Quantitative Methods for Finance - BUSN3740

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Module delivery information

Location Term Level1 Credits (ECTS)2 Current Convenor3 2021 to 2022
Spring Term 4 15 (7.5) Diana Tunaru checkmark-circle


This module builds on knowledge gained from CB367: Introduction to Data Analysis and Statistics for Business. The module is designed to provide a sound mathematical and statistical foundation for studying finance. Students will learn the key mathematical and statistical tools necessary to analyse effectively financial data.
Topics covered include:
• Basics: algebra, linear equations
• Solving simultaneous linear equations
• Rates of change and Differentiation
• Optimization (minimisation-maximisation)
• Introduction to matrix algebra
• The classical simple and multiple linear regression model (estimation – inference)


Contact hours

The module will be taught by lectures, seminars and private study.
Total Contact Hours: 32
Private Study Hours: 118

Method of assessment

Main assessment methods:
VLE Test (30%)
Examination, 2 Hour (70%)

Reassessment Instrument:100% examination

Indicative reading

Bradley, T. (2013) Essential Mathematics for Economics and Business. 4th edn. Chichester: Wiley.
Swift, L. and Piff, S. (2014) Quantitative Methods for Business, Management & Finance. Basingstoke: Palgrave Macmillan.
Teall, J. and Hasan I. (2002) Quantitative Methods for Finance and Investments. London: Blackwell Publishing.

See the library reading list for this module (Medway)

Learning outcomes

The intended subject specific learning outcomes.
On successfully completing the module students will be able to:

Understand fundamental topics of mathematics.
Apply key mathematical formulae to calculate financial variables for decision-making.
Use quantitative techniques to analyse the behaviour of financial markets.
Understand the context of published academic finance literature.

The intended generic learning outcomes.
On successfully completing the module students will be able to:

Demonstrate numeracy and quantitative skills.
Demonstrate ability in data analysis.
Demonstrate understanding of the application of mathematical methods.
Work and study independently, and utilise resources effectively.


  1. Credit level 4. Certificate level module usually taken in the first stage of an undergraduate degree.
  2. ECTS credits are recognised throughout the EU and allow you to transfer credit easily from one university to another.
  3. The named convenor is the convenor for the current academic session.
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