivered online, requires around 15 hours of study and can be completed at your own pace.

Quantitative techniques and statistics are among the main tools finance and investment professionals utilise in analysing data linked to financial markets and securities. This interactive pre-study course is designed to deliver these essential building blocks in maths and quants and prepare you for a Finance Master’s if you don’t have a finance, maths, or quants background. For those with a relevant background, it can serve as a refresher of this essential knowledge.

This pre-study interactive course is designed by Henley Business School faculty to introduce you to the world of coding in Python. According to recent employer surveys, coding is a top 5 employability skill in the new world of work. The World Economic forum includes technology design and programming in the top 10 skills of 2025. Coding is an extremely powerful skill to have going forward in all areas of business and finance. Due to the importance of coding skills in your future, you will be using coding in some of your courses depending on your choice of programme at Henley Business School. So, this pre-study course can be seen as a stepping stone in your learning journey. Even if coding is not directly utilised as a part of your programme, completing this course, earning the certificate and complementing this with some further independent learning, could set you apart in an increasingly competitive job market. Python is the most utilised programming language in the world and so the purpose of this course is for you to learn the fundamentals of Python, and how to use it to solve practical problems in your area of interest.

This pre-study interactive course is designed by Henley Business School faculty, the University of Reading Study Advice Team and the International Study and Language Institute. The course aims to prepare you for your learning journey at Henley and focuses on helping you build important study skills to succeed in your Masters and beyond. Skills such as curiosity and independent thinking, effective communication and collaboration, digital capabilities, storytelling, empathy and professional integrity are not only important in your course itself but are also sought after by employers in all areas of business. Therefore, they are instrumental in succeeding in your professional career. The learning journey of our Masters programmes is designed in a way that allows you to build such skills and this course intends to demonstrate exactly how, and therefore help you to make the most of your Masters.

It then analyses the main framework of portfolio theory and optimum asset allocation, and how portfolio diversification can help minimise investment risk. Through the introduction of the main pricing models (single-factor and multi-factor) and the fundamental concept of arbitrage-free pricing, students are exposed to techniques for valuing and comparing different risky assets. The theory of Market Efficiency and its violation completes students’ perception of how financial market equilibrium is reached in modern economies. Furthermore, the module provides an in-depth introduction to financial derivatives (futures and options) and their key role in leveraging and hedging investments. All the techniques introduced are widely applied in other elements of the programme.

Part I of the module discusses the role of banks and traditional currencies in the global economy and explains how technology is reshaping the financial services industry.

In Part II, the main characteristics and risks of a range of financial markets are presented: equity, fixed income, foreign exchange, futures and commodity markets. A series of interactive workshops are used to learn about ethical investing and alternative trading systems. The aim of part II is to provide an economic framework for understanding global financial markets, financial institutions, market players and the importance of liquidity and price efficiency.

The module is core for students in the Finance and Financial Technology programme. It requires no prior knowledge of mathematics or finance and is designed to be easily understood by students of any academic background.

The aims and objectives of the module are to give students an introduction to econometrics so that they might understand the econometric techniques used in the finance research literature. Via case studies and computer modelling exercises, students then learn how to apply these techniques to real data. Emphasis is placed on practical applications of the techniques in the global financial markets. The module aims to encourage the development of IT and data handling skills: in particular, the use of Python as the software to apply the econometric techniques to data from Eikon and Bloomberg.

Students who complete this course should be able to write programming functions in Python, process data files including reading, modifying and writing data to external files; to connect to databases and to obtain and process data from the Web, as well as, to use Python for Finance applications including developing trading strategies, and back-testing with historical data, estimate risk measurements, and evaluate the performance of the strategies.

Students are expected to have completed the “Future of Work: Coding with Python for Business and Finance” pre-entry module before they start this module.

In this module you will learn how the evolution of the sector is unfolding and gain insights to the challenges and opportunities that it is generating.

