The module will introduce you to various topics drawn from the modern empirical finance literature and to the underlying econometric techniques used to evaluate alternative models of the dynamics of asset prices and returns. Knowledge of basic econometrics (such as that covered by ECON6068 Quantitative Economics) is a pre-requisite. Software such as EViews or R will be used to give practical illustrations. Successful completion of this module is of particular value if you intend to prepare an empirical dissertation involving financial applications as the final component of your MSc programme.
Financial Econometrics 1 provides you with the necessary skills to undertake quantitative research in finance. Lectures will introduce a broad range of topics (e.g. regression). However, you will discover that by understanding and applying some basic concepts various issues can be analysed in a similar manner. In particular, we will introduce basic theoretical concepts developed in statistics and econometrics. Understanding the main theoretical methods is essential to appreciate the analytical tools and their applications to finance. Tutorials take place in labs where you will be able to conduct your own research via the software EViews. The module introduces empirical methods used in finance and is a prerequisite for Financial Econometrics 2 in the 2nd semester.
The module is intended to build on Financial Econometrics 1 and offers a deep understanding to undertake empirical research in finance. Lectures will cover topics from introductory level to more advanced econometrics material. The students will learn how to use the EViews software through practical examples, and be able to conduct their own empirical research via the software.
This module will provide students with a solid understanding of key concepts, tools and insights of financial economics. Upon completion, students should be confident in using standard techniques to address issues like the functions and functioning of financial markets, and their efficiency properties, as well as be able to deal with standard application of financial theory to equity and bond pricing.
This module provides a deep insight in some key theories and topics in Financial Management. The module looks at how firms and corporation manage financial investment and decisions in the long term and short term. The module will discuss topics ranging from how firms evaluate financial performance, decision regarding investment in capital, how firms decide in dividend policy.
This module provides a solid mathematical introduction to the subject of Compound Interest Theory and its application to the analysis of a wide variety of complex financial problems, including those associated with mortgage and commercial loans, the valuation of securities, consumer credit transactions, and the appraisal of investment projects. The investment and risk characteristics of the standard asset classes available for investment purposes are also briefly considered, as is the topic of asset-liability matching. The module also provides introductions to the term structure of interest rates, simple stochastic interest rate models, the concept of no-arbitrage pricing of forward contracts, and behavioural economics. Pre-requisite for MATH6127
This module provides a solid mathematical introduction to the subject of Compound Interest Theory and its application to the analysis of a wide variety of complex financial problems, including those associated with mortgage and commercial loans, the valuation of securities, consumer credit transactions, and the appraisal of investment projects. The investment and risk characteristics of the standard asset classes available for investment purposes are also briefly considered, as is the topic of asset-liability matching. The module also provides introductions to the term structure of interest rates, simple stochastic interest rate models, and the concept of no-arbitrage pricing of forward contracts. Pre-requisite for MATH6127
This module provides a solid mathematical introduction to the subject of Compound Interest Theory and its application to the analysis of a wide variety of complex financial problems, including those associated with mortgage and commercial loans, the valuation of securities, consumer credit transactions, and the appraisal of investment projects. The investment and risk characteristics of the standard asset classes available for investment purposes are also briefly considered, as is the topic of asset-liability matching. The module also provides introductions to the term structure of interest rates, simple stochastic interest rate models, and the concept of no-arbitrage pricing of forward contracts.
The module aims to introduce the students to the basics of portfolio theory. Beginning with a summary of the reasons why both private investors and large institutional investors might wish to own share portfolios, the module progresses to consider how risk and return vary as share prices move and introduces the student to the basics of Markowitz portfolio theory. Illustrative two-asset cases will then be considered before the risk/reward diagram for an N asset portfolio is examined. The notions of short selling and riskless assets will then be introduced to the student and incorporated into the theory. Finally, the student will learn how to solve the general Markowitz portfolio problem to determine the Optimum portfolio, the Capital Market Line and the Market Price of Risk. If time permits, discussion will also take place of more advanced models of portfolio theory.
Students will be introduced to the regulation of financial reporting; the information perspective to financial reporting; the valuation relevance of financial reporting; economic consequences and Positive Accounting Theory; Earnings management.
The module explores bank regulations as well as theoretical and practical techniques to measure market risk, interest rate risk and credit risk. It also discusses the theoretical and practical aspects of the risk management techniques employed in the financial services industry to hedge market risk, interest rate risk and credit risk.
This module explores traditional financial risk management and bank regulation in the context of an increasingly AI-driven and digitally enabled financial system. It covers core tools for measuring and managing market, credit and interest risk, then examines how AI and other data-driven models reshape risk transmission, shift risks to new players and can accelerate episodes of market stress. The module highlights how these developments both challenge and enhance existing risk frameworks and regulation, and shows how financial risk management must adapt to contemporary risks and opportunities created by AI and digital finance.
