BA4003  Quantitative Methods for Banking and Finance (2024/25)
Module specification  Module approved to run in 2024/25  
Module title  Quantitative Methods for Banking and Finance  
Module level  Certificate (04)  
Credit rating for module  30  
School  Guildhall School of Business and Law  
Total study hours  300  


Assessment components 


Running in 2024/25 (Please note that module timeslots are subject to change) 
No instances running in the year 
Module summary
This module provides a grounding in practical aspects of quantitative analysis with an emphasis on problemsolving techniques in the context of Banking and Finance. The module aims to prepare students for later modules that develop the quantitative and qualitative aspects of Banking and Finance. The module will cover both Financial Mathematics/Statistics, relevant to Banking and Finance, and Information Technology skills required for their subsequent studies.
Module aims
The module aims to:
1. provide a general understanding of the role and application of quantitative methods in Banking and Finance;
2. introduce and support the understanding of different types of data, methods of collection, analysis and presentation of results;
3. introduce methods of calculating summary statistics with the use of Spreadsheet & SPSS packages and apply information technology applications in a variety of realworld situations;
4. enable the student to understand the relationship between variables and the application of probability based methodologies;
5. enable the student to formulate ideas using algebra and calculus and apply these techniques in practical contexts in the discipline of Banking and Finance.
Syllabus
Review of basic Mathematics  Variables, equations and inequalities; exponents, the order of arithmetic operations and the rules of algebra, the number e; Logarithms Data collection and summary statistics Sources and types of data Measurement scales (nominal, ordinal, cardinal, interval); Data presentation and summary statistics: Tables, charts, frequency, and cumulative frequency distributions, Graphical representations, numerical measures to describe data: Measuring central tendency & variability.
Index numbers  Measuring changes over time: price indices, retain price index (RPI); Calculating changes: percentage point change, percentage change; Comparing time series; Application of index numbers in Banking and Finance;
Introduction to sampling Population & samples, inference, random sampling, hypothesis testing, confidence interval, application to Banking & Finance;
Measuring relationships between variables in the context of Banking and Finance  Scatter diagrams, correlation, covariance, simple linear & multiple regression, strength of evidence & statistical testing, predictions: Basic principles, use of Excel/SPSS, and their interpretation, forecasting: extrapolation, interpolation;
Quadratic Equations  The use of linear and quadratic functions to model variables; particularly cost, revenue, profit, demand and supply
Probability  Basic concepts of probability and probability distribution, Basic Rules of probability, Introduction to decision theory, Decision making under uncertainty, Expected monetary value, Decision tree, Application to Banking & Finance.
Time series analysis  Components of a time series (trend, cyclical variation, seasonal variation and random variation), Trend estimation by applying moving averages and simple linear regression, Decomposition of time series: additive and multiplicative models, Forecasting future values
The time value of money  Introduction & future value: Simple and compound interest, annuity present value (appvaluing a bond), discounting & present value, amortization (appdetermining mortgage payment); APs and GPs, depreciation, payment of interest, present and future values, APR, annuities and mortgages, methods of investment appraisal including Payback, ARR, NPV and IRR.
Differential & Integral calculus Concept of differentiation and integration relevant to finance, Basic rules, polynomial and rational functions, product, quotient, chain; exponential and logarithmic functions. Applications including marginal utility, duration and immunisation, portfolio risk and diversification. Basic Integration and applications including valuing dividend payments, expected option values, annuities and growing annuities;
Return, Risk, and Comovement  Return on Investment, Geometric mean return on investment, Internal Rate of Return, Bond yield, Introduction to risk, Expected return, minimum variance portfolio, standard deviation, Covariance
Portfolio Mathematics Portfolio analysis, portfolio return, portfolio variance, diversification and efficiency, the market portfolio and beta, driving the portfolio variance expression.
Elements of Matrix Mathematics  Introduction to Matrices: Matrix arithmetic (Portfolio mathematics), Inverting matrices
Learning and teaching
The module is delivered in a three hours session each week for a thirty week period. Teaching is structured around
 A one hour lecture
 A onehour seminar
 A one hour computer lab session
The lecture provides instruction in concepts models and methods. The seminars and computer lab activities provide a basis for exploring and applying the lecture material. Here the learning approach will engage students in group work, discussion and practice. Through actionlearning students will collectively and individually reflect on their learning experience.
Each week students are given tasks based on the lecture themes. Students are expected to complete these tasks in seminars and in computer session. These will also constitute the basis for directed independent study.
The IT is blended into the weekly tasks so students will be using Excel and word processing to enter data, analyse and interpret the output. The module will focus on applying mathematics, statistics and theory to problems relevant to Banking and Finance.
Learning outcomes
On successful completion of this module students will be able to:
1. formulate quantitative models to address finance based problems;
2. distinguish between different types of data, data collection processes and appreciate the alternative methods of data presentation and their limitations;
3. apply a range of applications of information technology in a variety of realworld situations and develop digital literacy IT skills based on word processing, spreadsheet and SPSS packages;
4. use statistical techniques to analyse, test and interpret relationships between financial variables;
5. apply the principles and rules of probability and probability distribution to evaluate the likelihood of possible outcomes and expected values;
6. apply financial mathematics such as linear and quadratic functions, matrix algebra and calculus to a range of financial problems.
Bibliography
• Oakshott, L., (2009). “Essential Quantitative Methods for Business, Management and Finance2, 3rd ed. Palgrave Macmillan
• Waters, D., (2001) “Essential Quantitative Methods”, 3rd Edition, Pearson Higher Ed, 2001 027364694X
• Dewhurst, F. (2006) “Quantitative Methods for Business and Management”, 2nd Edition, McGraw Hill
• Swift, L. and Piff, S. (2005) Quantitative Methods for Business, Management and Finance, 2nd edition, Palgrave
• Wisniewski, M., (2006) Quantitative Methods for Decision Makes, 4th Edition, Financial Times/Prentice Hall