FinTech with Industrial Placement - MSc

This module covers key concepts related to financial risk management, especially market risk in financial institutions. It broadly addresses the rationale for practising risk management, followed by approaches to measuring and managing risk. 

The course will cover the following indicative topics: taxonomy of Risks, Essential Financial Products, Introduction to Regulation, Modelling Portfolio Risk, Market Risk and VAR, Credit Risk, Risk-Adjusted Performance Measures.

This module will provide students with a core understanding of Financial Technology applications, and specifically how a wide range of disruptive innovations are reshaping the financial system. Particular emphasis will be placed on understanding how banks and other financial institutions can benefit from using these technologies.

The module will cover the following indicative topics: Introduction to FinTech, Payments, cryptocurrencies and blockchain, Digital finance, New forms of lending and crowdfunding, Data and technology in financial services, The role of artificial intelligence and machine learning.

This module will cover the following topics: Investment appraisal techniques and decisions, Stock market efficiency – capital market behaviour, Portfolio theory, The Capital Asset Pricing Model, Sources of finance, Capital Structure.

This module provides a general introduction to the quantitative methods used in financial applications and topics may include: Statistical concepts, Probability distributions, Statistical inference, estimation and hypothesis testing, Correlation, spurious correlation and general dependence measures, Linear regression, Multiple linear regression, Logistic regression, Monte Carlo simulation, Modelling in Excel.

This module will introduce students to Python, a programming language that has become the industry standard. Students will learn how to use Python in order to conduct financial and econometric analysis. Particular emphasis will be placed on programming for specific financial applications such as portfolio optimization, asset valuation, and derivatives pricing.

This module will provide students with a core understanding of algorithmic trading, and specifically how to develop and implement quantitative trading strategies. The module will cover the following indicative topics: High-frequency trading and tick data, Backtesting and automated execution, Mean reversion strategies, Momentum strategies, Arbitrage strategies, Risk management, Performance evaluation.

In this module, students will learn about the fundamentals of machine learning and forecasting techniques and gain hands-on experience with analysing and solving a variety of problems encountered in business and management.

Three indicative areas of the module could include:

• Machine learning: The introduction of modern machine learning techniques used in business data analysis, including both supervised learning (e.g. regression, classification, and artificial neural networks) and unsupervised learning (e.g. association rule discovery and cluster analysis).
• Forecasting: Students will learn about various forecasting methods, including exponential smoothing methods and the Box-Jenkins method (i.e. the ARIMA model and variants).
• Data analysis report writing. Students will systematically carry out a data analysis project and write a data analysis report.

The data analysis packages such as R, SPSS, and Weka may be used in this module.

This module aims to introduce students to the power of big data analytics and data visualisation techniques in contributing to business value creation. The module will also enable students to solve a variety of complex data centred business problems using computer software tools like Apache Spark and Python.

The module covers two main themes as follows.

1. Theoretical understanding of big data analytics: This part involves learning about the theoretical foundations of big data analytics, text mining, and social media data mining. It also introduces the effective use of data visualisation and database management concepts and their links with big data analytics. Example applications of big data analytics and visualisation techniques discussed within the module will focus on addressing contemporary challenges faced by industry.
2. Building practical skills and managerial insights: In this part of the module, students will learn how to interact with both SQL and NoSQL databases and how to solve business problems using advanced functions within the Apache Spark and Python software platforms. Students will be guided through demonstrations involving a variety of exercises that will prepare them to be data-driven managers and executives capable of utilising big data analytics for business value creation.

This module will allow students to apply the knowledge and skills gained throughout the course in order to produce an extended independent piece of work. Every student will be allocated a personal supervisor who will guide them through the process. As part of their project, students will generate an interesting and relevant research question in finance, analyse the related financial literature, and conduct their own independent research into their topic. More specifically, students will research topics in the fields of finance (MSc Finance); finance, investment and risk (MSc Finance, Investment and Risk); finance and financial management (MSc Finance and Management); banking and finance (MSc Banking and Finance); financial technology (MSc Financial Technology).