ACFN4101: Mathematical Foundations for Finance

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module provides students with essential mathematical knowledge and skills to support their study of finance, with a focus on algebra, calculus, and matrix mathematics applied to financial concepts such as the time value of money, risk and return, and portfolio optimisation. It aims to develop students’ ability to apply quantitative methods to financial problems, interpret data, and use tools like Excel and introductory programming to support analysis. The module also fosters transferable skills including problem-solving, analytical thinking, and effective communication of quantitative findings, laying the foundation for more advanced study in finance.

Educational Aims

Upon successful completion of this module students will be able to…

1. Use core mathematical concepts—including algebra, calculus, and matrix operations—to analyse and solve fundamental problems in finance.
2. Interpret and manipulate financial data using quantitative techniques.
3. Use digital tools such as Excel and introductory programming in Python, R or other languages to model financial scenarios, perform calculations, and present results.
4. Demonstrate clear written communication which integrates theoretical knowledge with problem-solving.
5. Work independently and collaboratively, as appropriate, in the completion of course tasks.

Outline Syllabus

This module introduces students to the fundamental mathematical techniques required for the study of finance, with an emphasis on applying quantitative reasoning to financial concepts and decision-making. The module begins by consolidating students’ understanding of core mathematical principles, such as functions, equations, and basic algebra. These foundations are quickly connected to financial applications, particularly the time value of money, which forms the basis for understanding investment decisions, interest rates, and valuation.

Building on this foundation, the module introduces matrix mathematics and its relevance to portfolio theory. Students learn to manipulate financial data, extracted from key financial databases such as Datastream and CRSP, using matrices, understand variance-covariance structures and interpret risk-return trade-offs. Alongside these theoretical elements, students gain practical skills through structured lab sessions using Excel and rudimentary functionality in Python and R, or other suitable languages, where they learn to perform calculations and visualize relationships in real financial datasets.

As the module progresses, students are introduced to differential and integral calculus, with an emphasis on their applications in finance. Key topics include optimization—such as maximizing utility or minimizing risk in a portfolio context—as well as the use of calculus in understanding rates of change, marginal analysis, and cumulative processes in financial models.

The module culminates in a project that requires students to integrate the mathematical techniques they have learned with financial data analysis, fostering critical thinking and applied problem-solving. This structured progression ensures that students not only develop core quantitative skills, but also learn to apply them in meaningful financial contexts, preparing them for more advanced study in finance.

Assessment Proportions

This module adopts a structured and progressive approach to teaching and learning that is well aligned with the overall aims of the BSc (Hons) Finance programme. As a core first-year module, it lays the essential quantitative groundwork required for understanding financial theory and practice in subsequent modules covering key areas including Financial Markets, Corporate Finance, Investments and Quantitative Finance.

The teaching strategy blends traditional lectures with lecture-based workshops, practical and lab-based sessions to balance conceptual understanding with practical application. Lectures introduce key mathematical principles and their relevance to finance, while workshops provide guided problem-solving practice. Lab sessions offer hands-on experience with financial data and tools such as Excel, Python, R, or other suitable languages, allowing students to apply mathematical methods to real-world financial problems. This multi-modal delivery approach caters to a variety of learning preferences and supports inclusive learning.

To support student learning and engagement, the module incorporates formative elements such as worked examples, structured exercises and drop-in sessions. These are complemented by opportunities for peer interaction and digital support materials via the virtual learning environment.

This module follows an inclusive assessment and learning strategy, designed to reflect both individual competence and applied understanding. It includes a final exam to evaluate students' mastery of core mathematical concepts and a group coursework project that challenges them to apply techniques to real or simulated financial data. The project promotes research-led learning, digital literacy, and communication skills. All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication.

Overall, this module is closely integrated into the programme’s broader strategy by fostering core numeracy, analytical thinking, and applied data skills—key competencies for both academic progression and employability in the finance sector.

ACFN4102: Quantitative Methods

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module provides a solid foundation in essential mathematical knowledge and statistical skills relevant to the disciplines of accounting and finance. Students will learn essential techniques like differentiation, integration, multiple equation systems and optimization, equipping them with tools to find optimal solutions with or without constraints. Students will explore random variables and distributions and use descriptive statistics for data analysis, presentation and visualisation.

With a practical approach, this module enhances student’s mathematical and statistical knowledge, critical thinking, and problem-solving skills, preparing them for more quantitatively focused subjects offered across the whole suite of accounting and finance undergraduate programmes.

Educational Aims

Upon successful completion of this module students will be able to…

1. Use core mathematical concepts—to analyse and solve problems in accounting and finance.
2. Explain key probability and statistical concepts such as random variables, distributions, statistical estimation, sampling and hypothesis testing.
3. Interpret and manipulate accounting and financial data using quantitative techniques.
4. Select and apply appropriate mathematical techniques to investigate and solve problems in accounting and finance.

Outline Syllabus

This module introduces students to the fundamental mathematical techniques required for the study of finance and accounting, with an emphasis on applying quantitative reasoning to financial and accounting concepts. The module begins by consolidating students’ understanding of core mathematical principles, such as functions, equations, basic matrix algebra, differentiation and integration. These foundations are quickly connected to financial and accounting applications, particularly the time value of money, which forms the basis for understanding investment decisions, or portfolio theory, maximizing utility or minimizing risk.

As the module progresses, students are introduced to probability theory and descriptive statistics, including the concepts of random variables, sample space, distributions, and sample descriptive statistics like mean, median, mode, variance, standard deviation during the lectures.

The last part of the module will introduce students to estimation and testing techniques, in which they will learn about populations, random samples, parameter estimation in large and small samples, confidence intervals and hypothesis testing.

Alongside these theoretical elements, students gain practical skills through structured lab sessions using Excel, where they learn to perform calculations and visualize relationships in real financial datasets from core financial databases such as Datastream and CRSP.

Assessment Proportions

This module adopts a structured and progressive approach to teaching and learning that is well aligned with the overall aims of the BSc (Hons) Accounting and Finance programme. As a core first-year module, it lays the essential quantitative groundwork required for understanding financial and accounting theory and practice in subsequent modules such as Corporate Finance, Investments, and Auditing amongst others.

The teaching strategy blends traditional lectures with practical sessions, workshops, and lab-based sessions to balance conceptual understanding with practical application. Lectures introduce key mathematical principles and their relevance to finance and accounting. Practical sessions provide the applications of mathematics on case studies and example while lecture-based workshops provide guided problem-solving practice. Lab sessions offer hands-on experience with financial data and tools such as Excel to apply mathematical methods to real-world financial problems. These computer lab sessions will be delivered via the virtual PC Lab solution Apporto through Teams. This set up has two major advantages. First, it allows to share screens and as such fosters collaboration, as it is designed to deliver a PC Lab style PC desktop to a student via a web browser from wherever they are and on whatever machine (even a tablet) they choose, meaning all users benefit from a consistent experience. But more importantly this configuration will allow to record the computer lab sessions and will provide the possibility for the student to rewind the session, which is otherwise not feasible in a “normal computer lab”. This multi-modal delivery approach caters to a variety of learning styles and supports inclusive learning.

To support student learning and engagement, the module incorporates formative elements such as worked examples, structured exercises, and drop-in sessions. These are complemented by opportunities for peer interaction and digital support materials via the virtual learning environment.

The assessment strategy combines formative and summative assessments. Regular homework problem sets provide opportunities for ongoing skill development and self-assessment. A mid-semester test will make sure that students understand and have a good command of all basic mathematical principles, as these will be used in all modules thereafter.

The final examination assesses students’ comprehensive understanding and their ability to independently apply all methods covered during the course. All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs.

Overall, this module is closely integrated into the programme’s broader strategy by fostering core numeracy, analytical thinking, and applied data skills—key competencies for both academic progression and employability in the finance sector.

ACFN4151: Foundations of Accounting

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to develop knowledge and understanding of the principles, concepts and techniques of financial accounting, which is the recording and presenting of the financial information of an organisation to external stakeholders such as investors.

The module will enable students to prepare financial statements for sole traders and limited companies by developing students’ basic knowledge and application of financial reporting standards. The module will develop students’ ability to identify and discuss financial reporting treatments in areas such as inventories and property plant and equipment and will develop students’ ability to interpret financial statements.

The module will also introduce students to management accounting, which is the way in which accounting information assists internal stakeholders to make informed decisions, and to plan and control business activities. This module will develop students’ ability to discuss the role of management accounting in areas such as decision making, and the source and use of information and internal control.

The module aims to provide the basics for professional study and exams for membership of the UK professional bodies; it covers an introduction to importance of developing sustainable business practices and the impact of professional ethics.?This module also aims to develop a foundation level understanding of the role of accounting information in the broader economic, social and organisational context.

Educational Aims

Upon successful completion of this module students will be able to:

1. Explain key terms, concepts, and principles of financial reporting, including the business, socioeconomic and regulatory context of financial reporting.?
2. Record common financial transactions using double-entry book-keeping and discuss their financial reporting treatment.
3. Prepare basic financial statements – including the statement of profit or loss and the statement of financial position- for different organisational forms
4. Interpret financial information using ratio analysis to assess performance and financial health.
5. Explain the difference between internal reporting and external financial reporting, and the varying demands of different users of accounting information.??
6. Discuss how accounting information supports internal decision making, planning and control within organisations.

Outline Syllabus

This foundation course provides students with a comprehensive introduction to financial accounting with a particular focus on sole traders, companies, and partnerships. The module provides students with a foundation level of technical knowledge relating to how common transactions are recorded. Students will be able to calculate, from financial transactions and data, amounts to be included in financial statements. This module will enable students to prepare basic financial statements, including the statement of profit or loss and the statement of financial position.

The module covers the financial reporting treatment and technical calculations for a range of financial reporting standards including:

• Income: such as revenue from sales to customers
• Types of expenses: such as marketing and payroll costs
• Assets: such as property, plant and equipment, amounts owed to customers and inventories
• Liabilities: such as loans and amounts owed to suppliers
• Capital: both provided by and distributions to the owners.

Students will explore the framework for financial reporting. This includes the conceptual framework for financial reporting, external sources of financial reporting treatment and the regulatory environment. Ethics and professionalism are important areas of focus.

Practical elements provide students with insights into the understanding of, and interpretation of, financial statements through ratio analysis, including understanding the limitations of financial reporting.

Students will also explore the differences between financial accounting for external reporting purposes, and management accounting for internal decision making. This module will enable students to understand and explain the role of management accounting in internal decision making, including the implications of sustainability, strategic and operational factors.

Assessment Proportions

The module aligns with the programme by providing students with a range of technical knowledge and skills to respond to future situations which may arise in their careers in accounting and finance. It also helps students understand the role of professionals in a socio-economic context and reflect upon the importance of responsible governance and sustainable practices, helping develop professionals who are ethical and responsible citizens.

This module will be taught using lectures, practical sessions, and workshops. Financial accounting requires a significant amount of technical content which will be delivered using lectures. Students also need to develop the skills to apply their technical knowledge to a range of scenarios – the practical sessions will allow students to engage with financial reporting scenarios, and other and real-life scenarios to explore the topics in greater detail. Workshops will then provide students opportunity to apply their technical knowledge to a greater range of scenarios.

Students will be required to complete at least fortnightly quizzes to receive timely feedback on their progress.

The coursework on this module will consist of a testand an exam.

All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs.

For those students who wish to pursue a career as a professional accountant, the module provides some exemptions from core professional exams.?

ACFN4202: Data Analytics for Accounting and Finance

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to equip students with statistical and econometric foundations for data analysis by combining theoretical knowledge with practical data analysis and visualisation skills. It covers core econometric techniques, focusing on linear regression and basic time series concepts, both of which are fundamental to a broad range of financial and accounting applications.

Students will gain hands-on experience employing modern programming languages such as Python and R to estimate and evaluate relevant models using real-world data. In addition to technical proficiency, the module fosters essential transferable skills such as critical evaluation of models, data analysis and interpretation, effective communication and teamworking, group project management and scientific report writing.

Educational Aims

Upon successful completion of this module students will be able to…

1. Explain the principles of Ordinary Least Squares (OLS) and maximum likelihood estimation, and their application in financial modelling.
2. Estimate and evaluation multivariate regression models, assess goodness of fit, and compare alternative model specifications.
3. Describe the key characteristics of financial time series data, including return distributions, volatility and serial dependence.
4. Apply univariate time series models to financial data and estimate their parameters using appropriate techniques.
5. Use programming languages (Python and R) to perform regression analysis, timeseries modelling and data visualisation for structured and unstructured datasets.
6. Demonstrate effective communication and teamwork in project planning, task allocation, and the preparation of scientific reports.

Outline Syllabus

This module introduces key econometric techniques used in accounting and finance, focussing on the linear regression model. The module begins with an introduction to econometrics and various data types relevant to empirical analysis. Students will then explore multivariate regression models to examine relationships within data. They will learn how to estimate these models using the Ordinary Least Squares (OLS) method, assess the properties of OLS estimators, and carry out hypothesis testing to evaluate statistical significance. Students will also be introduced to diagnostic tools for identifying issues such as heteroscedasticity or autocorrelation, along with appropriate corrective measures. Theoretical concepts are consistently reinforced through empirical applications using real-world data from both developed and emerging markets, implemented in Python and R.