By the end of the module, it is expected that students will be able to:

• Describe the principles of insurable risks and the legal basis for insurance contracts and illustrate the main types of insurance and the business model used by insurers.
• Explain how the insurance industry is evolving and the new products, services and business models brought about by current technological changes (InsurTech trends and proposals) and explain how Internet of Things (IoT), cloud technologies and big data are used in the insurance industry and their applications in insurance risk analysis, and pricing.
• Describe and apply selected insurance models used in non-life, life insurance, asset and liability management, and the calculation of solvency capital requirements and illustrate the regulatory environment and new ethical challenges resulting from the availability and use of big data in the insurance market
• Explain cyber risk and describe the insurance products that provide cover for such risks

The module aims to provide students with the ability:

• To use Python to train a computer to recognise patterns in data sets from popular finance applications using a wide range of industry standard Machine Learning models. For instance, we will look at stock price forecasting, asset classification and optimal investment selection.
• Develop a rigorous Data Science approach to the collection, organisation and visualisation of large sets of structured and unstructured financial data.
• Understand and be able to apply big data techniques, including artificial intelligence and large language models, to propose solutions to finance problems at the level of a business problem, a complex project and at the level of the whole organisation.

The module places no pre-requisite of knowledge in any area of mathematics or computer science and as is designed to be easily understood by students of any academic background. The module is ideal for students in any area of finance as techniques of Machine Learning and Big Data become much more common in their application throughout all parts of the financial services industry.

The self-directed nature of study for this model should encourage students to be resourceful in their search for relevant literature and data, and to manage the various stages involved effectively, leading to timely submission of the finished piece.

The module aims to build on the investment management foundation that has been introduced in the Securities, Futures and Options module. This investment portfolio management module covers the issues involved in understanding the investment management industry, constructing and maintaining an optimal investment portfolio (active, passive or smart beta investment styles), diversification, evaluating portfolio performance, risk management and portfolio rebalancing. The module will also introduce you to the world of alternative investments - the hedge fund industry, in particular. The compulsory, practical group project of the course will provide students with hands-on experience in constructing and managing a realistic investment portfolio.

The focus of the module is on PE and VC and high-growth ventures from the viewpoints of both the entrepreneur or management team and of the investing institution (general partner), although private equity as a whole is covered (including management buyout transactions) as well as the relationship between the private equity or venture capital firm and its own investors (limited partner institutions) as are mergers and acquisition deals. adherence to environmental, social and corporate governance (ESG) and diversity & inclusion (D&I) considerations.

This is a highly practical module; extensive use will be made of case studies and a group project in addition to guest seminars from invited experienced practitioners and entrepreneurs.

This module is intended for students who may be interested at some stage in their careers in raising private equity or venture capital finance for an entrepreneurial venture or management buyout, or who see themselves working in a professional investment capacity (eg with a private equity firm as an investment executive or with an investment bank) or as an investor with a pension fund, bank or insurance company or as a corporate finance adviser, or who simply want to gain an understanding of how the private equity & venture capital fundraising and investment and M&A processes work.

To do this, students will examine contemporaneous case studies that cover key instances of financial failure, crisis and/or misconduct, and understand the risks the community is exposed to these. Students will also study how safeguards such as whistleblowing processes and regulatory accountability mechanisms, enable the exercise of financial power and regulatory power to be kept in check.

This module reviews the current state of the trading industry and identifies aims, features, regulations, and limitations of three main groups of algorithmic trading strategies: market making, trade execution and statistical arbitrage. Practical seminars are used to demonstrate how to apply trading algorithms to high-frequency data.

This module will equip the students with a basic knowledge of algorithmic and high frequency trading strategies which are commonly used in the trading industry.

By the end of the module, students will be able to

• Discuss the particularities of different forms of alternative investments, especially hedge funds, commodities and real estate
• Characterize the benefits and risks of different alternative investment classes
• Apply key concepts of financial engineering to structured product design
• Explain and analyse different types of structured products currently available in the global capital markets.

You will look at how to implement sustainability objectives in a corporate environment and how governments can facilitate corporate sustainability with the right incentives and policies. This module aims to give students a thorough understanding of how sustainability goals are shaping the corporate world and how this transformative process is involving a wide range of stakeholders.