20 credits

This introductory module is aimed at graduates who have not previously studied statistics at university level. It assumes no prior knowledge of statistics and builds from simple concepts to theoretical methods that are required for application to real life data and problems. The module will provide the foundation elements of probability and statistics that are required for the more advanced modules studied later on.

This will include:

• an introduction to probability and probability rules
• random variables and probability distributions
• data visualisation and representation
• hypothesis testing and confidence intervals
• power and sample size calculations
• correlation and simple linear regression

10 credits

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

20 credits

This module will provide an introduction to financial decision-making, and much of the relevant analysis will be developed from the standpoint of corporate finance. It will explain how a company should decide on the investments to be undertaken to meet its objectives, generally assumed to be the maximisation of its value. It will be demonstrated that this will require a rate of return on its investments in excess of the return available in the capital market on equally risky financial investments. As a result, it will be necessary to develop an understanding of the capital market risk-return relationship. This will require an appreciation of the nature of risk and how this can be managed by the development of portfolios.

Even though the focus of the module will be on corporate finance, it'll also require an appreciation of how the risk-return tradeoff is determined in the capital market.

Credits: 10
Assessment: final examination (50%) & individual assessment (50%)

This class aims to bridge the gap between theoretical finance and practical data analytics, preparing you for an increasingly data-driven financial services sector by equipping you with essential data analysis skills.

You will develop predictive models and use advanced tools such as machine learning, while learning how to present output as concise and interpretable visualisations. Emphasis will be placed on developing critical thinking as well as technical proficiency, with discussions concerning the treatment of incomplete data, the appropriate model and technique to use given different scenarios, and the ethical and privacy concerns of using large, novel datasets and machine learning models.

You will be required to effectively interpret and apply data insights across various contexts, including risk management, forecasting interpretation of accounts, and financial technology applications.

10 credits

This module aims to expose you to a number of diverse topics in stochastic processes that can be used to model real systems, with an emphasis on the valuation of financial derivatives. In additional to theoretical analysis, appropriate computational algorithms using R are introduced.

You will learn:

• how stochastic models arise
• the concept of a financial options
• how the Black-Scholes equation arises
• how to perform computer simulations based on mathematical models

10 credits

You'll be exposed to a number of diverse topics in econometrics that can be used to model real financial data, with an emphasis on the analysis of financial time series. The statistical software R is introduced for financial modelling.

You will learn:

• to analyse various financial time series data
• to undertake statistical analysis of financial risk
• to use R for econometric modelling of real financial data
• to use time series models to do forecasting

10 credits

Most people have an intuitive understanding of what risk is. The aim of this module is to formalise this understanding and develop models to quantify risk. Quantification of risk relies on many statistical methods. The emphasis in this course is the practical use of such methods.

You'll develop skills in communicating risk to risk managers as well as formulating practical risk questions that can influence policy decisions.

You can expect to learn about:

• the difference between uncertainty and variability
• quantifying uncertainty using methods such as bootstrapping and Bayesian inference
• selecting appropriate probability distributions based on given scenarios
• fitting probability distributions to data
• building risk models
• communicating your results as written reports

All theory will be implemented practically via computing sessions using the statistical software R. You'll learn to create bespoke functions in R to implement your models and use summary statistics and plots to communicate your results. 

10 credits

This module will develop your skills in data presentation and statistical communication. You will learn to develop data dashboards, which are increasingly used to allow key stakeholders (and the public) to gain key insights into data via interactive visualisation.

Topics covered will include:

• Creating a data dashboard in RStudio
• User interface design with respect to accessibility
• Creating interactive data visualisations which reflect a specific aim
• Reactive programming  in RStudio
• Static programming in R

10 credits

This module introduces deep Learning prediction algorithms covering background theory and practical application in Python.

Topics covered include:

• activation functions
• gradient descent, backpropagation and optimisation
• convolutional Neural Networks
• recurrent Neural Networks
• generative Adversarial Networks

10 credits

The aim of this module is to introduce fundamentals of the model portfolio theory and specifically to examine the Markowitz (1952) approach to optimal portfolio selection.

The module explores issues relating to optimal portfolio choice and issues in passive and active fund management through the lens of the nature of variance, covariance, risk and return.

The purpose of this module is to introduce you not only to the basic models, but also more sophisticated, practical models such as the Black-Litterman model. It also focuses on asset allocation and performance evaluation. The module introduces practical applications and an extension of basic theory.