Then students will then cover key time series concepts necessary for understanding and modelling financial time series data, including distributional properties of returns and their time series dependence. Students will then explore univariate time series analysis, which is key for forecasting individual stock returns using various time series models.

The module also provides hands-on practical experience in Excel and SQL together with Python and R, allowing students to handle, manipulate and visualize large datasets, as well as manage databases, including core financial databases such as WRDS, CRSP and Datastream. By the end, students will feel confident using a wide range of statistical and visualization tools to analyse data and solve real-world problems.

Assessment Proportions

Throughout the module, emphasis is placed on integrating theoretical quantitative knowledge with practical application following a modern and inclusive teaching, learning and assessment approach. By working with real-world data and industry-standard programming tools, students will build a strong foundation in econometrics for accounting and finance through both conceptual understanding and hands-on experience, equipping them with essential skills for econometric analysis in contemporary accounting and finance environments

Traditional lectures introduce core concepts and are complemented by self-directed homework exercises, which students are expected to complete independently, and will be solved at the end of the block/topic in a lecture-based workshop with Q&A. These homework exercises encourage students to integrate and consolidate the taught material continuously, ensuring better learning outcomes. Lecture-based workshops with Q&A sessions should encourage proactive student engagement through discussion of issues they may have encountered while completing the homework.

Computer lab sessions using industry-standard programming languages such as Python and R to visualise and analyse global market data will help bring the theory to live and prepare students for an international career in the finance sector. These computer lab sessions will be delivered via the virtual PC Lab solution Apporto through Teams. This set up has two major advantages, i) it allows to share screens and as such fosters collaboration, as it is designed to deliver a PC Lab style PC desktop to a student via a web browser from wherever they are and on whatever machine (even a tablet) they choose, meaning all users benefit from a consistent experience, but more importantly ii) this configuration will allow to record the computer lab sessions, which is of utmost importance, as it provides the possibility for students to rewind the session, and also to come back to at Level 5 and 6 to refresh their memory when other modules will continue extending these basics programming skills.

The assessment strategy combines formative and summative assessments. Regular homework problem sets provide opportunities for ongoing skill development and self-assessment. A mid-semester group coursework project, including a student peer evaluation process, encourages collaboration and the integration of knowledge across topics. Introduced early in the term, it promotes purposeful learning and provides valuable feedback ahead of the final examination.

The final examination assesses students’ comprehensive understanding and their ability to independently apply all methods covered during the course. All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication.

ACFN4211: Foundations of Financial Markets, Securities and Institutions

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module introduces students to the main areas of financial trade that occur; stocks, bonds, money markets, currencies, commodities, crypto investments/tokens as well as futures and option payoffs on the above. It describes the financial landscape and motivates why institutions and individuals invest in and trade each of the assets. It discusses well run versus dysfunctional markets with cases on regulation and fraud.

Students will gain hands-on experience using key financial data depositories and working with real-world financial data. In addition to the core knowledge the module fosters essential transferable skills such effective communication and teamworking, group project management, and scientific report writing.

Educational Aims

Upon successful completion of this module students will be able to…

1. Explain the societal impacts of financial markets, including the roles of primary (e.g. IPOs) and secondary markets (e.g., decentralised and token-based systems).
2. Describe how key market participants - buyers, sellers, intermediaries, brokers, and market makers – interact to complete financial transactions.
3. Use financial data to calculate mean and variance, assess asset risk, and construct portfolios to explore diversification benefits across asset types.
4. Calculate and interpret capital and total returns on financial indices to evaluate investment performance.
5. Discuss the conditions under which markets may exhibit characteristics of the “Efficient Markets Hypothesis”.
6. Explain the origins and limitations of regulation across different markets, including the banking system.

Outline Syllabus

The course starts with a history of trade; from ethnological to economic reasons, from bilateral barter through to money, and from informal exchange to over the counter to online systems. It describes how these systems evolved and interact, both within and between countries and economic currency zones. It describes the evolution of the parties concerned; their motivations and growth patterns and the context in which these entities exist and compete.

Special consideration is given to the role of money and the central bank in each economic zone, allowing students to understand how their own personal resources are, and are not, protected by governments. Default and fraud situations are studied so as to anticipate and prevent their future potential impact.

It briefly shows platforms where trade prices are brokered/recorded and how to access these using standard software in order to calculate measures of return and risk. Key financial databases including Datastream, WRDS, TAQ and CRSP will be covered, and several different assets are then categorized by their financial risk characteristics; additionally diversified portfolios of different combinations (these content elements are assessed by coursework. Students will study several different financial indices, calculating both their capital gain and total rates of return.

Short case studies in each market take an opportunity to show times when markets work well by adapting to new information and conditions and also times when information asymmetry, market manipulation or fraud have caused markets to fail in their societal function of allocating an up to date and representative cost/benefit signal on the traded items.

The positive and negative impacts of regulation are discussed, and students will be required to review the trade-offs that exist between free and controlled marketplaces. Finally, it discusses new distributed and unregulated forms of finance and trade, touching on the future of money, crypto and token-based systems; their origin, development, strengths/weaknesses and failures including due to fraud or default.

By the end students should understand the implications and weaknesses of the “Efficient Markets Hypothesis” and how to assess it in several different market settings

Assessment Proportions

The module will be delivered through four hours of contact and lecture material for most of the weeks in the semester accompanied by book chapter and other readings.

The course is split into two parts. The first part covers the early concepts, data and calculation whilst the second part the later concepts and case studies. The workshops will be split into problem-solving sessions based on the material taught in lectures and the 'Commercial Awareness' sessions, i.e. a discussion about what is currently happening in today's financial markets. The approach intends to connect the module directly with the real financial world, fit with an ethos of achieving innovation, excitement and inspiration, allowing students to further study as they wish, and raise the number of successful career outcomes for the students.

The group coursework assessment is designed to test students’ ability to work with data (formative and transferrable skills). This coursework is supported by computer workshops giving access to programming languages and data platforms for the financial data. Lancaster University traffic light system of Generative AI use in assessment will be adhered to.

As such the module’s assessment strategy combines both formative and summative elements. The homework exercises allow students to track their progress and receive feedback throughout and the group coursework project encourages collaboration and practical application of their knowledge.

The final exam assesses students’ comprehensive understanding of the material covered. All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication.

ACFN4295: Foundations of Accounting and Finance

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to introduce non accounting and finance students to the foundational principles of financial accounting, management accounting, and managerial finance – three essential topic areas in business decision making. It is designed to build core competencies in preparing, interpreting, and using financial information for both internal and external decision-making.

Educational Aims

Upon successful completion of this module students will be able to:

1. Describe the roles of financial and management accounting, and financial management, explaining their respective purposes, users, and contributions to organisational planning and decision-making.
2. Prepare and interpret the financial statements - including the Statement of Financial Performance and the Statement of Financial Position – and explain how external stakeholders use this information.
3. Calculate and interpret basic financial ratios to assess organisational performance and support decision-making by internal and external stakeholders.
4. Apply management accounting techniques, such as cost-volume-profit analysis, to support managerial decision-making.
5. Explain key principles of financial management, including time value of money, and use investment appraisal methods such as Net Present Value (NPV) and Internal Rate of Return (IRR).

Outline Syllabus

This module offers an integrated introduction to financial accounting, management accounting and managerial finance for managers – three essential topic areas in business decision-making.

The module begins by exploring the differences between financial and management accounting and introduces students to the main users and purposes of accounting information. In financial accounting, students will learn how to prepare and interpret core financial statements, including the statement of financial position and the statement of financial performance. They will examine the accounting equation, principles of revenue and expense recognition, and the distinction between cash and accrual accounting. The module then introduces the use of basic financial ratios for analysing company performance.

In the management accounting section, the module shifts focus to internal decision-making, exploring cost behaviours, classifications, and the application of cost-volume-profit analysis. Students will consider the role of relevant costs and revenues in supporting short-term and strategic decision-making.

The finance part of the module introduces students to the fundamentals of financial management, including the time value of money, discounted cash flow valuation, and investment appraisal techniques such as Net Present Value (NPV) and Internal Rate of Return (IRR). Students will also explore the principles of financial planning and the evaluation of capital investment decisions, including scenario and sensitivity analysis.

Overall, the module provides students with the tools to understand, use, and critically assess financial information in a range of business contexts, equipping them with essential knowledge and skills for both academic progression and future professional roles.

Assessment Proportions

The learning, teaching, and assessment strategy for this module is designed to ensure that students gain a strong foundation in financial and management accounting and managerial finance for managers through active engagement, applied practice, and reflective learning. The module is delivered through a blend of weekly 2-hour lectures and accompanying lecture-based workshops, which provide structured opportunities for students to apply core concepts to real-world scenarios. This practice-based approach helps students consolidate theoretical knowledge through hands-on problem solving, fostering both understanding and transferable skills.

To support inclusivity and flexible learning, the module adopts a blended format, combining face-to-face sessions with weekly directed learning activities delivered online. These include pre- and post-session tasks such as readings and interactive quizzes to reinforce learning at the students’ own pace. All materials are accessible via the university's virtual learning environment to ensure parity of access and support for diverse learning needs. To obtain real-world data, students will also need to have access to databases, as for example Eikon and FAME.

Assessment is aligned with learning outcomes and designed to encourage both formative and summative engagement. A mix of coursework assessment and a final examination allows students to demonstrate their analytical, interpretative, and evaluative abilities across the three core subject areas. Formative feedback is integrated into workshops and online quizzes via Moodle will be used to promote iterative learning.

The module is responsive to ongoing developments in accounting and finance, including the growing influence of emerging technologies. Where appropriate, students will be encouraged to consider how new technologies may be shaping the future of financial reporting, decision-making, and data interpretation. This approach supports the development of critical thinking, digital awareness, and ethical sensitivity. Overall, the module is designed to offer an inclusive, up-to-date, and pedagogically sound learning experience for all students.

All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs.

ACFN5101: Econometrics for Finance

• Terms Taught: Michaelmas 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of probability, descriptive statistics and inference (Multivariate Regression)

Course Description

This module aims to provide students with a solid foundation in econometrics for finance by combining theoretical knowledge with practical data analysis skills. It covers core econometric techniques, focusing on linear regression and binary choice models, both of which are fundamental to a broad range of financial applications. Students will gain hands-on experience employing modern programming languages such as Python and R to estimate and evaluate these models using real-world data sourced from key databases such as Datastream, CRSP, TAQ and WRDS. In addition to technical proficiency, the module fosters essential transferable skills such as critical evaluation of models, data analysis and interpretation, effective communication and teamworking, group project management, and scientific report writing.

Educational Aims

Upon successful completion of this module students will be able to...

1. Demonstrate an understanding of two popular estimation methods - Ordinary Least Squares (OLS) and Maximum Likelihood (ML) - and key properties of OLS and ML estimators.
2. Estimate and assess multivariate regression models, including goodness of fit, and compare competing model specifications using appropriate criteria.
3. Apply binary choice models to financial data and interpret the estimated outputs.
4. Conduct hypothesis testing in both linear regression and binary choice models.
5. Perform regression and binary choice analyses using programming languages such as Python and R.
6. Demonstrate effective communication and teamwork by developing project plans, allocating tasks, conducting independent empirical investigations and writing scientific reports.

Outline Syllabus

This module introduces key econometric techniques used in finance, focussing on linear regression and binary choice models. The module begins with an introduction to econometrics and various data types relevant to empirical analysis. Students will then explore bivariate and multivariate regression models to examine relationships within data. They will learn how to estimate these models using the Ordinary Least Squares (OLS) method, assess the properties of OLS estimators, and carry out hypothesis testing to evaluate statistical significance. Students will also be introduced to diagnostic tools for identifying issues such as heteroscedasticity or autocorrelation, along with appropriate corrective measures. Theoretical concepts are consistently reinforced through empirical applications using real-world data from both developed and emerging markets, implemented in Python and R, or other suitable languages. In the second half of the module, students will explore Maximum Likelihood (ML) estimation, a key technique for modelling binary outcomes where OLS is not appropriate. Students will develop an understanding of the theory underlying ML, its estimator properties, and how to perform hypothesis testing within this framework. These techniques will be applied to binary choice models such as probit and logit, with practical examples drawn from real-world datasets. Throughout the module, emphasis is placed on integrating theoretical econometric knowledge with practical application. By working with real-world data from key databases such as Datastream, WRDS, CRSP and TAQ and industry-standard programming tools, students will build a strong foundation in econometrics for finance through both conceptual understanding and hands-on experience, equipping them with essential skills for econometric analysis in contemporary financial environments.

Assessment Proportions

The assessment strategy combines both formative and summative elements. Regular homework exercises allow students to track their progress and receive feedback through interactive workshops. A mid-semester group coursework project, including a peer evaluation component, encourages collaboration and the integration of knowledge across topics. Introduced early in the term, it promotes purposeful learning and provides valuable feedback ahead of the final examination. The final exam assesses students’ comprehensive understanding and their ability to independently apply the econometric techniques covered. Throughout the module, students are supported in progressively developing their analytical and practical competencies, ensuring they are well-equipped for application in real-world financial contexts.

