Accounting, Finance and Mathematics (Industry) BSc Hons - 2021 Entry

Course Structure

Lancaster University offers a range of programmes, some of which follow a structured study programme, and others which offer the chance for you to devise a more flexible programme to complement your main specialism. We divide academic study into two sections - Part 1 (Year 1) and Part 2 (Year 2, 3 and sometimes 4). For most programmes Part 1 requires you to study 120 credits spread over at least three modules which, depending upon your programme, will be drawn from one, two or three different academic subjects. A higher degree of specialisation then develops in subsequent years. For more information about our teaching methods at Lancaster please visit our Teaching and Learning section.

The following courses do not offer modules outside of the subject area due to the structured nature of the programmes: Architecture, Law, Physics, Engineering, Medicine, Sports and Exercise Science, Biochemistry, Biology, Biomedicine and Biomedical Science.

Information contained on the website with respect to modules is correct at the time of publication, and the University will make every reasonable effort to offer modules as advertised. In some cases changes may be necessary and may result in some combinations being unavailable, for example as a result of student feedback, timetabling, Professional Statutory and Regulatory Bodies' (PSRB) requirements, staff changes and new research.

Year 1

Core

• Future Global Leaders: Sustainability Across Business

  Future Global Leaders is a professional development module taken by all first year LUMS students. The module takes place mainly online and aims to introduce you to key ideas about leadership to help you become future leaders, to enhance your online working skills and to help you develop professional and careers skills which will enhance your employability.

• Introduction to Accounting and Finance

  This course provides a comprehensive introduction to the basic concepts and techniques of Accounting and Finance, which include financial accounting, managerial finance, and financial statement analysis.

  An important element of this course is that it provides exposure to the business and financial environment within which the discipline of Accounting and Finance operates, using real-world financial data for actual companies.

  The course covers concepts, techniques and interpretive skills that relate to the external financial reporting of companies and their relationship to the stock market, and to the use of accounting information for internal management purposes.

• Preparation for Placement

  During this Preparation for Placement module, you will learn about the skills and expertise employers expect you to evidence; how to produce excellent CVs and cover letters; how to make an impact on application forms, what to expect at interviews and assessment centres. You will get to hear from final year students about their placement experience and a chance for you to learn about the placement opportunities on offer meeting graduate employers.

• Principles of Economics A

  Providing a thorough introduction to the discipline of Economics, this module is divided into two parts. The first part covers microeconomic analysis, including the theory of demand, costs and pricing under various forms of industrial organisation, and welfare economics. Many applications of theoretical models are examined. The second part focuses on macroeconomic analysis, including national income analysis, monetary theory, business cycles, inflation, unemployment, and the great macroeconomic debates.

• Professional Skills

  This module will provide you with the opportunity to undertake a personal skills audit as part of the CV assignment. It will enable you to identify your current skills level and begin to collect evidence of skills acquisition. It will cover a detailed analysis of the individual in terms of personality, skills, goals, interests and career ideas, and self-development. It would include organisation recruitment processes, including consideration of what organisations are looking for, the nature of ‘transferable skills’ and how these can be developed, and how organisations can select and train people.

• Quantitative methods for accounting and finance

  The key aim of this module is to equip you with essential mathematical and statistical skills relevant to the disciplines of accounting and finance. The module aims to prepare you for more quantitatively-focused subjects offered across the whole suite of Accounting and Finance undergraduate programmes.

  This is a 40-credit module that spans across three semesters. In Michaelmas term, you will cover the topics in mathematics such as functions, differentiation, integration, matrix algebra. In Lent term, the module will introduce the basic statistics. You will learn some of the features of the financial data, the rules of probability and hypothesis testing. In Summer term, the module will introduce financial markets with some emphasis on risk, efficiency and operation.

Year 2

Core

• Accounting Information Systems and Auditing

  This module provides an overview of the design and main features of accounting information systems (AIS). It introduces methods used by business to meet the financial information needs of external parties and management and includes systems used for collecting, recording and storing transactions data, internal controls and effective design of AIS. It also provides an introduction to auditing, including the regulatory framework, audit planning, systems auditing and substantive testing.

• Computational Mathematics

  Students will gain a solid understanding of computation and computer programming within the context of maths and statistics. This module expands on five key areas:

  • Programming and R
  • Numerical solutions of equations
  • Numerical differentiation and integration
  • Monte Carlo methods
  • Numerical solutions to ODEs

  Under these headings, students will study a range of complex mathematical concepts, such as: data structures, fixed-point iteration, higher dimensions, first and second derivatives, non-parametric bootstraps, and modified Euler methods.