Learning outcomes include:

• understanding the practical applications of Modern Portfolio Theory
• being able to use Excel in the areas covered by the module that are also applicable to other areas of finance
• using analytical skills in interpreting empirical findings
• learning to use Bloomberg and other databases for construction of portfolios

10 credits

In the first part of the module, the focus is on the basic principles of derivatives. In particular, we examine futures and forward contracts, options, swaps and credit derivatives, and how these may be used for speculation, hedging, and arbitrage purposes. The emphasis is on understanding the pricing of these derivatives and the strategies devised to hedge long and short positions in underlying assets such as equities, bonds, and interest rates.

The role of derivatives in the global financial market is also covered, including a discussion of the difficulties that can arise due to the regulatory framework of derivatives and the (partial) lack of regulation of derivatives.

Credits: 10
Assessment: final examination (50%) & individual assessment (50%)

This module aims to equip you with the skills and knowledge necessary to leverage unstructured textual data in a financial context. As the financial services sector increasingly makes use of data-driven insights, you will be taught methods through which textual information from sources such as corporate reports, news articles, and social media can be analysed.

You will gain hands-on experience with basic qualitative software such as NVivo, before covering traditional textual analysis methods (such as dictionary and machine learning methods) before more recent and advanced natural language processing (NLP) is introduced, including transformer-based models such as BERT and large language models (LLMs). Through these methods, you will be able to extract sentiment, detect trends, and identify key patterns in financial texts, evaluating the relative benefits and drawbacks of each approach (for example, more recent methods lack explainability but offer greater accuracy).

The module will place emphasis on developing both technical and analytical skills, encouraging you to critically evaluate the accuracy, relevance, and limitations of textual data analysis in decision-making processes. Additionally, the module will address ethical considerations and challenges related to bias, transparency, and data privacy in text analytics, specifically in relation to relevant UK guidelines and regulation.

By the end of the module, you will be able to apply text analysis techniques to real-world business scenarios, enhancing your ability to provide data-driven insights and solutions within various professional finance settings.

20 credits

This module provides an overview of the application of AI techniques - including those which mimic natural evolutionary processes (genetic algorithms and genetic programming in particular) - to a range of financial applications such as forecasting, portfolio optimisation and algorithmic trading.

After completing this module, you'll be able to:

• understand the benefits and opportunities for evolutionary computing in the context of financial applications
• understand the principles of evolutionary computation, in particular, genetic programming and genetic algorithms, as well as neural networks (particularly those configurations most suited to time series data)
• understand how the computational approaches covered in the module may be applied to financial problem-solving and understand their limitations
• develop and evaluate practical solutions to finance-based problems

10 credits

This module aims to provide an overview of the application of evolutionary computation techniques - those which mimic natural evolutionary processes (genetic algorithms, genetic programming and neural networks in particular) - to a range of financial applications such as forecasting and portfolio optimisation.

The module is very practical in its nature: much of the learning is achieved via a number (around 3) of assessed small mini-projects, and students are expected to develop solutions to problems using evolutionary computation techniques, evaluate these on real data sets, and compare them with other more traditional approaches. Consequently, a large amount of self-directed study and learning is expected.

10 credits

Please note: this module is only available to students who have completed Evolutionary Computation for Finance 1.

This module aims to build on the foundations of Evolutionary Computation for Finance 1 to explore more advanced applications of evolutionary and natural computing, in particular algorithmic trading.

The module is very practical in its nature: much of the learning is achieved via assessed mini-projects, and students are expected to develop solutions to problems using neural networks and evolutionary computation techniques, evaluate these on real data sets, and compare them with other more traditional approaches. Consequently, a large amount of self-directed study and learning is expected.

60 credits

You'll undertake a research project to work on a financial and/or data science problem, putting the theoretical skills you have learned into practice. The emphasis is on using the knowledge and skills that you have gained during Semester 1 and 2 and applying them via independent study to a given problem.

You'll have been equipped with the technical knowledge you will need in your taught classes and the purpose of the projects is for you to demonstrate your ability to independently tackle a problem. You'll be allocated a supervisor but are expected to work relatively independently.

Project topics change each year and projects with external partners may be available. You'll also have the opportunity to propose a research project yourself if you are passionate about something in particular.