ACFN5111: Financial Markets

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Introductory course in Corporate Finance or Financial Management. 

Course Description

This module takes the asset base covered in the Foundations of Financial Markets, Securities and Institutions module (precursor) and shows how its prices are linked and coupled by the concept of “no arbitrage”. It also enlarges assets and trading mechanisms from those considered in the precursor module. It starts with spot deals and moves to forward markets with credit risk and then future markets with margining. It prepares students with an in-depth understanding of the material for either operating in financial markets or using their financial products. Students will develop their quantitative pricing skills and gain an understanding of risk and return measurement. In addition to the rational explanations for investing, the module details popular investments and platforms driven by other considerations including the compliance required for good governance and to avoid fraud.

Educational Aims

Upon successful completion of this module students will be able to…

1. Evaluate beta risk in mutual funds and duration risk on bond portfolios; explain the behavioural and rational motivations behind risk-based investing.
2. Analysis spot asset markets for currencies, equities and bonds etc. and apply market principles to investor cashflows including accrued income and swap structures.
3. Assess the impact of inflation on asset returns and calculate real rates of return in different economic contexts.
4. Apply forward market pricing to multiple asset classes using no-arbitrage spot-carry models, and evaluate credit risk in forwards versus futures margining systems.
5. Critically examine the structure and evolution of trading platforms - including Over the Counter, clearing houses, online exchanges, Defi/Fintech - and their implications for market microstructure.
6. Apply cash and carry pricing models to commodities such as gold, oil etc and distinguish between market conditions such as “normal backwardation” versus “contango”.

Outline Syllabus

This module starts by outlining the historical development of Over-the-Counter markets to Exchange Clearing House, modern Online Platforms and now decentralised finance (DeFi) and Blockchain (FinTech). It continues by evaluating the returns on an equity and a bond portfolio and how the key risk metrics of beta and duration are used to explain how the overall return depends on the components. With other irrational motivations, these sources of return premia are used to explain rational financial behaviour. The module looks at a money market borrowing and lending, implemented with fixed or floating interest rates and how interest rate swaps eliminate expected arbitrage between the two. For a sample US economy, it outlines the difference between nominal and real interest rates and show how US Treasury Inflation Protected Securities (TIPS) offer inflation indexed returns. It also compares the cashflow timing patterns of spot, forward and futures purchases, using these to motivate the potential credit risk from delayed settlement. The module continues by detailing no arbitrage pricing of forward to spot market, cash-carry transactions, and shows the credit risk present. It details trading and clearing mechanisms for futures exchanges, showing how they implement the key mechanisms of margin maintenance. Particularly for commodities it shows how cash, store and carry calculations lead to a term structure of futures prices by delivery term that produce either a Backwardation or Contango picture (oil or gold). The ethics and compliance of sales/brokerage/execution roles are discussed.

Assessment Proportions

This module uses near continuous formative assessment throughout to embed a wide range of fairly straight forward formulae to a diverse range of Financial Market applications. To do this it employs self-test numerical questions online, with progression tracking. It also has one key touch point, the coursework test. Although this is used for summative assessment, this is also intended to act as formative assessment for the remainder of the module. The CW test is both summative and formative. Computer labs session will be run to support the coursework test. The final assessment is an examination which evaluates students’ understanding of all the topics covered in the module.

ACFN5121: Principles of Corporate Finance

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Introductory course in Corporate Finance or Financial Management.

Course Description

This module introduces students to the key building blocks and core principles of corporate finance. It aims to equip students with essential concepts, tools, and analytical skills needed to understand and evaluate a firm’s three fundamental financial decisions: investment, financing, and payout.

Emphasizing the concept of the time value of money, students learn how to value financial securities such as bonds and stocks and apply these principles to capital budgeting decisions for assessing investment projects with various measures such as Net Present Value and Internal Rate of Return.

The course explores various financing options available to firms and examines multiple capital structure theories, enabling students to better understand the factors that influence firms' financing strategies. It also covers different forms of corporate payout and introduces the leading theories that explain firms’ payout decisions, offering a well-rounded perspective on corporate financial decisions. Further, the module deepens students’ understanding of corporate finance by examining corporate governance—how firms are directed and controlled—and its impact on financial decisions. The module also introduces students to the principles of option valuation. It also highlights the growing importance of sustainability in the financial decision-making process, exploring how environmental, social, and governance (ESG) considerations are reshaping corporate finance practices in today’s business environment.

Educational Aims

Upon successful completion of this module students will be able to:

1. Apply the principles of the time value of money to perform investment appraisal and evaluate opportunities using techniques such as Net Present Value (NPV) and Internal Rate of Return (IRR), including basic approaches to incorporating risk.
2. Analyse and value financial securities - including bonds, stocks and options - using appropriate financial models, and interpret their characteristics and implications for investors.
3. Evaluate firms’ leverage, cost of capital and capital structure using established theories and identify the key factors influencing financing decisions.
4. Evaluate corporate payout policies and explain the main determinants of firms’ payout decisions, including dividends and share buybacks.
5. Discuss the role of corporate governance and evaluate how ESG (Environmental, Social and Governance) factors influence firms’ investment, financing and payout decisions.
6. Explain the different forms of market efficiency (weak, semi-strong, and strong) and analyse their implications for corporate financial strategy and investor behaviour.

Outline Syllabus

This module provides students with a comprehensive overview of the core principles and practical applications of corporate finance. Through a blend of theoretical learning and real-world scenarios analysis, students will explore the three key financial decisions firms face—investment, financing, and payout—within the broader context of financial markets, corporate governance, and emerging issues such as sustainability. The module begins by introducing the various types of stakeholders of a firms and discussion the interactive roles of firms and broader financial markets, highlighting the importance of corporate finance consideration in firms’ strategic decision making. This is followed by an introduction to the concept of the time value of money, which serves as the foundation for key investment appraisal measures, including Net Present Value (NPV), and Internal Rate of Return (IRR). Students will also explore how risk can be incorporated into capital budgeting decisions within the time-value of money framework. The concept of time-value of money will be applied to the valuation of financial securities, such as bonds and stocks, which are important in guiding corporate financing strategy. Students will learn how to examine firm’s leverage from their financial statements, how to calculate a firm’s cost of capital, and evaluate the determinants of firms’ capital structure, alongside the effects of various capital structure decisions under different assumptions. Core capital structure theories, such as the trade-off and pecking order theories, will be discussed in the context of real-world financing choices. The module also explores various forms of corporate payout, such as dividends and share buybacks, alongside the economic and strategic motivations behind these decisions. Relevant theories of payout behaviour will be introduced and critically evaluated. Different forms of market efficiency and the corresponding implications on firms’ financial decisions will be discussed. Students will also study corporate governance, examining how ownership structures, board composition, managerial incentives, and agency problems influence corporate finance practise. The growing impact of ESG (Environmental, Social, and Governance) on firms’ financial policies, corporate governance structure, and corporate decision-making is addressed alongside various topics.

Assessment Proportions

The assessment strategy is designed to promote both analytical thinking and applied financial analysis skills. Students will complete a mid-term test, which will, among others, require application of investment appraisal, valuation or capital structure theories. This task assesses students' ability to apply financial theories to real-world scenarios and demonstrate independent problem-solving skills. The mid-term assessment allows students to consolidate their learning and receive early feedback on their progress. The final assessment is an in-person examination testing students' comprehensive understanding of module content.

ACFN5151: Financial Reporting

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of introductory Accounting

Course Description

This module aims...

1. To develop a comprehensive understanding of the principles and concepts underpinning financial reporting for publicly listed companies in adherence to International Financial Reporting Standards (IFRS), focusing on the technical aspects of the accounting process and the key accounting regulations.
2. To enable critical engagement with the regulatory framework for financial reporting, including the role of the IFRS Foundation, the standard-setting process, and the conceptual framework that guides the preparation of financial statements.
3. To examine the recognition, measurement, and presentation of key financial statement elements, such as property, plant and equipment, intangible assets, inventories, and financial instruments, and to understand their implications for accurate and transparent financial reporting.
4. To enhance the ability to analyse the impact of management judgment and discretion on financial reporting, including issues related to impairment, provisions, earnings management, and segmental reporting, and to understand the ethical implications of these decisions.
5. To develop an understanding of the application of IFRS standards in specific areas such as cash flow statements, revenue recognition, leases, and sustainability disclosures, and to critically assess the impact of these standards on financial reporting practices.
6. To provide students with the knowledge and analytical skills to critically evaluate the role of professional ethics in accounting practices and to understand the ethical dilemmas that may arise in the context of financial reporting and the enforcement of accounting standards.
7. To develop students’ ability to apply theoretical knowledge to practical scenarios, analysing case studies and real-world examples to assess the broader implications of financial reporting choices, including the impact on stakeholders and the economy.
8. To enable students to understand the evolving landscape of financial reporting, including the increasing importance of digitalization, sustainability disclosures, and the integration of new IFRS standards for sustainability reporting.

Educational Aims

Upon successful completion of this module students will be able to…

1. Explain the key principles and concepts of financial reporting under International Financial Reporting Standards (IFRS), including the regulatory framework and the roles of the IFRS Foundation and IASB.
2. Apply the IFRS conceptual framework to the recognition and measurement of financial statement elements , and critically assess how these principles influence reporting practices.
3. Recognize and measure key financial statement items - such as property, plant, and equipment (PPE), intangible assets, inventories, investment properties, and financial instruments - in accordance with IFRS.
4. Evaluate the impact of management judgment and discretion on financial reporting decisions, including areas such as impairment, provisions, revenue recognition, and earnings management, using ethical and professional reasoning.
5. Prepare and analyse cash flow statements, ensuring appropriate classification , and interpretation of financial data across business segments and activities.
6. Critically evaluate the recognition, measurement, and presentation of financial instruments, including compound instruments and assess their implications for financial decision-making and stakeholder communication.

Outline Syllabus

This module provides an in-depth exploration of financial accounting and reporting practices for publicly listed companies, focusing on the principles and challenges of preparing high-quality financial information in a regulated environment. Students will develop both technical skills and critical awareness by engaging with key issues such as the purpose of financial reporting, the role of conceptual frameworks, and the ethical responsibilities of accounting professionals. Central to the module is the question of how regulatory requirements and management judgment influence the content and presentation of financial statements. The module covers the preparation and analysis of key financial reports, including the statement of cash flows and the recognition and measurement of assets including PPE, intangible assets, and investment property. Topics include asset impairment, depreciation, amortisation, and valuation techniques, as well as the classification and reporting of inventories and assets held for sale. Students will also explore the recognition of revenue, the measurement and disclosure of provisions and liabilities, and the treatment of financial instruments. In the later weeks, the module addresses more advanced topics including the calculation and interpretation of earnings per share, the structure and presentation of published accounts, and the ethical and practical issues surrounding earnings management. Students will examine the reporting of business segments, lease transactions, and the emerging area of sustainability-related disclosures. While the module involves technical accounting processes, it consistently emphasises the importance of transparency, accountability, and ethical considerations in financial reporting. By the end of the course, students will be equipped to analyse and interpret financial reports, assess the implications of accounting choices, and critically evaluate the role of regulation and judgment in shaping financial information presented to stakeholders.

Assessment Proportions

Formative Assessment – Short weekly quizzes designed to test students’ understanding of key concepts and IFRS standards. These will be graded and provide instant feedback, helping students to identify areas for improvement. Summative Assessment – Designed to test both technical knowledge and the ability to critically analyse and apply financial reporting concepts.

• Mid-term Test (25%) - An open book written exam focused on the technical aspects of financial reporting under IFRS, which will be provided to students at the beginning of the day and will have to be handed in at the end of it.
• Final Exam (75%) - A comprehensive closed-book written exam that requires students to apply the concepts learned throughout the course.

ACFN5182: Business Law

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of Introductory Accounting

Course Description

This module aims to enable students to develop knowledge and understanding of the legal framework relevant to businesses. This includes knowledge of the English legal system, contracts, tort of negligence, business structures and the role and duties of officers such as directors. This also includes knowledge of range of relevant laws in areas such as insolvency, corporate and fraudulent behaviour, anti-money laundering regulation, bribery and employment law. This module aims to enable students to develop the ability to apply their knowledge of law to a range of scenarios and to advise on a range of remedies, outcomes and implications. The module aims to provide the basics for professional study and exams for membership of the Accounting professional bodies and covers an introduction to importance of developing sustainable business practices and the impact of professional ethics.? For those students who wish to pursue a career as a professional accountant, the module may provide some exemptions from core professional exams.??

Educational Aims

Upon successful completion of this module students will be able to:

1. Analyse the principles of English law and the structure of the English legal system, including sources of law and the role of legal institutions.
2. Explain the nature and formation of contractual agreements, agency relationships and the legal consequences of negligence in business contexts.
3. Assess the legal implications of incorporation, including the roles and responsibilities of shareholders and directors, and the impact of insolvency law on corporate entities.
4. Identify and appraise instances of fraudulent and criminal behaviour in business, and examine relevant legal responses.
5. Apply employment law and other relevant legal principles to professional scenarios, demonstrating legal reasoning and problem-solving skills.
6. Discuss contemporary legal issues affecting businesses, and communicate legal arguments effectively.

