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FIN42050

Academic Year 2024/2025

Quant. Methods for Finance (FIN42050)

Subject:
Finance
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
5
Module Coordinator:
Dr Juan Arismendi-Zambrano
Trimester:
Autumn
Mode of Delivery:
Blended
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

This module covers the fundamental mathematical and statistical background necessary for the understanding of financial markets. The module will incorporate topics in quantitative methods including set theory, calculus, linear algebra, and probability and statistics. Further, the module will focus on the application of these topics to problems in finance, asset pricing and risk management.

About this Module

Learning Outcomes:

On completion of this module, students are expected to be able to
1. Demonstrate a knowledge and understanding of the key practical and theoretical components of quantitative financial methods.
2. Apply quantitative techniques to practical real world problems in finance.
3. Implement a range of applied quantitative techniques using spreadsheets.
4. Develop research skills including data analysis.

Indicative Module Content:

1. Few Aspects of Logic, Set Theory
2. Introduction to Finance and Time value of money
3. Linear Algebra
4. Calculus
5. Probability and Statistics

Student Effort Hours:
Student Effort Type Hours
Lectures

18

Tutorial

6

Specified Learning Activities

10

Autonomous Student Learning

66

Total

100


Approaches to Teaching and Learning:
The key teaching and learning approaches used in the module are lectures, problem-based learning, independent learning and group work.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Exam (In-person): Final examination End of trimester
Duration:
2 hr(s)
Alternative linear conversion grade scale 40% No

50

No
Exam (In-person): Midterm examination End of trimester
Duration:
2 hr(s)
Alternative linear conversion grade scale 40% No

40

No
Exam (Take-Home): Online Quizzes on Tutorial Questions will be graded on a 2 marks per each of the 5 tutorials basis. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6 Alternative linear conversion grade scale 40% No

10

No

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Spring Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Group/class feedback, post-assessment
• Online automated feedback
• Self-assessment activities

How will my Feedback be Delivered?

Not yet recorded.

The course will mainly follow the outline of the following textbook:

Mathematics for Economists by Carl P. Simon and Lawrence Blume, International Edition ISBN 978-0-393-95733-4 (Wil be referred to as S&B)

There are some notes prepared and available to students on Brightspace. Students are expected to download and read these before coming to the relevant lecture.
The notes and the lecture slides are complements and are not meant to substitute each other, any practical examples and financial applications will be covered during the lectures.

Students may find that the following textbooks will be helpful in their learning as these deal with explain the topics covered in great detail:
• Knut Sydsaeter, Peter Hammond, Arne Strom, Andrés Carvajal (2016), Essential Mathematics for Economic Analysis, 5th Edition, Pearson Education Limited. ISBN: 9781292074610 (Wil be referred to as SHSC)
• Ronald J. Harshbarger, James J. Reynolds (2013), Mathematical Applications for the Management, Life, and Social Sciences, International Editon, 10th Edition, Cengage Learning UK. ISBN: 9781133108481 (Will be referred to as HR)

Recommended Reading for Modelling in Excel:

Sengupta, Chandon (2010), Financial Analysis and Modelling Using Excel and VBA, John Wiley & Sons.

Name Role
Dr Adrian Fernandez-Perez Lecturer / Co-Lecturer
Zhannur Issayev Tutor
Ronald Wafula Tutor
Gabija Zdanceviciute Tutor