  Throughout the module, students will gain an understanding of general programming and algorithms. They will develop a good level of IT skills and familiarity with computer tools that support mathematical computation.

  Over the course of this module, students will have the opportunity to put their knowledge and skills into practice. Workshops, based in dedicated computing labs, allow them to gain relatable, practical experience of computational mathematics.

• Linear Algebra II

  Students will be provided with the foundational results and language of linear algebra, which they will be able to build upon in the second half of Year Two, and the more specialised Year Three modules. This module will give students the opportunity to study vector spaces, together with their structure-preserving maps and their relationship to matrices.

  They will consider the effect of changing bases on the matrix representing one of these maps, and will examine how to choose bases so that this matrix is as simple as possible. Part of their study will also involve looking at the concepts of length and angle with regard to vector spaces.

• Management Accounting for Business Decisions

  This module provides an introduction to the use of management accounting information for management purposes. This includes an examination of cost-volume profit analysis, the concepts of direct and indirect costs, and various costing methods. The importance of budgets to organisations and their impact on performance are also discussed.

• Principles of Finance

  This module covers project evaluation methods as well as risk, return and the cost of capital, including the capital asset pricing model. Corporate financing, including dividend policy and capital structure, options, and working capital management will also be investigated.

• Principles of Financial Accounting

  This module examines the main features of financial reporting by UK companies as well as the regulatory requirements and conceptual bases associated with these, with attention given to the UK Companies Acts and international accounting standards. Time will also be devoted to inflation accounting, group accounts, and problem areas and to specific reporting topics of current interest and concern.

• Probability II

  Probability provides the theoretical basis for statistics and is of interest in its own right.

  Basic concepts from the first year probability module will be revisited and extended to these to encompass continuous random variables, with students investigating several important continuous probability distributions. Commonly used distributions are introduced and key properties proved, and examples from a variety of applications will be used to illustrate theoretical ideas.

  Students will then focus on transformations of random variables and groups of two or more random variables, leading to two theoretical results about the behaviour of averages of large numbers of random variables which have important practical consequences in statistics.

• Statistics II

  Statistics is the science of understanding patterns of population behaviour from data. In the module, this topic will be approached by specifying a statistical model for the data. Statistical models usually include a number of unknown parameters, which need to be estimated.

  The focus will be on likelihood-based parameter estimation to demonstrate how statistical models can be used to draw conclusions from observations and experimental data, and linear regression techniques within the statistical modelling framework will also be considered.

  Students will come to recognise the role, and limitations, of the linear model for understanding, exploring and making inferences concerning the relationships between variables and making predictions.

Year 3

Core

• Dissertation for Accounting and Finance placement students

  The dissertation is an extended academic piece of work which aims to allow students to explore an issue which may arise on their plcement or which they may have become interested in as a result of their academic studies at Lancaster. The specification is deliberately flexible to allow for the varying placement experiences and resulting dissertation ideas which might arise.

Year 4

Core

• Financial Accounting I

  This module deals with accounting for complex entities, addressing concepts, issues and techniques.

  It examines accounting for business combinations, goodwill and strategic investments, and other aspects of consolidation, foreign currency translation, segmental reporting, and accounting for financial instruments, all within the context of modern accounting theory.

• Financial Accounting II

  This module develops your ability to critically evaluate advanced financial accounting issues, placing this within the international accounting context. It focuses on International Financial Reporting Standards (IFRS), with appropriate and relevant comparisons to US GAAP.

  Other topics covered include the accounting treatments of taxation, leases, pensions, provisions and contingent liabilities. The module also looks at empirical research on issues of relevance to accounting practitioners and accounting regulators.

• Likelihood Inference

  Statistical inference is the theory of the extraction of information about the unknown parameters of an underlying probability distribution from observed data. Consequently, statistical inference underpins all practical statistical applications.

  This module reinforces the likelihood approach taken in second year Statistics for single parameter statistical models, and extends this to problems where the probability for the data depends on more than one unknown parameter.

  Students will also consider the issue of model choice: in situations where there are multiple models under consideration for the same data, how do we make a justified choice of which model is the 'best'?

  The approach taken in this course is just one approach to statistical inference: a contrasting approach is covered in the Bayesian Inference module.