Outline Syllabus

Areas of law covered include:

• English law: the relationship between civil and criminal law; the structure of the English system of courts; sources of English law; and the interaction between sources of law and ethics.
• Contractual agreements: essential features of a legally binding contract and contract formation; enforcement of contracts; termination of contract; and remedies
• Agency: the role of agents and their authority to enter into contracts.
• Negligence: instances and consequences of negligence.
• Business structures: differences between unincorporated businesses (sole traders and partnerships), limited liability partnerships and companies; company formation including a company’s constitution and administration; the duties of directors and the role of company officers, including the company secretary; meetings and resolutions; the roles of shareholders, including minority shareholders; and the features of share capital.
• Insolvency: the nature of fixed and floating charges; company termination including administration and liquidation, and the rights of creditors and employees; and bankruptcy.
• Fraudulent and criminal behaviour: offences and consequences including under anti-money laundering regulation, bribery, fraud and the implications of fraudulent trading and insider dealing; and protection from dismissal where accountants raise concerns about malpractice in the workplace.
• UK laws and regulations: UK General Data Protection Regulations; and intellectual property and protections.
• Employment law: the features of employment and the implications of employment status; features of contracts of employment; termination of employment contracts and types of dismissal (unfair and wrongful dismissal) and remedies; statutory redundancy; employment law including the Equality Act 2010.

Assessment Proportions

• Coursework test (in person, exam conditions) (25%)
• Exam (in person, closed book) (75%)

ACFN5201: Financial Time Series Analysis

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of Econometrics, Multivariate Regression, Inference

Course Description

This module aims to equip students with both theoretical knowledge and practical skills for working with real-world financial data. The module provides students with the tools to model and forecast complex financial time series data, including individual asset and portfolio returns, as well as to measure and forecast volatility, which is essential for effective risk management. The module develops students’ capacity to implement advanced forecasting techniques using modern programming languages such as Python and R, while fostering transferable skills such as critical model evaluation, research methods in finance, data analysis, effective communication within a group and scientific report writing. This module combines lectures with practical lab work to develop students' analytical skills. Students will learn to evaluate and select appropriate financial models for specific problems. They will also build practical skills to address challenges posed by noisy market data, enhancing their ability to make sound financial decisions.

Educational Aims

Upon successful completion of this module students will be able to…

1. Analyse the key characteristics of financial time series data, including return distributions, volatility clustering, and serial dependence – and evaluate their implications for modelling and forecasting.
2. Estimate and compare univariate and multivariate time series models, assessing model fit and selecting appropriate specifications for financial applications.
3. Forecast financial returns and evaluate forecast accuracy using rigorous econometric techniques and performance metrics.
4. Implement and critically appraise various volatility modelling techniques, including GARCH-type models and realised volatility measures derived from high-frequency data.
5. Apply programming tools (such as Python and R) to conduct empirical analysis of financial time series data, supporting risk management and investment decision-making.
6. Demonstrate effective communication and teamwork by planning and conducting empirical investigations and integrating findings into a coherent scientific report.

Outline Syllabus

The module consists of a mixture of lectures and practical lab sessions that will allow students to empirically implement all the analytical tools discussed during lectures using real-world data with Python and R, or other suitable languages. Each topic will be covered in several lectures, followed by a lab session in which students will apply acquired knowledge directly to real world financial data. The empirical analysis will be conducted using data from global financial markets, including developed and emerging markets. The module will start with an introductory lecture covering the key concepts necessary for understanding and modelling financial time series data, including distributional properties of returns and their time series dependence. Students will then explore univariate time series analysis, which is key for forecasting individual stock returns using various time series models. Students will learn how to estimate ARIMA models, forecast stock returns and evaluate the quality of these forecasts. Over the following weeks, participants will build upon the knowledge of univariate time series analysis and integrate this knowledge for modelling and forecasting the joint dynamics of multiple time series via, for example, vector autoregression and vector error correction models. These types of models are essential for forecasting portfolio returns and portfolio risk management. The second half of the module will be devoted to volatility modelling and forecasting. Students will learn and implement GARCH-type models using daily stock returns, before moving to the most advanced techniques using intra-day returns. Based on second-by-second data from the TAQ database, students will implement realized volatility models and learn how these estimates can be used for risk management. Throughout the module, students will develop critical thinking skills while evaluating competing models, selecting the most appropriate analytical techniques for specific financial problems, and learning to overcome the challenges faced by practitioners working with noisy, real-world financial data obtained from core financial databases including TAQ, OptionMetrics, WRDS, Datastream, CRSP and Thomson Reuters.

Assessment Proportions

The assessment strategy combines formative and summative assessments. Regular homework problem sets provide opportunities for ongoing skill development and self-assessment. Solutions are provided online and subsequently discussed during interactive sessions, creating a supportive learning environment that accommodates different learning paces. The mid-semester group coursework serves multiple purposes. It encourages collaborative learning, promotes knowledge integration across topics, and provides timely feedback that students can apply before the final examination. The final examination assesses students’ comprehensive understanding and their ability to independently apply all methods covered during the course.

ACFN5231: Foundations of Asset Pricing

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of Principles of Corporate Finance/Financial Management

Course Description

This module aims to provide students with a solid foundation in the main areas of financial theory necessary for informed financial decision-making. These areas include time value of money, risk-return trade-offs, decision-making under uncertainty, utility theory, and equilibrium asset pricing, including mean-variance analysis and the Capital Asset Pricing Model (CAPM). The module further aims to give students a deep understanding of how these concepts are reflected in real financial markets, as well as introduce them to practical tools for stock-returns analysis using modern programming languages (such as Python and R) and real-world data. By integrating theoretical concepts with hands-on application using financial databases, the module will develop students’ ability to make investment decisions and understand asset pricing mechanisms in modern financial markets.

Educational Aims

Upon successful completion of this module students will be able to…

1. Interpret the time value of money and implement discounted cash flow techniques to support investment decision-making.
2. Apply utility theory and consumption-based asset pricing models to evaluate decisions under risk and understand the determinants of asset prices and risk premiums.
3. Analyse the principles of no-arbitrage, market completeness, and risk-neutral pricing, and evaluate the conditions under which these concepts hold in financial markets.
4. Perform mean-variance analysis of portfolio stock returns and critically assess its relationship with the Capital Asset Pricing Model (CAPM), including the construction of efficient frontiers and optimal portfolios.
5. Use the CAPM to estimate expected returns and evaluate investment opportunities based upon investor risk preferences.
6. Apply programming tools (such as Python or R) programming languages to analyse stock returns data and construct optimised investment portfolios.

Outline Syllabus

This module provides a comprehensive introduction to asset pricing, equipping students with the theoretical and practical tools to understand how financial assets are valued in markets. It begins with the foundational concept of the time value of money, covering discount rates, net present value, and their implications for returns and decisions at different investment horizons. Students explore the relationships between risk and return, illustrated by real-world examples of historical returns of risky portfolios. The module introduces students to the concept of decision-making under uncertainty, risk aversion, and utility functions, examining how individuals make financial choices. The next core topic of the module is portfolio theory. In this regard, students will learn the key principles of portfolio construction, minimum variance portfolios, the mean-variance frontier, and efficient portfolios with risky and riskless assets. The Capital Asset Pricing Model (CAPM) is examined in detail, enabling students to estimate expected asset returns. The module also illustrates how sustainability considerations are integrated into modern investment decisions, highlighting their growing influence on asset pricing. Practical exercises using Python and R, or other suitable languages, will allow students to apply all the theoretical concepts covered during the module to real financial markets using financial data from sources such as CRSP, Compustat, TAQ, WRDS and RepRisk. By the end, students will understand key asset pricing models, their assumptions, and limitations, enabling informed investment decisions in different market conditions.

Assessment Proportions

The assessment strategy is tailored to the module structure. An in-class mid-term test emphasizes understanding of the theoretical principles of asset pricing, while the final written exam at the end of the semester places greater emphasis on critical evaluation and interpretation of the application of models to real-world financial decision-making problems. This progressive assessment approach allows students to build confidence with foundational concepts before tackling more complex applications. Formative assessment opportunities are provided through regular problem sets and lab exercises, with detailed feedback available during workshop sessions. This continuous feedback cycle helps students identify areas for improvement before summative assessments.

ACFN5295: Intermediate Accounting and Finance

• Terms Taught: Lent/Summer 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

The module aims to enhance students’ knowledge of accounting and finance. It will introduce principles of financial accounting in limited liability companies. The students will explore the valuation of inventory and non-current assets such as machinery, buildings, and land, which are critical for understanding the financial statements. This module equips students with an understanding of various costing methods, pricing strategies, and the application of budgetary control within the strategic decision-making process. Students will also learn how to use the concept of time value of money to value financial assets and will examine the relationship between risk and return by introducing the ideas of diversification. This module will allow students to understand the nuanced context of company financial structures and decision-making from investors' and financial managers' points of view.

Educational Aims

Upon successful completion of this module students will be able to…

1. Interpret and evaluate financial statements to assess an organisation’s financial performance and position, including the impact of non-cash expenses such as depreciation and provisions.
2. Apply cost accounting methods - such as marginal, absorption, and activity-based costing - to support decision-making in pricing, budgeting, and cost control.
3. Construct and analyse budgets and variances to evaluate organisational performance and support effective financial planning and control.
4. Explain and evaluate the structure and functioning of financial markets and how firms raise capital through debt and equity instruments.
5. Calculate market prices of bonds and equities and assess the key features of debt and equity financing in relation to corporate financial strategy.
6. Analyse the importance of diversification and the role of systematic risk in determining investment returns and portfolio performance.

Outline Syllabus

The content on this module will be drawn from three core areas: financial accounting, management accounting, and finance, providing a comprehensive foundation in each discipline. The financial accounting component of the module introduces students to fundamental concepts including the valuation of inventory and non-current assets, with a focus on understanding financial statement presentation and the role of depreciation as a non-cash expense. It covers provisions and introduces the structure and characteristics of limited companies. Students will explore cash flow statements and undertake financial statement analysis. The management accounting component examines cost assignment methods such as marginal and full costing, highlighting challenges in overhead allocation and the implications for management decision-making. Further, it delves into activity-based costing (ABC), evaluating its advantages and limitations, and introduces pricing strategies informed by economic theory and developments like lifecycle costing. Budgeting is addressed in detail, covering preparation methods and the function of budgets in organisational control, along with a discussion of traditional budgeting limitations. Finally, this component explores responsibility accounting, standard costing, and variance analysis, including the reconciliation of budgeted and actual performance. The finance part of the module is built on the previously introduced key financial concepts, such as the time value of money, and will teach the students to use them to value financial assets. Keys areas of financial markets including traditional equity, bond and currency markets will be covered as well as modern development such as cryptocurrencies. This component will also examine the relationship between risk and return by introducing the ideas of diversification. Students will explore how investors generate returns by trading financial assets and learn about the financial manager's role in creating value for the investors by studying the concept of cost of capital. As such students will be provided with the essential tools to help businesses plan for a profitable future.

Assessment Proportions

Assessment strategies are aligned with our defined learning outcomes and foster both formative and summative engagement. By incorporating a combination of coursework assessment and a final examination, students will have the opportunity to showcase their analytical, interpretative, and evaluative skills across the three core areas of study. Additionally, formative feedback will be integral to the learning process, provided through workshops and online quizzes to encourage an iterative approach to learning.

ACFN6121: Advanced Corporate Finance

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of principles of Corporate Finance/Financial Management

Course Description

This module aims to develop students' ability to apply advanced corporate finance theories and techniques to complex real-world decision-making processes. It explores advanced techniques that companies use in capital budgeting, including real options, analyses how they design strategic and short-term financing policies, evaluate mergers and acquisitions, integrate corporate governance and sustainability (ESG) factors into financial strategies, manage risks as well as international financial operations. By connecting theory with practice, this module prepares students to engage critically and strategically with key corporate finance challenges in a dynamic global business environment.

Educational Aims

Upon successful completion of this module students will be able to:

1. Analyse and apply advanced capital budgeting techniques, including real options, and financing strategies to inform corporate financial decision-making.
2. Critically evaluate mergers, acquisitions, and corporate control transactions, assessing valuation and strategic outcomes.
3. Assess and integrate Environmental, Social and Governance (ESG) considerations into financial decision-making.
4. Critically evaluate financial risk assessment and management strategies used to enhance shareholder value and organisational resilience.
5. Analyse and manage corporate finance activities in an international context, including cross-border financing and investment.
6. Evaluate the role of emerging technologies (e.g., Natural Language Processing) in advancing corporate financial analysis, reporting and governance oversight.

