This module equips you with the knowledge and tools to implement financial models using Excel. The module covers a range of topics in finance drawn from investment analysis, corporate finance, fixed income analysis, risk measurement and international finance. The emphasis of the module is on the practical application of finance theory, with lectures on each topic followed by in- depth practical classes, in which you will work through real world problems using Excel.
This is an applied module that covers the implementation of quantitative financial models that are widely used in financial institutions such as investment banks, mutual funds and hedge funds, and in non-financial corporations.
The content of this module is relevant to the finance industry in an international setting, with examples drawn from a number of global financial markets. The international aspects of finance are explicitly addressed through the modelling of exchange rates.
The sustainable operation of financial markets relies on the fact that security prices reflect the fair value of the future cash flows that they promise to pay. The fair valuation of security prices is maintained by the actions of investors, who exploit deviations from fair value through the implementation of financial models such as those covered in this module.
Research in Teaching
Some of the material covered in the module draws on research in the area of applied finance undertaken by the module convener, and either published in academic journals or used as a basis for commercial consultancy. Where appropriate, references to this research will be provided.
Full module specification
|Module title:||Financial Modelling|
BEA2018 Corporate Finance Or BEE2027 Financial Markets and Decisions I
|Duration of module:||
Duration (weeks) - term 1: |
The aim of this module is to equip you with the knowledge and tools to implement financial models using Excel. The course introduces you to the general principles of building financial models, as well as a number of specific financial modelling tools including regression analysis, statistical functions, matrix functions, optimization and simulation. These methods are applied to a range of practical problems in finance, including financial statement modelling, yield curve modelling, risk measurement and exchange rate modelling. The emphasis of the course is on practical application of the theory, with lectures on each topic followed by in-depth practical classes, in which you will work through real world problems using Excel. The module will also introduce you to the use of VBA (Microsoft’s programming language) in financial modelling.
ILO: Module-specific skills
- 1. explain the objectives of financial modelling and the characteristics of financial data
- 2. download financial data from the internet for a range of securities, including stocks, bonds and currencies
- 3. undertake a range of financial and statistical calculations in Excel
- 4. estimate the cost of capital for a company
- 5. build a financial statement model and use this to estimate the fair value of a company
- 6. estimate a model of the term structure of interest rates
- 7. estimate the expected yield of a corporate bond
- 8. evaluate the risk of an investment portfolio
- 9. estimate the fair value of a currency
- 10. implement currency trading strategies based on momentum, carry and fair value
ILO: Discipline-specific skills
- 11. evaluate the characteristics of financial data and their statistical properties
- 12. explain the nature and use of financial statement models
- 13. estimate corporate and Treasury bond yields and yield curves
- 14. use simulation to model the distribution of financial security returns
- 15. design a range of different trading strategies and test their effectiveness
ILO: Personal and key skills
- 16. solve applied problems using Excel
- 17. download and manipulate complex data
- 18. plan and undertake independent research
- 19. present research in a professional manner
- 20. work effectively in teams
Learning activities and teaching methods (given in hours of study time)
|Scheduled Learning and Teaching Activities||Guided independent study||Placement / study abroad|
Details of learning activities and teaching methods
|Category||Hours of study time||Description|
|Contact hours||12||Lectures (6 x 2 hours)|
|Contact hours||18||Practical Classes (6 x 3 hours)|
|Form of assessment||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Weekly online quizzes||Six quizzes, each taking about 20 minutes||1-16||Correct answers provided on ELE|
|Weekly case studies||Six weekly supervised practical case studies, each lasting three hours||1-18||Feedback during practical classes and suggested solutions provided on ELE|
Summative assessment (% of credit)
|Coursework||Written exams||Practical exams|
Details of summative assessment
|Form of assessment||% of credit||Size of the assessment (eg length / duration)||ILOs assessed||Feedback method|
|Individual written assignment||50||800 words,excluding tables, figures and references||1-19||Standard feedback form|
|Group written assignment (five students per group)||50||4000 words, excluding tables, figures and references||1-20||Standard feedback form|
Details of re-assessment (where required by referral or deferral)
|Original form of assessment||Form of re-assessment||ILOs re-assessed||Timescale for re-assessment|
|Individual assignment||Individual assignment (800 words)||1-19||July/August|
|Group assignment||Individual assignment (800 words)||1-19||July/August|
Students who fail the module overall will resit the component(s) in which they failed.
• Financial statement modelling
• Yield curve modelling
• Default adjusted bond yields
• Risk measurement
• Exchange rate modelling
Indicative learning resources - Basic reading
Benninga, S. (2014) Financial Modelling, 4th edition, Massachusetts: MIT Press.
Module has an active ELE page?
Indicative learning resources - Web based and electronic resources
To be advised during the course.
Indicative learning resources - Other resources
To be advised during the course.
Last revision date