MSc Financial Technology

Programming is taught to a level at which the knowledge can be useful to a financial professional.

This class provides an introduction to the basic principles of financial decision taking and the theory of finance. It will develop the basic principles of valuation, the nature of risk and uncertainty, and the relationship between risk and returns.

The first part provides an introduction to the basic theory and application of statistical modelling. Topics covered include:

• data analysis
• probability theory
• distributions and moments
• estimation
• hypothesis testing

The second part focuses mainly on two areas:

• regression modelling
• multivariate analysis

While key background theory will be presented, the emphasis is on the generation and interpretation of output from commercially available software.

This module will introduce the challenges of analysing big data with specific focus on the algorithms and techniques which are embodied in data analytics solutions.

At the end of the module, you'll understand:

• the fundamentals of Python for use in big data technologies
• how classical statistical techniques are applied in modern data analysis
• the limitations of various data analysis tools in a variety of contexts

This course uniquely encourages students to explore the nexus between sustainable finance and financial technology (FinTech). Students will be introduced to key concepts that define FinTech. The dynamic FinTech landscape will be navigated, outlining how a new ecosystem of financial services is evolving, with new innovative financial products and services being offered and new alternative markets being developed. Against the background of the global sustainability challenge and the critical need for mobilising sustainable finance, the role that FinTech can play as an enabler of this capital flow will be discussed. 

Across areas such as crowdfunding and peer-to-business lending, cryptoassets and blockchain, and wealth management and roboadvisors, students will synthesise academic and industry evidence to evaluate the levels of investment currently being directed through FinTech enabled channels to fund sustainable companies and projects. Particular attention will be given to the area of Environmental, Social and Governance (ESG) investing and Socially Responsible Investing (SRI) and how technology can support this activity. The challenges of ESG measurement will be considered and financial data science applied to the problem of ESG scoring. Portfolio management demonstrations will be provided to students under emerging portfolio construction frameworks that seek to optimise across risk, return and ESG.

The class will build on the fundamental multivariate statistics by developing both visualisation and advanced analysis techniques relevant in the area of big data. The focus will be on application and interpretation of techniques and there will be an investigation of what makes good data. The class will develop both new theoretical knowledge in the form of analytics techniques as well as new software skills in relevant analytics software.

This class is led by industrialists in partnership with academics concerned with FinTech problems. The practical reality of applying analytical methods in financial technology business is often far removed from the classroom. While traditional teaching can alert students to such issues, understanding needs to be reinforced by experience, which is acquired in this module.

The aim of this class is to critically evaluate the underlying technologies and innovations emerging in fintech and explore the various potential applications and considerations in each case. By linking academic theory with practical use cases and industry speakers, the module aims to provide students with both a technical as well as a strategic overview of core technologies.

A proportion of this module will also focus on various considerations inherent in financial technologies, such as ethics, trust, security and built-in behavioural biases. Such considerations are important given the amount of personal financial data that is now generated, the increasing use of machine learning and artificial intelligence in financial services, and the number of new entrants into the banking industry.

The class employs some of the basic principles of financial analysis to consider the application of risk analysis. It explores issues relating to risk management in the banking sector, with a particular focus on the regulatory requirements stemming from the Basel Accord.

The aim of this class is to examine the Markowitz (1952) approach to optimal portfolio selection. The class explores issues relating to optimal portfolio choice and issues in practical fund management. This would be useful for those who wish to specialise in Roboadivsors.

This class will provide you with a strong grounding in derivatives that may be used to manage the financial risks faced by individuals, financial institutions and business corporations. This would be useful for those who wish to focus on FinTech for market instruments.

While fixed income securities (bonds) have been traded for a far longer time than equities, it's only recently 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 an MSc programme in Finance is expected to be familiar with the nature of the valuation of bonds.

This class will put the MSc courses at Strathclyde in a position to provide students with the opportunity to analyse bonds and the markets in which they are traded in more depth than is possible at the moment.

Find out more in the class outline for Fixed Income Analysis class outline.

This class introduces fundamental stochastic methods and techniques and then provides applications to large scale modelling. These methods are crucial for a better understanding of uncertainty and risk and these approaches are closely related to many important fields from forecasting to simulation.

The class will focus on the main two forms of business simulation:

• discrete-event simulation (DES)
• system dynamics (a continuous simulation technique)

For DES, the class will start with an introduction, aiming to familiarise students with the concept and its use. For system dynamics the class will provide a background to system dynamics including its links to other modelling techniques.

This class will explore the entire process of structuring a risk problem, modelling it, supporting and communicating recommendations, both theoretically and in practice.

This class will continue the focus on machine learning and data mining. It will also provide an introduction to data visualisation and big data platforms.

To aim of this class is to equip students with a sound understanding of the principles of machine learning and a range of basic approaches, along with the knowledge of how and when to apply the techniques.

The project allows the student to use all that they have learnt to specialise in an area of interest, delving much more deeply into it.

This project uses management science techniques to solve a data problem.