Outline Syllabus

This module offers an in-depth exploration of advanced topics in corporate finance, equipping students with the analytical tools and strategic frameworks needed for complex financial decision-making in both domestic and international contexts. The curriculum begins with advanced valuation techniques, including capital budgeting under leverage, the Weighted Average Cost of Capital (WACC), Adjusted Present Value (APV) and the Flow to Equity (FTE) method. Students will assess project-specific costs of capital and analyse the impact of different financing policies on valuation. A focus on corporate and real options introduces the application of option pricing theory to investment decisions, emphasizing managerial flexibility in uncertain environments. The module then covers long-term financing strategies, including equity financing through IPOs, SEOs, and private placements, as well as debt financing with attention to financial covenants and leasing alternatives. The costs, structures, and market implications of issuing securities are critically examined. Short-term financial management is addressed through working capital optimization, forecasting short-term financing needs and applying the matching principle. Students will evaluate secured and unsecured short-term instruments. The module also investigates mergers, acquisitions and corporate control, highlighting strategic motivations, valuation of targets, deal structuring, synergies, hostile takeovers and governance implications. Internal corporate governance mechanisms are explored, including board dynamics, executive compensation, shareholder activism and the use of financial and textual data to monitor governance risks. Sustainable finance is examined through the integration of environmental, social, and governance (ESG) factors into corporate decision-making, with a focus on valuation, risk and emerging sustainability-linked financial instruments. Risk management topics include hedging strategies for insurance, commodity, interest rate and currency risks. The module concludes with international corporate finance, addressing exchange rate and political risks, cross-border valuation and multinational financial management. Emerging trends such as Fintech, AI, and natural language processing (NLP) are discussed for their transformative impact on financial practices.

Assessment Proportions

The assessment strategy is designed to promote both analytical thinking and applied financial analysis skills. Students will complete a mid-term test, which will, among others, require application of investment appraisal, valuation or capital structure theories. This task assesses students' ability to apply financial theories to real-world scenarios and demonstrate independent problem-solving skills. The mid-term assessment allows students to consolidate their learning and receive early feedback on their progress. The CW test is both summative and formative. The final assessment is an in-person examination testing students' comprehensive understanding of module content.

ACFN6131: Investments and Portfolio Management

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of introductory Asset Pricing, Portfolio Theory, Risk Return

Course Description

Building upon theoretical knowledge of asset pricing and financial markets acquired by students during the first two years of the programme, this module aims to develop a deep understanding of modern investment and portfolio management practices. It will extend students’ understanding of the complexities of financial risks and risk factors, from classical Fama-French factors to the frontiers of factor investing. Students will gain experience in constructing advanced factors, including betting-against-beta, and learn to apply Natural Language Processing (NLP) for financial analysis. The module aims to develop students’ capabilities to independently construct novel factors, enhancing their scientific creativity. It will also foster their deep understanding of global financial markets through empirical analysis of different markets, including Europe, US, and Asia. This global perspective intends to promote critical thinking and the ability to compare the performance of different portfolio construction techniques in markets with varying structures. Focusing on empirical applications of theories in industry-standard programming languages, the module prepares students for careers as investment and portfolio managers, able to conduct independent analysis of investment risks and factors, effectively collaborate within teams, and clearly communicate their ideas and corresponding empirical evidence in written work.

Educational Aims

Upon successful completion of this module students will be able to…

1. Critically evaluate advanced asset pricing theories, including Arbitrage Pricing Theory and multi-factor models (Fama-French 3 and 5 factor models, Carhart 4 factor model), assessing their relevance and limitations in contemporary investment analysis.
2. Construct and empirically test investment factors (e.g., size, value, momentum, betting-against-beta) using financial databases, applying appropriate weighting schemes and evaluating cross-market validity.
3. Apply Natural Language Processing techniques to extract investment signals from unstructured data and assess their integration into factor-based strategies across global markets.
4. Design and implement portfolio optimization strategies under various constraints, such as short-selling and Environmental, Social and Governance (ESG) considerations, using a mean-variance and multicriteria approaches.
5. Evaluate portfolio performance through rigorous back-testing, calculating ex-post risk measures and analysing the impact of transaction costs in dynamic portfolio models.
6. Create executable code that implements factor construction and portfolio evaluation and collaborate effectively in teams to synthesize complex investment insights into comprehensive written reports.

Outline Syllabus

This module provides students with advanced understanding of modern portfolio theory and investment strategies, with particular emphasis on quantitative methods and their practical implementation. Starting with a critical review of CAPM limitations, students will explore alternative asset pricing frameworks including Arbitrage Pricing Theory, Fama-French multi-factor models (3-factor and 5-factor), and Carhart’s 4-factor model. The module examines various return predictive signals through cross-sectional regressions and portfolio sorts, and their applications across different markets and asset classes. Students will gain hands-on experience with factor construction, beginning with classical size and value factors before exploring the expanding “Factor Zoo”, that includes factors based on past returns, account data, as well as some behavioural considerations. The module further introduces Natural Language Processing techniques for extracting investment signals from unstructured data, such as company statements and analyst’s reports. Portfolio optimization techniques form another core component, from fundamental mean-variance approaches to more complex multicriteria optimization incorporating different constraints, such as limits on individual industries or ESG considerations. Students will learn rigorous back-testing principles and various ex-post risk measures to evaluate strategy performance. Transaction cost modelling will be covered to illustrate real-world challenges of implementing theoretically sound portfolios in practice. Throughout the module, all theoretical concepts will be complimented by practical lab sessions. Here students will implement the discussed approaches using industry-standard programming languages and real financial data. Students will apply these techniques to international markets beyond the US, promoting global understanding of factor performance.

Assessment Proportions

Assessment is aligned with learning outcomes. The group coursework requires students to construct and test Fama-French-Carhart factors in international markets, comparing local factor performance with US benchmarks. This assessment develops collaborative skills while fostering global financial market understanding. The final report also tests students’ abilities to communicate their empirical results in a clear and structured manner. To ensure academic integrity, students will be required to follow the latest university guidance regarding the use of Generative AI in their coursework. The final examination assesses theoretical knowledge of the module’s content, as well as critical thinking via questions related to interpretation of the empirical results or model limitations.

ACFN6151: Advanced Financial and Sustainability Reporting

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Intermediate level Financial Accounting and Reporting, Financial Statements and Disclosure, Accounting Standards.

Course Description

This module aims to develop students’ advanced understanding of corporate reporting and contemporary developments in sustainability and Environmental, Social and Governance (ESG) disclosure. It equips students with the technical competencies required to apply International Financial Reporting Standards (IFRS) to complex transactions and reporting scenarios, and to critically evaluate the role of both financial and non-financial disclosures in assessing corporate performance. The module also develops students’ ability to engage with and synthesise academic and professional literature, encouraging them to question underlying assumptions, regulatory developments, and evolving stakeholder expectations. Students will also hone their skills in constructing clear, evidence-based arguments and to communicate complex reporting issues. As a compulsory final-year module for the BSc (Hons) Accounting and Finance, and BSc (Hons) Accounting and Management programmes, it plays a key role in preparing students for professional careers and further academic study, by fostering technical precision, critical thinking, and reflective judgement in corporate reporting.

Educational Aims

Upon successful completion of this module students will be able to…

1. Account for complex transactions, such as business combinations, share-based payments, and financial instruments, applying relevant standards and professional judgement.
2. Prepare and evaluate consolidated financial statements, assessing financial performance and position in complex organisational contexts.
3. Critically assess the conceptual and regulatory foundations of sustainability reporting, including current debates on standard-setting and corporate accountability.
4. Critically analyse sustainability reporting frameworks and disclosures, demonstrating awareness of their role in communicating non-financial performance and Environmental, Social, Governance (ESG) risks.
5. Synthesize academic and professional literature to critically evaluate the competing approaches to complex financial and sustainability reporting issues.
6. Communicate complex reporting issues effectively and professionally, tailoring structured outputs to diverse audiences and stakeholder needs.

Outline Syllabus

This module offers an in-depth understanding of advanced corporate reporting practices, combining financial accounting with sustainability and ESG-related disclosures. Building on prior financial reporting knowledge, the module examines complex applications of International Financial Reporting Standards (IFRS), equipping students with the skills needed to prepare and interpret corporate reports in increasingly intricate business environments. Students will engage with key areas of advanced financial reporting, such as accounting for employee benefits, share-based payments, and the treatment of financial instruments. A major component focuses on the consolidation of financial statements. Technical competencies will be reinforced through case-based examples drawn from real company disclosures and regulatory guidance. Alongside technical content, the module covers key areas of the rapidly evolving field of sustainability reporting. It examines the rationale for non-financial disclosure and the role of frameworks such as the Global Reporting Initiative (GRI), Task Force on Climate-Related Financial Disclosures (TCFD), and the International Sustainability Standards Board (ISSB) in shaping ESG communication. Students are encouraged to think critically about the scope, reliability, and comparability of sustainability reporting and its role in informing investor and stakeholder decisions. Throughout the module, students will be challenged to engage with academic and professional literature and to critically evaluate how financial and non-financial reporting together shape perceptions of corporate performance, governance, and accountability in today’s business environment.

Assessment Proportions

Assessment is aligned with the module’s dual focus. A coursework assignment will assess students’ ability to critically evaluate sustainability disclosures and frameworks, encouraging synthesis of conceptual knowledge and professional insight. The end-of-term exam will focus on advanced IFRS application, testing technical accuracy, judgement, and the ability to interpret complex reporting situations under time constraints. Lancaster University traffic light system of Generative AI use in assessment will be adhered to.

The module aligns with the programme’s broader strategy by developing both technical expertise and critical, reflective capabilities needed in professional and academic settings. It also supports progression by integrating knowledge from earlier modules and fostering graduate-level communication, judgement, and analytical reasoning.

ACFN6161: Strategic Management Accounting

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Intermediate level Management Accounting and Information Systems. Note this cannot be the first and only Accounting module. It should include some Cost Accounting.

Course Description

This module aims to extend students’ understanding of management accounting, focussing on three distinct approaches to the discipline: conceptual, practical and applied. The conceptual material in the course will seek to develop critical thinking skills for students by inviting them to consider the wide range of philosophical and economic theories that fit with the management accounting paradigm. The practical element of the course will introduce students to new management accounting techniques, building on those encountered in ACFN5261 (or equivalent). These techniques will include the role of uncertainty in management accounting, detailed consideration of performance management, control systems and the use of transfer pricing. The emphasis in learning these techniques will be placed on identifying when and how these approaches can be used to help management make appropriate decisions in a range of contexts, including those relating to the private and public sectors. The applied material will centre on case study classes in which students will be required to engage with case study material and articulate their ideas about how to help the case organisation solve the particular problems that it faces.

Educational Aims

Upon successful completion of this module students will be able to

1. Apply and critically evaluate advanced management accounting techniques in strategic contexts including ethical, sustainability and corporate governance considerations.
2. Analyse and explain the requirements for and use of management control systems, assessing their role in supporting strategic objectives and organisational performance.
3. Select and apply appropriate performance management tools, evaluating their effectiveness in measuring and driving strategic outcomes.
4. Critically assess the role of management accounting in strategic decision-making.
5. Demonstrate independent critical thinking by selecting and applying appropriate management accounting techniques to complex real-world scenarios.
6. Articulate a reasoned argument under time-constrained conditions, communicating strategic accounting insights clearly and professionally.

Outline Syllabus

The module will set out the key themes of strategic thinking as it applies to both private and public sector organisations. This will lead naturally to consideration of the ways in which organisations are structured, how managers and other employees are motivated and the need for reward structures. A central consideration of the module will be the ways in which these reward systems are operated and the management accounting techniques that allow organisations to develop performance measures, including accounting measures, key performance indicators and balanced scorecards. The module will critically consider a variety of costing techniques used by organisations, including Kaizen costing, target costing, lifecycle costing and sustainability management accounting. Consideration will be given to how organisations can estimate costs through cost behaviour analysis and how transfer pricing affects the way in which costs are calculated as well as their influence on management behaviours. The module will explore the role of uncertainty in decision making, including issues relating to the time value of money, the provision of perfect and imperfect information and the use of decision trees. Throughout the module reference will be made to the roles of sustainability and digital innovation in the development of current management accounting practices, including the use of artificial intelligence and blockchain in helping the process of strategic decision-making.

Assessment Proportions

The main themes of the module will be taught using in-person lectures illustrated by practical examples. In support of this the module will use workshops to prepare students for the discursive and quantitative elements of their assessment. The students will take two case classes, using in-depth real-world scenarios to exemplify topics from the early part of the taught sessions, and these will form the basis of the coursework assignment. The assignment will assess the students’ ability to analyse and synthesise the case class material in a coherent and effective manner through a PowerPoint slide deck. Students will attend a workshop designed to prepare them for this activity and specific drop-in sessions will be provided to support the assignment. A dedicated feedback session will also enable students to develop their learning in advance of the exams. In the latter half of the module there will be workshops allowing students to practice their application of quantitative management accounting techniques in advance of their final 2-hour examination.

ACFN6171: Auditing and Control/Accountability

• Terms Taught: Michaelmas
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Financial Reporting (Intermediate level Accounting)

Course Description

This module aims to provide students with an understanding of audit principles, risk assessment, and internal controls, and to develop competencies in the practical application of these areas of expertise. The module is being offered as part of the programme to provide students with a general understanding of the role and activities of an auditor, and how such activities add value to a business.

