MA4040  Financial Mathematics with Statistics 1 (2023/24)
Module specification  Module approved to run in 2023/24  
Module title  Financial Mathematics with Statistics 1  
Module level  Certificate (04)  
Credit rating for module  30  
School  School of Computing and Digital Media  
Total study hours  300  


Assessment components 


Running in 2023/24 (Please note that module timeslots are subject to change) 
No instances running in the year 
Module summary
This module develops the mathematical and statistical tools that are used in mathematics of finance. It also introduces methods of analysing data using appropriate financial and statistical software.
Prior learning requirements
GCSE in maths grade C or above
Module aims
This module introduces the basic terminologies used in finance and develops the mathematical techniques to solve problems in the area of finance. Descriptive statistics and statistical techniques that are useful to present, analyse and make inferences about financial data are also introduced. A selection of suitable software (eg. Excel, R and SPSS) will enable students to analyse financial data in order to make informed financial decisions.
Syllabus
Introduction to statistical methods and applications in finance; Applications of AP and GP in Finance;
Descriptive statistics (measures of central tendency and variability) and their applications in finance (introduction of a package); Introduction to financial measurements including simple and compound interest and the use of Excel built functions; Probability and introduction to the basic statistical distributions and hypotheses testing; Mathematics of finance; Introduction to expected values, mortality rate, index; Introduction to model fitting.
Learning and teaching
Students’ learning is supported by blended learning via facetoface learning activities that include lectures, seminars, individual and groupbased case studies and investigations and real data analysis resourced by Weblearn. There is full provision of documents related to the module in electronic format that can be accessed by students all the time. The documents include lecture notes, slides, guidance to packages, Online exercises, Online self assessment exercise and tests, data for analysis and feedback. Students are motivated to analyse real financial and statistical data sets made available to them.
Learning outcomes
On successful completion of this module, students should be able to:
LO 1. Demonstrate an understanding of the mathematical aspects of interest as applied to financial
instruments, calculate various rates of interest, PV, FV and make distinctions between them.
LO 2. Identify different types of data, summarise and present financial data; use sample data to make
inference about population parameters.
LO 3. Understand the ideas of probability and the basic probability distributions including discrete and
continuous; use expected values in a financial context and understand their use in the decision
making process.
LO 4. Using appropriate statistical package (such as R, SPSS, Excel) to fit mathematical models to a
given set of data; and investigate and interpret financial models.
Assessment strategy
The assessment consists of three sets of Online class tests. In the first test students are tested on their understanding of the basic financial instruments and summary statistics (LO1, LO2). The second test examines the understanding gained by the students regarding the basic discrete and continuous probability distributions and the various financial models (LO3,LO4). Final test will test LO14.
Bibliography
Guthrie, G and Lemon, L (2003) Mathematics of Interest and Finance, Prentice Hall.
Zima, P and Brown, R (1998) Schaum’s Mathematics of Finance, McGrawHill.
Kaminsky, K (2003) Financial Literacy: Introduction to the Mathematics of Interest, Annuities.
UPA.
Adams, A, Booth, P, Bowie, D and Freeth, D (2003) Investment Mathematics, John Wiley.
Jackson, M and Mike Staunton (2001) Advanced Modelling in Finance using Excel and VBA,
John Wiley.
Albright, S., Winston, W. and Zappe,C. (2003) Data Analysis & Decision Making with Microsoft Excel, Thomson.
Winston, W. and Albright, S. (2001) Practical Management Science (2nd ed.) Duxbury Press.
McClave, T. and Sincich, T. (2003) A First Course in Statistics, Prentice Hall.
Veaux, R. and Vellman,P. (2004) Intro Stats, Addison Wesley ISBN 0201709104.