Quantitative Foundations of Finance

The details
Essex Business School
Colchester Campus
Undergraduate: Level 5
Monday 13 January 2020
Friday 20 March 2020
30 August 2019


Requisites for this module
BE300 or EC111 or IA712



Key module for

BSC N300 Finance,
BSC N301 Finance (Including Foundation Year),
BSC N302 Finance (Including Year Abroad),
BSC N304 Finance (Including Placement Year),
BSC GN13 Finance and Mathematics,
BSC GN15 Finance and Mathematics (Including Placement Year),
BSC GN18 Finance and Mathematics (Including Foundation Year),
BSC GN1H Finance and Mathematics (Including Year Abroad)

Module description

This is not an introductory module in finance. Tools and techniques that are basic for a quant career in finance, from mathematics, such as algebra and calculus will be taught on the module. Some prior computing skill in managing large amounts of data is necessary as well as a willingness to learn further computing skills in Maple, which will be demonstrated on the module.

The module carefully examines the building blocks of modern finance theory and focuses on the theoretical and analytical cornerstones on which the building blocks are placed. We study how these building blocks can, in certain cases, help us identify potentially optimal decisions now, even though their future consequences are still uncertain.

A common feature of finance is the need to make good use of, and where possible the best use of limited resources; constrained optimization techniques, which are taught on the module, can often guide us in this need. Since concepts in probability are widely employed in finance to describe the inevitable uncertainty regarding the future, we examine its basic elements. It is a near universal truth that most of us dislike risk and prefer to avoid risk. We also find that we will avoid risks only if the price for avoiding that risk is acceptable. We study how expected utility theory helps us measure how averse we are to taking such risks.

We then proceed to apply these building blocks to examine several concepts: choice under uncertainty, maximizing returns and minimizing risk subject to constraints, mean-variance analysis and the capital asset pricing model. Finally, we show how real options can often help improve corporate investment decisions as compared to traditional approaches that employ the net present value rule.

Module aims

The aim of this course is to familiarize you with the mathematical tools and the analytical skills necessary to understand the theory of finance.

Module learning outcomes

On successful completion of the module, you will be able to:
1) Apply mathematical techniques and tools employed in finance.
2) Describe and evaluate measures of risk aversion using expected utility theory.
3) Understand the concept of ‘efficient frontier’ when investing in risky assets.
4) Evaluate investment decisions employing Real Options and NPV approaches.

Module information

Skills for Your Professional Life (Transferable Skills)
The module, class activities and coursework will help you to develop the following transferable skills:
a) solve practical problems that need to make best use of limited resources.
b) choose a portfolio of assets that best suit the needs of professional investors.
c) employ Maple software to solve quantitative problems in finance.
d) support the decision making activities associated with capital budgeting decisions.

Learning and teaching methods

There will be one class per week associated with each lecture, each of one-hour duration, for ten weeks. The class will lag the corresponding lecture by one week. There are weekly class exercises, which will normally be released before the relevant class but after the associated lecture. You are advised to make an attempt at all the exercises. Class work will need to be handed in each week but are neither marked nor evaluated. Several exercises are solved using the mathematical software called Maple, which can be accessed from the machines in the university's labs. It is good to know how to use at least one mathematical and modelling software and Maple is adequate for that.


  • Sydsaeter, Knut; Hammond, Peter J.; Strom, Arne. (2012) Essential mathematics for economic analysis, Harlow: Pearson.
  • Copeland, Thomas E.; Shastri, Kuldeep; Weston, John Fred. (2014) Financial theory and corporate policy, Harlow: Pearson Education.
  • Luenberger, David G. (2014) Investment science, Oxford: Oxford University Press.

The above list is indicative of the essential reading for the course. The library makes provision for all reading list items, with digital provision where possible, and these resources are shared between students. Further reading can be obtained from this module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Weighting
Coursework TEST 1
Coursework TEST 2
Coursework ASSIGNMENT 1 05/02/2020
Coursework ASSIGNMENT 2 10/03/2020
Exam 120 minutes during Summer (Main Period) (Main)

Overall assessment

Coursework Exam
45% 55%


Coursework Exam
45% 55%
Module supervisor and teaching staff
Hardy Thomas



External examiner

Dr Athanasios Verousis
The University of Newcastle-upon-Tyne
Senior Lecturer in Accounting & Finance
Prof Christos Ioannidis
Aston University
Available via Moodle
Of 77 hours, 77 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).


Further information
Essex Business School

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