Educational Aims

Upon successful completion of this module students will be able to…

1. Analyse key business cycles within an organisation, evaluating their implications for audit risk and assurance strategies.
2. Critically assess the role of accounting information systems in supporting reliable financial reporting and effective audit processes.
3. Evaluate the professional responsibilities of auditors, including independence, ethics, and their contribution to corporate governance and accountability.
4. Apply core audit concepts to real-world scenarios, demonstrating how evidence is gathered, evaluated, and reported in compliance with professional standards.
5. Identify and critically evaluate fraud risks, including those emerging from digital technologies and computerised systems and assess appropriate audit responses.
6. Evaluate the impact of technology-enabled audit methods (e.g., big data analytics, blockchain and AI) and construct evidence-based arguments on contemporary auditing issues using academic and professional sources.

Outline Syllabus

The module critically examines how data and information have become central to organizational decision-making and performance; and evaluates the effectiveness and limitations of Accounting Information Systems (AIS) as the backbone for collecting, processing, and communicating financial data. Through data processing and electronic systems, organizations ensure timely, accurate, and relevant information flows to stakeholders, but students will also analyse challenges such as system failures, biases, and data integrity issues. Further, the module requires students to evaluate how documenting systems, including ledger coding structures, support or undermine transparency and control. Effective internal control mechanisms are crucial for organisations, and the module appraises the COSO framework as a model of internal control, exploring both its application and its limitations in contemporary contexts. Students will also consider how such frameworks contribute to wider debates on corporate governance and accountability. The module examines the purpose of an audit and emphasises how audit independence is fundamental to audit credibility. Students will critically assess the audit process, from audit appointment through to audit testing,completion and reporting, and evaluate the suitability of different approaches in varying risk environments. Business operations are typically structured into cycles, including the revenue, expense, conversion, and management cycles. Each carries unique risks and control requirements. Students will analyse risks associated with each cycle and design appropriate audit procedures, applying knowledge from prior financial and management accounting modules. In modern audits, addressing fraud is crucial. Understanding the fraud triangle—pressure, opportunity, and rationalization—helps in preventing and detecting fraud, especially in the realm of computer fraud. Students will critically evaluate the adequacy of the fraud triangle in digital environments and appraise alternative approaches such as AI-enabled fraud detection. Finally, the module explores and critiques emerging technologies like big data and blockchain that are reshaping the future of AIS. Students will evaluate both opportunities and limitations of these technologies and engage with professional debates on how innovations such as blockchain may transform the role of auditors.

Assessment Proportions

Assessment will consist of coursework and a final exam. The coursework is intended for the students to apply their knowledge allowing students to check their understanding and receive timely feedback. The final written examination tests students’ ability to synthesize and articulate reasoned arguments under exam conditions. This ensures assessment is aligned with both practical application and academic rigour. Throughout the module, emphasis will be placed on developing critical thinking, professional judgement, and ethical awareness. Real-world scenarios and examples will be used to highlight the relevance of corporate governance and emerging technologies like blockchain in the audit environment. By integrating theory with practice and using varied assessment methods, the module ensures students can meet all learning outcomes effectively, preparing students for further study and professional roles in accounting and audit.

ACFN6211: Fintech and Market Microstructure

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of introductory Corporate Finance or Financial Management. 

Course Description

This module aims to provide students with a comprehensive understanding of modern financial markets, focusing on the structure, functioning and evolution of trading mechanisms. Covering both traditional market structures and emerging digital finance innovations, the module bridges foundational market microstructure concepts with cutting-edge developments such as algorithmic trading, blockchain technologies, cryptocurrencies, and decentralised finance (DeFi). By integrating theory with real-world applications, it equips students with the analytical tools and practical insights necessary to navigate and contribute to today’s rapidly transforming financial landscape. This module will strengthen students’ capabilities in financial market analysis, preparing them for roles in trading, investment management, and financial technology.

Educational Aims

Upon successful completion of this module students will be able to…

1. Differentiate market structures, order types and trader behaviours in limit order markets and evaluate their implications for price formation and liquidity.
2. Estimate and critically evaluate liquidity measures, applying them to assess market efficiency and trading conditions.
3. Apply economic theories to conduct empirical analyses associated with informed trading, using financial data and appropriate modelling techniques.
4. Evaluate the fundamentals of algorithmic and high-frequency trading and implement basic algorithmic strategies using programming tools.
5. Critically assess blockchain technology and cryptographic principles, distinguishing between blockchain types and analysing their economic foundations and implications for financial systems.
6. Evaluate the principles of Decentralised Finance (DeFi) and digital assets (e.g. cryptocurrencies, stablecoins and central bank digital currencies), analysing their impact on traditional financial markets and regulatory frameworks.

Outline Syllabus

This module explores the structure, functioning, and evolution of financial markets, with a focus on both traditional and emerging trading environments. It begins by introducing key concepts in market design and the variety of structures through which assets are traded, including dealer markets, auction markets, and hybrid models. Students will examine the mechanics of limit order markets, including the role of different order types, liquidity providers, and the dynamics that drive price formation.

A central theme is liquidity—how it is measured, what affects it, and why it is crucial to market efficiency. Students will learn to assess liquidity using bid-ask spreads, market depth, and transaction cost analysis methods such as implementation shortfall. Building on this, the module delves into informed trading and market impact, exploring seminal models like Glosten-Milgrom and Kyle, and discussing real-world practices such as stealth trading and short selling.

The module then shifts focus to the technological innovations reshaping markets. Students will explore algorithmic and high-frequency trading (HFT) strategies, analysing their impact on market quality and fairness. The foundations of blockchain technologies are introduced, providing an essential technical and conceptual basis for understanding digital assets.

In the latter part, the module examines cryptocurrencies as an emerging asset class, evaluating their role in diversified portfolios and the risks they present. Students will critically assess decentralised finance (DeFi) platforms and protocols, exploring their potential to disrupt traditional financial intermediation. Finally, the module addresses the rise of stablecoins and central bank digital currencies (CBDCs), examining their implications for monetary policy and financial stability.

Throughout, students are encouraged to apply analytical frameworks to current market developments, preparing them to engage with both established and frontier areas of financial practice. Throughout, students will engage in hands-on workshops and computer labs using Python and R, or other suitable languages, applying these techniques to real-world tasks. Students will make use of common financial databases such as Trades & Quotes, CryptoCompare, and CoinMarketCap.

Assessment Proportions

This module blends theoretical understanding with practical application, ensuring students are equipped with both the knowledge and skills required in the field. Weekly lectures are structured to introduce key concepts and frameworks, providing the foundational theory necessary to engage critically with the subject matter. These are complemented by interactive workshops and computer lab sessions, where students apply the theories using appropriate tools and datasets, fostering hands-on learning and deeper comprehension.

The assessment strategy, consisting of two equally weighted group coursework assignments, is designed to reinforce collaborative learning and real-world problem solving. By working in teams, students develop essential skills such as communication, teamwork, and project management, in addition to technical competence. The assignments, each consisting of an online-Moodle-submission written group report and a short group presentation, require students to conduct empirical analysis, interpret their findings, and present results effectively, aligning closely with the programme’s emphasis on developing analytical capabilities and professional communication skills. The group presentations, scheduled after the online Moodle submission with all group members required to present, not only help check students’ understanding of their written work and develop their presentation skills but also, together with peer evaluations, mitigate free riding concerns.

Throughout the module, formative feedback is provided in workshops, supporting students’ progress and encouraging continuous learning. This approach ensures that students are not only assessed at the end of a task but are guided and supported throughout their development.

This module aligns with the programme’s broader learning, teaching, and assessment strategies by promoting active engagement, practical application, and the development of transferable skills. It supports the programme's goal of preparing students for professional practice by integrating technical knowledge with collaborative working and effective communication. The combination of theoretical grounding, practical skills, and continuous feedback ensures students graduate with a well-rounded skill set ready for the challenges of the industry.

All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication.

ACFN6221: Banking and Behavioural Finance

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to provide students with a comprehensive understanding of the banking sector by integrating core financial theory with behavioural insights. It introduces the economic functions of banks, including the rationale of their existence in the first place, and their distinction from other financial institutions, while exploring how psychological and organisational factors influence banking operations and perceptions. The module develops students’ ability to critically assess risk management practices, regulatory frameworks, and monetary policy tools, including their behavioural dimensions (e.g., psychological factors, cognitive biases such as overconfidence, loss aversion and herding behaviour). Through a blend of theoretical foundations and real-world examples, students will engage with topics such as Value-at-Risk, central bank policy, and financial regulation, which complement the skill set of students graduating in areas related to financial economics. The module also encourages critical thinking about current developments in banking, including the impact of blockchain technologies. In doing so, it fosters key transferable skills in research, analysis, and communication, equipping students to navigate the complexities of modern financial systems and regulatory environments.

Educational Aims

Upon successful completion of this module, students will be able to:

1. Critically analyse the core functions and economic role of banks within financial systems.
2. Evaluate how behavioural factors—such as trust, reputation, and decision-making under uncertainty—shape banking operations, stakeholder interactions and risk perception.
3. Apply and assess key tools of bank management and risk analysis, including Value-at-Risk (VaR), asset and liability management, and financial ratios, to support strategic decision-making.
4. Evaluate the central bank objectives, instruments, and theoretical frameworks and critique how behavioural biases (e.g., overconfidence, loss aversion) influence the effectiveness of monetary policy.
5. Critically assess the resilience and adaptability of the banking sector in response to major economic shocks, such as the COVID-19 pandemic, and evaluate the implications of emerging technologies (e.g., blockchain, digital currencies) on banking structures and customer behaviour.
6. Compare and contrast traditional and behavioural approaches to financial regulation and communicate complex banking and behavioural finance concepts effectively through professional written outputs.

Outline Syllabus

This module provides a comprehensive examination of the banking sector. The objective is to combine core banking principles with key behavioural factors that influence financial decisions. Students will critically justify the need of banks in our financial system and assess how banks operate, evaluate lending decisions, manage risk, and interact with regulatory authorities, while also evaluating how human psychology and decision-making biases might affect banking practices.

The module begins by analysing a simple yet not obvious question: why do banks exist? We will use a benchmark mathematical model to show that, under certain conditions, banks are indeed redundant institutions in the economy. We will then relax these conditions and show how banks can complement the financial system whenever financial markets fail. This will allow students to critically assess the complex interplay between banks and financial markets. Behavioural elements such as trust, reputation, and decision-making under uncertainty will be analysed to highlight their impact on both consumer behaviour and banking operations.

In the second part, the focus shifts to assessing the fragility of banks’ balance-sheets and how and why banks manage risks. Students will evaluate key risks that affect banks’ operations, and the tools used to hedge them, including Value-at-Risk (VaR), asset and liability management, and the use of financial ratios. The module will analyse the behavioural factors that affect risk-taking in banks, such as overconfidence, loss aversion, and the effects of organisational culture on decision-making.

The third part of the module focuses on the regulatory landscape surrounding banking. Students will critically evaluate the role of central banks, with a particular focus on their monetary policy objectives, in ensuring financial stability and supporting economic growth. This section analyses the tools used by central banks to implement policy, such as interest rate changes, and assesses how these decisions can be influenced by behavioural tendencies like present bias and status quo bias. Students will evaluate how these psychological factors affect consumer responses and, in turn, the effectiveness of monetary policy.

Additionally, the module addresses contemporary challenges and opportunities in the banking sector, such as the ongoing impact of the COVID-19 pandemic, the rise of blockchain technology, and the increasing importance of digital banking. Students will gain critical insights into how these developments are reshaping the banking industry and the consumer behaviours that drive these changes.

Assessment Proportions

This module adopts an interactive teaching approach that emphasizes student engagement and active participation through a combination of traditional lectures, group project sessions and computer labs. This module style encourages students to take part in discussions, ask questions, and collaborate with peers to deepen their understanding of the subject matter.

The aim is to foster a dynamic learning environment that supports critical thinking and real-world application.

Students will have access to lecture slides in advance, enabling them to prepare for the topics that will be discussed in class. Lectures will link theoretical concepts to real-world examples, helping students to connect academic material with its practical relevance.

To complement the lecture-based learning, students will attend workshops, computer labs, group project sessions and drop-in surgery session. These sessions provide a supportive environment where students can consolidate their understanding through problem-solving activities, deeper discussions of complex topics, and practical application of tools and techniques introduced in lectures. The aim is to accommodate diverse learning needs and offer timely feedback and guidance throughout the module.

The group project sessions will help to foster team interaction through structured group work and in-class presentations, which not only enhance students' understanding of the material but also help develop essential transferable skills such as communication, collaboration, and problem-solving. By working in teams, students learn to engage with diverse perspectives and deliver coherent, well-reasoned arguments. These activities simulate real-world professional environments, preparing students for the collaborative nature of the modern workplace. The use of real-world data from Compustat Bank and SNL Financials will also help students to improve their analytical and coding skills.