The module seeks to equip students with essential practical and technical skills that are critical for success in the financial sector. It is designed to develop students' competencies in key areas such as financial data analysis, financial modelling, programming for finance, use of industry-standard databases, and effective communication of financial information. Through a combination of workshops, case studies, simulations, and certifications, students will acquire the applied knowledge and transferable skills that are highly valued by employers across a range of financial careers.
This module examines how financial technologies (FinTech) and applied artificial intelligence (AI) are reshaping financial services. It is deliberately not a model‑building module: core AI/ML concepts are covered at an intuitive level (what they are, where they work, where they fail), and the emphasis is on turning those concepts into practical, auditable workflows that analysts, product teams, operations, risk and compliance functions can use in the age of AI and FinTech. You will learn how to: (i) design effective prompts for common finance tasks, (ii) use AI copilots to prototype lightweight tools and scripts that support financial work, and (iii) design workflow automation solutions for back‑office and compliance processes. FinTech topics (cryptocurrencies, decentralised finance (DeFi), open banking and embedded finance, platforms/ecosystems, RegTech/SupTech, digital assets, payments and lending) are used as contexts for applied exercises rather than as the core syllabus. This positioning reduces overlap with modules focused on digital money/banking, banking transformation, or technical machine learning. You will finish the module with hands‑on artefacts (prompt packs, workflow designs, lightweight prototypes and controls documentation) that translate directly into workplace skills. The module is designed for students on the MSc (Finance) or equivalent programmes. It assumes prior exposure to core finance (corporate finance, investments, basic risk management) and introductory statistics. Prior programming experience is not required; students with stronger technical backgrounds are encouraged to take the companion “AI and Machine Learning in Finance” module for deeper model‑building and coding experience.
Many real-world engineering structures are too complex for their behaviour to be understood using an ‘exact’ analytical or theoretical method alone. Therefore, in practice we often use approximate numerical or simulation-based tools for structural analysis, of which Finite Element Analysis (FEA) is the most established. The Finite Element Method (FEM) unlocks the ability for engineers to predict the performance of complex structures in detail, including their deformations and stresses generated by mechanical loads, and their free and forced vibration. However, the predictions obtained from these simulations are only as reliable as the data used to generate them, and this is limited by necessary simplifications and assumptions. A skilled FE analyst understands the assumptions and limitations of the method, and they can make best use of the range of commercial FEA software packages available by drawing on an understanding of the theory behind the simulations. This module is aimed at providing the requisite background theory and practical experience of solving problems using the Finite Element Method. It provides fundamental knowledge and an understanding of the technique of FEM, equipping students with tools to analyse engineering structures problems using FEM and typical commercial FEA packages.
The purpose of the module is to develop students’ ability to undertake field research in geography by practice-based learning on a fieldtrip and associated lectures. The module will give practical experience of carrying out research to ensure practical skills and research experiences that will ready students for Year 2. The practical experience of undertaking group research project as part of a fieldcourse, as well as a range of research skills including design, methodology and data analysis, will be learnt. The module is core for all first year geography programmes; activities in the field will be tailored to physical and human geography specialisations.
This module focuses on the exceptional diversity of forms and functions of fishes, how they evolved and how best to study them in the field and lab. Using a combination of lectures, laboratory exercises, and field trips, we will begin by exploring how geological and climatic events in Earth's history are associated with the evolution of major fish lineages. We will then shift focus to consider how fish interact with their habitats and evaluate different techniques we can apply to study the community composition and functional ecology of fishes. Students will leave this module with an appreciation of the extensive diversity of fishes and a knowledge of how their evolutionary history has shaped marine and aquatic ecosystems around the world.
This module aims to provide you with a comprehensive understanding of the fixed income securities and the analytical tools required for evaluating fixed income securities investments and strategies. Together with an understanding of investment data and performance measurement. You will develop core skills in bond valuation, yield curve analysis, credit risk assessment, and interest-rate risk management, and apply these in the context of modern portfolio construction. Reflecting the evolution of global capital markets, the module integrates the study of investment strategies and introduces sustainable fixed income instruments, such as green and sustainability-linked bonds. In addition, the module offers a brief session on the use of AI, machine learning, and fintech in bond analytics, providing you with awareness of emerging industry practices. The module blends financial theory, empirical evidence, and practical techniques for analysing fixed income markets and formulating investment strategies in the current financial environment.
This module aims to provide you with a thorough knowledge of the fixed income securities and techniques available for fixed income securities analysis, together with an understanding of investment data and performance measurement. Emphasis will be placed on the use of this knowledge in the current financial market environment.
This module will introduce and develop flexible statistical modelling methods that allow for general and complex forms of data to be modelled, extending ideas already encountered in earlier modules on linear and/or generalised linear modelling. The two main foci of the syllabus will be methods for modelling grouped data using random effects, and non-parametric “smoothing” methods for modelling data with complex functional form.
This module will provide the essentials of modelling and understanding the dynamics of aerospace vehicles: equations of motion derived from first principles, sensing and actuation systems and their limitations, model verification, implications for guidance and control.