Optional

• Bayesian Inference

  Bayesian statistics provides a mechanism for making decisions in the presence of uncertainty. Using Bayes’ theorem, knowledge or rational beliefs are updated as fresh observations are collected. The purpose of the data collection exercise is expressed through a utility function, which is specific to the client or user. It defines what is to be gained or lost through taking particular actions in the current environment. Actions are continually made or not made depending on the expectation of this utility function at any point in time.

  Bayesians admit probability as the sole measure of uncertainty. Thus Bayesian reasoning is based on a firm axiomatic system. In addition, since most people have an intuitive notion about probability, Bayesian analysis is readily communicated.

• Generalised Linear Models

  The concept of generalised linear models (GLMs), which have a range of applications in the biomedical, natural and social sciences, and can be used to relate a response variable to one or more explanatory variables, will be explored. The response variable may be classified as quantitative (continuous or discrete, i.e. countable) or categorical (two categories, i.e. binary, or more than categories, i.e. ordinal or nominal).

  Students will come to understand the effect of censoring in the statistical analyses and will use appropriate statistical techniques for lifetime data. They will also become familiar with the programme R, which they will have the opportunity to use in weekly workshops.

• Machine Learning

  Using the classical problem of data classification as a running example, this module covers mathematical representation and visualisation of multivariate data; dimensionality reduction; linear discriminant analysis; and Support Vector Machines. While studying these theoretical aspects, students will also gain experience of applying them using R.

  An appreciation for multivariate statistical analysis will be developed during the module, as will an ability to represent and visualise high-dimensional data. Students will also gain the ability to evaluate larger statistical models, apply statistical computer packages to analyse large data sets, and extract and evaluate meaning from data.

• Mathematics for Stochastic Finance

  This module formally introduces students to the discipline of financial mathematics, providing them with an understanding of some of the maths that is used in the financial and business sectors.

  Students will begin to encounter financial terminology and will study both European and American option pricing. The module will cover these in relation to discrete and continuous financial models, which include binomial, finite market and Black-Scholes models.

  Students will also explore mathematical topics, some of which may be familiar, specifically in relation to finance. These include:

  • Conditional expectation
  • Filtrations
  • Martingales
  • Stopping times
  • Brownian motion
  • Black-Scholes formula

  Throughout the module, students will learn key financial maths skills, such as constructing binomial tree models; determining associated risk-neutral probability; performing calculations with the Black-Scholes formula; and proving various steps in the derivation of the Black-Scholes formula. They will also be able to describe basic concepts of investment strategy analysis, and perform price calculations for stocks with and without dividend payments.

  In addition, to these subject specific skills and knowledge, students will gain an appreciation for how mathematics can be used to model the real-world; improve their written and oral communication skills; and develop their critical thinking.

• Medical Statistics: study design and data analysis

  The aim is to introduce students to the study designs and statistical methods commonly used in health investigations, such as measuring disease, causality and confounding.

  Students will develop a firm understanding of the key analytical methods and procedures used in studies of disease aetiology, appreciate the effect of censoring in the statistical analyses, and use appropriate statistical techniques for time to event data.

  They will look at both observational and experimental designs and consider various health outcomes, studying a number of published articles to gain an understanding of the problems they are investigating as well as the mathematical and statistical concepts underpinning inference.

• Stochastic Processes

  Important examples of stochastic processes, and how these processes can be analysed, will be the focus of this module.

  As an introduction to stochastic processes, students will look at the random walk process. Historically this is an important process, and was initially motivated as a model for how the wealth of a gambler varies over time (initial analyses focused on whether there are betting strategies for a gambler that would ensure they won).

  The focus will then be on the most important class of stochastic processes, Markov processes (of which the random walk is a simple example). Students will discover how to analyse Markov processes, and how they are used to model queues and populations.

• Time series analysis

  Modern statistics is characterised by computer-intensive methods for data analysis and development of new theory for their justification. In this module students will become familiar with topics from classical statistics as well as some from emerging areas.

  Time series data will be explored through a wide variety of sequences of observations arising in environmental, economic, engineering and scientific contexts. Time series and volatility modelling will also be studied, and the techniques for the analysis of such data will be discussed, with emphasis on financial application.

  Another area the module will focus on is some of the techniques developed for the analysis of multivariates, such as principal components analysis and cluster analysis.

  Lastly,students will spend time looking at Change-Point Methods, which include traditional as well as some recently developed techniques for the detection of change in trend and variance.