Students will complete a mid-term assessment, consisting of a combination of multiple-choice questions and short numerical exercises. The assessment will evaluate students’ understanding of core banking principles and their ability to apply tools such as financial ratio analysis, Value-at-Risk (VaR), and asset-liability management techniques. It also offers an early opportunity for students to consolidate their learning and receive feedback on their progress in applying both theoretical and practical aspects of banking. The coursework test is both summative and formative.

The final assessment is a comprehensive in-person examination and designed to test students’ understanding of the entire module. All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs.

ACFN6222: ESG, Climate and Energy Finance

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module focuses on key topics emerging on the role of accounting and finance in relation to environmental and social challenges faced in today's society. These include but are not limited to the assessment of the impact of climate change on institutions and asset classes such as climate-related risks (physical & transitional); exploring the upside opportunities from climate change for firms and financial institutions, including firms in the energy sector (both hydrocarbon and renewables); how climate change affects energy markets and the price of electricity in the short-run and long-run; understanding how financial markets can help transfer and hedge climate risks as well as finance the transition to a net-zero economy; and the related regulatory issues including carbon emission schemes, green bonds, sustainable investing approaches, insurance industry considerations. ?

The module provides students with valuable insights, aided by real-life examples and case studies, on the issues of climate and energy finance, organisational sustainability and corporate social responsibility, from different stakeholder perspectives including managers inside the companies and external investors.

Educational Aims

Upon successful completion of this module students will be able to...

1. Critically evaluate key social, environmental and economic/financial risks and opportunities for business resilience, and assess their implications for corporate and investor decision-making
2. Analyse the roles of accounting and finance in embedding sustainability-related impacts and dependencies into strategic planning and reporting frameworks.
3. Evaluate the regulatory landscape for Environmental, Social and Governance (ESG) disclosure, including emerging standards, voluntary frameworks and the role of ESG ratings in shaping market behaviour.
4. Assess the impact of climate change on energy markets, including short and long-run price dynamics, and evaluate how financial instruments can hedge climate risks and support the transition to a net-zero economy.
5. Critically analyse investor behaviour and decision-making in relation to ESG and climate finance, including the use of alternative and green investment vehicles.
6. Evaluate the financial implications of energy transition policies, including carbon taxation, abatement costs, and capital requirements, and apply Net Present Value (NPV) analysis to assess investment viability in green electricity projects.

Outline Syllabus

This module is designed to familiarise students with the key issues that are emerging on the role of finance in relation to environmental and social issues faced in today's society.

Students will develop an appreciation of how climate and energy finance as well as sustainability accounting influence and support decision making for companies and investors. Specifically, the module will provide insights on the complexity that characterises the relationships between accounting, finance, organisations and society and between shareholders and a wide range of stakeholders. Specifically, the module will provide insights on a range of topics including the impact of climate change on institutions and asset classes such as climate-related risks (physical & transitional); the opportunities that climate change presents for firms and financial institutions, including firms in the energy sector (both hydrocarbon and renewables); how climate change affects energy markets and the price of electricity in the short-run and long-run.

The module will also provide knowledge and understanding of ESG investing and climate finance and introduce the stakeholders and institutions which play a role in addressing climate change problems. Relatedly, it will provide insights into the recent developments in alternative investment and portfolio management by considering various green and ethical asset vehicles of investment, such as green stocks, bonds, as well as other financial instruments. Another focus would be the changing regulatory context for companies and investors, including emerging regulations and voluntary sustainability reporting frameworks, as well as critically evaluate the role of ESG (Environmental, Social, and Governance) ratings.

Assessment Proportions

This module adopts a blended learning and assessment strategy, including lectures, practical lectures and computer-based workshops. This design aims to support deep engagement with the climate finance topics and the technical skills to analyse each of these topics.

The lectures will provide an overview of the weekly topics covered on this module and help students to develop sufficient depth of knowledge and understanding of each topic, students will also be encouraged to supplement the lectures with their own reading from the required readings. Strong performance in this module will require students to demonstrate a deeper level of knowledge and understanding than is likely to be achieved through only reading the weekly key readings. This deeper level of knowledge and understanding requires students to read and understand some of the weekly supplementary readings and have a good awareness of the contemporary changing context for business resilience and sustainability, through following quality news media such as the Financial Times, The Guardian and the BBC. The purpose of practical lectures is to provide more opportunities to interact with students and cultivate their interest in engaging with academic and professional literature, which will help to strengthen their understanding and communication skills.

The aim of the computer-based workshops is to help students understand the practical application of module content by placing them in hypothetical organisational settings where decisions need to be made. The purpose is to help students prepare for similar situations upon embarking in their careers by fostering the development of critical thinking, teamwork, and technical skills in a sustainability context and introduce them to core sustainability databases including Compustat and RepRisk. Furthermore, the coursework assessment will follow a format similar to the case studies discussed in practical lectures. Therefore, active engagement with the practical lectures and taking relevant notes will provide students with valuable experience and insight to help them succeed in their coursework assessment. Lancaster University traffic light system of Generative AI use in assessment will be adhered to. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication.

The final exam will assess all the material that students have covered over the module and evaluates their knowledge and understanding comprehensively. All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs. (Please note that Gen AI is not relevant for the final assessment.)

Overall, this module aligns with the programme’s broader strategy by developing both technical expertise and critical, reflective capabilities needed in professional and academic settings.

ACFN6231: Advanced Investments using Machine Learning and AI

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Intermediate level Investments and Portfolio Management / Asset Pricing/ Markets Valuation

Course Description

This module offers an exciting deep dive into modern investment management using the latest developments in machine learning and artificial intelligence (AI) methods. Students will learn about advanced portfolio strategies such as Risk Parity and Insurance Strategies, creating a solid foundation in planning, constructing, and managing diversified portfolios. The module explores how advanced techniques like machine learning and variable selection methods can optimize portfolio performance and enhance decision-making. Practical, hands-on tools using modern programming languages such as Python and R will be introduced, empowering students to implement sophisticated portfolio strategies. By blending theory with real-world applications, this module equips students with the skills to harness cutting-edge technologies and approaches in finance for advanced investment decisions in rapidly evolving financial markets.

Educational Aims

Upon successful completion of this module students will be able to...

1. Critically analyse factor investing approaches and asset pricing anomalies, evaluating their relevance and performance contemporary financial markets.
2. Apply machine learning techniques and variable selection methods including tree-based models and penalized regression, to enhance prediction and decision-making in investment management.
3. Implement sophisticated portfolio strategies using modern programming languages and neural networks architectures, adapting models to diverse investment contexts.
4. Critically compare the effectiveness of different machine learning algorithms for specific investment challenges, assessing trade-offs in accuracy, interpretability, and scalability.
5. Apply hyperparameter tuning techniques to optimise model performance and improve predictive reliability in financial applications.
6. Design and evaluate portfolio backtesting methodologies, conducting robust model validation and performance assessment across varying market conditions.

Outline Syllabus

This module examines the application of modern machine learning techniques to factor investing, blending theory with hands-on practice to enhance quantitative investment strategies. The module begins by critically reviewing the foundations of factor investing and asset pricing anomalies, particularly focusing on empirical challenges like data-mining biases and model instability in financial markets.

The module then introduces machine learning methods designed to refine factor selection and improve return predictability. Penalized regression techniques, such as LASSO and Ridge regression, are presented as effective tools for handling large sets of candidate factors while controlling for overfitting and multicollinearity. Tree-based methods, including decision trees, random forests, and boosting, are then explored for their ability to capture non-linear interactions between factors and uncover complex patterns that traditional models may overlook.

A significant portion of the module is dedicated to neural networks, where students learn to build deep learning models for factor-based return prediction and portfolio construction. We emphasize the practical challenges of applying machine learning in finance, including the importance of validation, hyperparameter tuning, and robust out-of-sample testing to avoid overfitting and ensure performance stability.

Throughout, lab sessions enable students to implement models using real-world financial data from databases such as CRSP, TAQ, Datastream and WRDS, with exercises focused on factor portfolio construction, performance evaluation, and back-testing. Key themes include model validation, the trade-off between complexity and interpretability, and the practical integration of machine learning techniques into factor investing frameworks.

By the end of the module, students will be able to critically evaluate machine learning models in the context of factor investing, implement a range of predictive techniques, and apply rigorous validation methods to design robust, data-driven investment strategies.

Assessment Proportions

This module uses a highly practical teaching and learning approach structured around traditional lectures and practical computer lab sessions each week. This format is specifically designed to accommodate the complex nature of machine learning and AI applications in investment management, allowing students sufficient time to process theoretical concepts before applying them in practice.

The teaching is organized into thematic blocks, each focusing on a specific machine learning methodology (penalized regressions, tree-based methods, and neural networks), with the final block dedicated to validation and portfolio performance evaluation. Each lecture introduces key theoretical concepts and methodological frameworks, while the accompanying lab sessions provide hands-on experience implementing these techniques using Python and R, or other suitable languages, with real financial datasets.

To support student learning throughout the module, drop-in sessions are scheduled at the end of each thematic block. These optional sessions provide additional assistance prior to coursework submissions, allowing students to receive personalized guidance on any challenges they may encounter while learning the material.

The assessment strategy consists of two equally-weighted group coursework assignments, which evaluate students’ ability to apply machine learning techniques to investment problems. Students will conduct empirical analysis, write a report, and present their findings. To ensure academic integrity, students will be required to follow the latest university guidance regarding the use of Generative AI in their coursework. The presentation component will further develop their communication skills in explaining complex quantitative concepts.

This balanced approach to learning, teaching, and assessment ensures students develop both theoretical understanding and practical implementation skills while fostering collaborative learning and communication abilities.

Throughout the module, the equitable and inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs. The module will follow ILSP guidance to ensure all students can fully participate regardless of their background or personal circumstances. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication.

ACFN6251: Financial Statement Analysis

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Year 2 (intermediate level, not intro) Corporate Finance and Financial Reporting

Course Description

This module aims to equip you with the skills to interpret and analyse financial statements for corporate performance evaluation, valuation, and investment decision-making. You will apply financial ratios, cash flow analysis, and forecasting techniques to assess financial health, profitability, and risk. Through real-world examples, you will develop the ability to critically evaluate corporate reports, applying analytical frameworks to investment, credit, and strategic business decisions. The module also explores the growing connectivity between financial and non-financial reporting, highlighting how Environment, Social and Governance (ESG) and other non-financial data are increasingly used to assess corporate performance and long-term value creation.

This module is offered because it develops students the expertise to extract meaningful insights from corporate disclosures, supporting careers in equity research, corporate finance, risk analysis, and investment banking, where financial statement interpretation is a core competency.

Educational Aims

Upon successful completion of this module students will be able to…

1. Critically evaluate how an entity's strategy and business model inform the interpretation of financial statement data and contextualize business valuation.
2. Analyse and assess disconnects between reported financial results and underlying economic activity, reformulating financial statement to distinguish operating from financing activities and evaluating the implications for performance analysis.
3. Compute, interpret, and critique financial ratios from the perspective of a financial analyst, considering the strengths and limitations of ratio analysis in assessing corporate performance.
4. Forecast and justify pro forma financial statements for valuation purposes, synthesising macroeconomic data, industry trends, and company-level metrics.
5. Apply and critically compare valuation techniques (e.g., models based on the discounted cash flow approach, and valuation multiples) to estimate business value and formulate evidence-based recommendations.
6. Evaluate the integration of financial and non-financial reporting, including Environment, Social and Governance (ESG) disclosures, and communicate complex analyses effectively through structured reports tailored to diverse stakeholder audiences.

Outline Syllabus

This module will cover the following topics:

• The general process of financial statement analysis
• Strategic analysis
• Accounting analysis, with the focus on the reformulation of financial statements
• Financial analysis, including ratio analysis and cash flow analysis
• Forecasting: principles and the preparation of simple pro forma financial statements
• Valuation using a variety of techniques
• Using non-financial data, including ESG, in business analysis

Assessment Proportions

This module teaches students how to analyse and interpret financial statements to evaluate a firm's financial health and determine its intrinsic value. Unlike other accounting modules focused on preparing financial data, this one adopts the perspective of the end user, such as an investor or analyst.

Financial analysis and valuation are inherently unstructured tasks without single correct answers. Success depends on the ability to deliver a rigorous, logical, and clearly communicated interpretation of financial data—rather than rote memorization or textbook examples.

Using real financial statements, students will learn to assess a firm’s past performance and forecast its future using financial ratios, forecasting methods, and valuation techniques. The goal is to evaluate profitability, stability, and growth potential to support an investment decision—buy, sell, or hold.

Grounded in established valuation theory, the module emphasizes practical application. It mirrors the work of professional financial analysts who must build compelling arguments to support their investment recommendations.

A key feature of the module is an extended case study, developed across the term. This multi-stage project serves as a model for conducting financial analysis and valuing a real company. Students will present their findings in the form of an analyst’s research report, which constitutes the main coursework assessment. Lancaster University traffic light system of Generative AI use in assessment will be adhered to. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication. All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs.

ACFN6271: Advanced Auditing and Assurance

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Auditing / Control (Intermediate level, not an intro course in Accounting).

Course Description

This module equips final-year Accounting and Finance major students with the advanced technical knowledge, digital competencies and ethical judgement required for complex audit and assurance engagements in a rapidly evolving environment.? It critically examines revised auditing standards, sustainability-assurance requirements and technology-driven techniques (audit-data-analytics, AI, blockchain), developing professional scepticism and clear communication skills.

Educational Aims

Upon successful completion students will be able to...

1. Critically evaluate the current regulatory, professional, and ethical frameworks for audit and assurance, assessing their implications for practice across diverse organisational contexts.
2. Analyse and assess complex engagement risks (e.g., fraud, going concern, climate, and cyber) and design and justify appropriate audit responses.
3. Apply and critically reflect on the use of audit data analytics tools, using them to analyse datasets, identify anomalies, and enhance substantive procedures.
4. Exercise professional judgement in evaluating audit evidence, determining its sufficiency and appropriateness in complex and uncertain scenarios.
5. Appraise and critique emerging assurance areas, including sustainability reporting, Environment, Social and Governance (ESG) disclosures, and ethical dimensions of assurance engagements.
6. Communicate and defend audit and assurance findings effectively to diverse stakeholders, integrating data visualisation where appropriate to enhance clarity and impact.

Outline Syllabus

The module opens by mapping the evolving assurance landscape, examining the international standard-setter's strategic priorities, international standards for firm-level quality management, and the auditor's public-interest role. Building on this context, students revisit fraud responsibilities guided by current international auditing standards on the topic, learning to apply both professional judgment and data analytics for anomaly detection. We then address going-concern assessments using the relevant international standard, focusing on developing professional scepticism regarding business viability.

Subsequently, the focus shifts to practical digital skills. We explore auditing evidence in digital form and introduce a structured Audit Data Analytics (ADA) toolkit, using Python or R. Hands-on labs provide experience in data import, cleansing, analysis techniques, and effective data visualisation, using a realistic dataset that forms the basis of the assessed ADA case study. The syllabus then tackles complex auditing areas, such as evaluating subjective management judgements ('complex estimates'), navigating the challenges of multi-company 'group audits', and verifying transactions recorded on distributed ledgers ('blockchain audit trails').

A dedicated section will focus on the rapidly expanding field of sustainability assurance. We analyse the drivers behind Environmental, Social, and Governance (ESG) reporting and conduct a detailed examination of the primary international standard governing sustainability assurance engagements. This includes understanding the specific ethical requirements established for sustainability assurance. Case discussions will illuminate practical challenges.

Effective communication is then addressed, covering the principles of audit reporting based on international standards, the identification and articulation of Key Audit Matters, and techniques for clearly presenting ADA findings. The module concludes by looking towards the future, exploring the potential impact of Artificial Intelligence, the evolution towards 'continuous' assurance models, and the critical challenges of auditing cyber-risk.

Throughout this learning journey, digital skills and sustainability knowledge are carefully interwoven with core auditing principles, reflecting their centrality to contemporary practice. Module assessments are designed to integrate these elements, requiring students to apply ADA techniques and exercise robust professional judgment in realistic scenarios. This process develops key Lancaster Graduate Attributes including problem-solving, ethical awareness, and sustainability consciousness. Furthermore, the module provides ideal preparation for demanding professional qualification paths.

Assessment Proportions

A blended, large-cohort-friendly design combines concept-driven lectures with data-analytics labs and structured class discussions. Interactive lectures introduce updated standards, ethical issues and emerging technologies, using live polling and mini cases to stimulate participation. Computer-lab workshops develop practical ADA competence: students manipulate real-world datasets in Python/R, reinforcing digital literacy and professional scepticism. Facilitated case discussion allow students to apply standards and judgement to complex situations.

Assessment is aligned to outcomes and workload limitations: a 25 % ADA analysis case (plus a short professional report) tests data skills and interpretation; a 75% closed-book exam (2 hours) assesses critical understanding of frameworks, risk analysis, evidence evaluation and sustainability assurance. Both components emphasise ethical reasoning and clear communication.

Throughout, the module focuses one two programme-wide themes: sustainability and digital skills. Learning activities progressively support these themes so students can evidence them in both assessments. Formative feedback arises from lab demonstrations, Mentimeter quizzes and online Q&A, while summative feedback employs detailed rubrics to ensure consistency.

All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs. Lancaster University traffic light system of Generative AI use in assessment will be adhered to. This module supports the responsible use of generative AI as a learning and professional tool. Students will be encouraged to critically apply AI in their coursework where appropriate, with assessments designed to ensure that AI use enhances, rather than replaces, the development of core skills such as critical thinking, analysis, and communication.

By integrating cutting-edge professional standards, hands-on analytics and sustainability assurance, the module prepares students for graduate audit roles.

ACFN6281: Taxation

• Terms Taught: Lent/Summer
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: Knowledge of Financial Accounting Reporting, Financial Statements, Reporting Standards

Course Description

The module aims to enable students to understand the general objectives of taxation as it impacts on businesses and individuals and the impact of taxation of business transformation. This includes the ability to evaluate the external influences on UK tax policy including an understanding of the impact of sustainability and environmental taxes. The module aims to enable students to prepare computations including obligations for taxpayers for the core areas of taxation in the UK covering the tax bases of income, capital and consumption. The module aims to provide students with the skills to identify and explain tax efficient strategies and understand the implications of tax evasion in both a UK and International context. The module aims to provide the basics for professional study and exams for membership of the UK professional bodies and covers an introduction to professional ethics.

Educational Aims

Upon successful completion of this module students will be able to...

1. Critically explain and apply the objectives of taxation and the underlying principles of the main tax bases in the UK (income, capital, and consumption), in individual and business contexts.
2. Evaluate the external influences on UK tax policy formation, including political, economic, and sustainability-related drivers, and critique their implications for fairness and efficiency.
3. Apply ethical frameworks to professional tax scenarios, assessing implications of compliance and non-compliance for taxpayers, practitioners and society.
4. Calculate, interpret, and justify tax liabilities, including income tax, national insurance, capital taxes (inheritance tax, stamp duty, capital gains), and corporate tax, considering group structures and chargeable gains.
5. Interpret and apply VAT legislation, including calculations for registration, deregistration, and different supply types, and evaluate its role within the UK tax system.
6. Evaluate and advise on tax planning strategies for individuals and companies, including opportunities relating to business transformation and long-term financial decision-making.

Outline Syllabus

This taxation module offers a comprehensive foundation in the UK tax system and is ideal for students interested in understanding how tax impacts individuals, businesses, and society. The module covers a wide range of tax types—income tax, national insurance, capital gains tax, inheritance tax, corporation tax, VAT, and stamp duties—providing students with both theoretical knowledge and advanced applied skills in tax computation and interpretation.

Students will explore the fundamental objectives of taxation, such as raising revenue, redistributing income, and influencing economic behaviour. Students will gain insight into how tax bases are structured around income, capital, and consumption, and how these interact with government policy. The module also introduces the external influences that shape UK tax policy, including globalisation, political pressures, and increasingly, sustainability concerns and international coordination.

Practical elements of the module teach students how to calculate tax liabilities for individuals in employment or running a business, for companies, and in more complex scenarios such as inheritance and capital gains. Students will work through real-world examples to compute national insurance contributions, corporation tax (including chargeable gains and group relief), and VAT obligations, and critically evaluate the results from a professional and policy perspective, enhancing student problem-solving and numerical skills.

Ethical decision-making is an important focus, and students will consider how professional standards and codes of conduct apply to tax practitioners. The module also explains taxpayer obligations and the potential consequences of non-compliance, helping students critically assess the balance between tax planning and evasion.

Finally, the module introduces tax planning strategies, especially those relevant to business transformations and restructuring. Whether you aim for a career in accounting, finance, or policy, this module provides a strong foundation in taxation and shows how tax impacts virtually every financial decision in both personal and business contexts.

Assessment Proportions

The module contributes to the overall aims of the accounting and finance degree programmes because it provides technical understanding of a topic which supports students in their future career in accounting and finance. For those students who wish to pursue a career as a professional accountant, the module provides some exemptions from core professional exams.

The module integrates with other modules on the degree programmes by providing the tax implications of the accounting entities, structures and transactions studied on financial accounting modules. It also links to ethics, law and sustainability modules which form part of the degree programmes.

The module is taught using a mixture of lectures, seminars and online drop-in sessions. The module requires technical knowledge and the lectures are structured to provide the explanation of the technical issues supported by practical examples.

Tax professionals need to be able to offer a range of solutions to a given scenario. This not only requires the technical knowledge but also the ability to communicate and ask the appropriate questions of other non-specialist professionals and clients. Students taking this module are allocated to study groups of 4- 5 students to work together in tutorials which use a mixture of technical questions and formative mini case studies. 5% of the module grade will be a peer assessment form for students to assess themselves and their peers.

To ensure fairness and inclusivity in peer assessment, students will be provided with clear criteria, training in giving constructive feedback, and opportunities to raise concerns confidentially. Peer assessment will contribute only 5% of the overall grade, and results will be moderated by staff to mitigate unfair outcomes. We recognise that students bring different strengths to group work, including neurodivergent learners, and groups will be supported in setting ground rules and allocating roles to ensure equitable participation. The emphasis is on developing teamwork, reflection, and accountability skills in a supportive environment, not on penalising differences in working styles.

To support lecture understanding of technical topics, online drop-in sessions provide opportunity to work through practice scenarios.

All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs.

ACFN6295: Advanced Accounting and Finance

• Terms Taught: Lent/Summer 
• US Credits: 5
• ECTS Credits: 10
• Pre-requisites: None

Course Description

This module aims to provide students with a critical and integrated understanding of advanced topics in financial accounting, strategic management accounting, and corporate finance. It equips non-financial managers with the analytical and evaluative capabilities essential for effective decision-making in a contemporary business environment. Students will evaluate relevant concepts and practices in the financial decision-making process, with particular emphasis on the ethical implications, strategic relevance, and behavioural dimensions of accounting and finance.

By the end of the module, students should be capable of interpreting and applying complex financial information to make informed decisions that align with organisational objectives.

Educational Aims

Upon successful completion of this module, students will be able to:

1. Critically evaluate the motivations and methods of earnings management and financial disclosure, assessing their impact on transparency, performance and stakeholder confidence.
2. Assess the role of corporate governance and audit in promoting accountability, ethical conduct and stakeholder trust within complex organisational environments.
3. Apply strategic management accounting techniques to support organisational planning, performance measurement, and control, aligning financial insights with strategic objectives.
4. Analyse the implications of financial structure, market behaviour, and payout policy on firm value, capital allocation and financing decisions.
5. Synthesize insights from accounting and finance to evaluate complex business problems and develop ethically sound, strategically informed recommendations.
6. Demonstrate transferrable skills in critical thinking, problem-solving, and data interpretation applicable across diverse business functions and decision-making contexts.

Outline Syllabus

This module introduces students to a range of core concepts and current debates that span financial accounting, corporate finance, and management accounting. The content is structured around key questions, building both subject knowledge and critical inquiry skills:

• What is the purpose of financial disclosure and corporate governance in modern firms?
• How do managers manipulate earnings, and how can governance mechanisms mitigate this?
• What tools and frameworks exist for strategic cost management and behavioural control?
• How do capital markets and financing decisions affect firm value?
• What role does behavioural finance play in explaining deviations from market efficiency?

Indicative thematic progression:

• Governance, sustainability, and financial disclosure
• Earnings management and external auditing
• Performance measurement, cost control, strategic management accounting
• Market efficiency, behavioural finance, capital structure
• Payout policy, security issuance, value creation

The syllabus draws on academic literature, corporate case studies, and applied financial data to bridge theory and practice.

Assessment Proportions

The module adopts a blended approach to learning, incorporating lectures for conceptual understanding and workshops for applied engagement. Emphasis is placed on interactive, student-centred learning and the integration of accounting and finance content. Workshop activities may include case analyses, debates, and application exercises. Students are encouraged to work independently and collaboratively.

To ensure deeper understanding and skill development, the module uses a 100% coursework-based assessment strategy. However, instead of traditional reports, assessments will involve structured problem-solving tasks and applied case analysis to reflect real-world challenges while remaining accessible. Examples of problem-solving tasks include: using regression models to analyse firm performance and corporate governance, assessing impact of sustainability measures on firm value, analysing effective cost management, examining budget factors that influence managerial decision making. Formative feedback is embedded in workshop discussions, while summative tasks offer students scope to demonstrate analytical thinking, clarity of argument, and applied knowledge.

The module incorporates relevant technology through the use of financial databases and analytical software in applied tasks. It also engages with the evolution of subject knowledge in the context of Generative AI (Gen AI) technologies by encouraging students to critically evaluate the outputs of AI tools in financial analysis and ethical considerations in their use, aligning with current discipline developments in data-driven decision-making. This approach aims to provide all students with an equitable and inclusive learning experience by preparing them for modern professional challenges.

All assessments and the related teaching and learning approach will take students Individual Learning and Support Plans (ILSPs) into account and equitable and an inclusive learning experience for all students will be assured through accessible learning materials, multiple modes of engagement, and personalized support for different learning needs.