FIN-40055 - Module Specification
Keele Business School
Faculty of Humanities and Social Sciences

Programme/Approved Electives for 2023/24

Available as a Free Standing Elective

Co-requisites

None

Prerequisites

None

Barred Combinations

None

Description for 2023/24

This module provides students with a solid foundation in modern quantitative techniques used by financial institutions. Moreover, it covers the Chartered Financial Analysts (CFA) Institute syllabus to sit for Level I and Level II exams. In addition to learning these techniques students also gain experience in applying these techniques to real data collected from Bloomberg terminals using STATA/EXCEL. This will help students to perform to a high standard during their dissertation/consultancy/project in semester 3. This module also significantly increases the employability skills of students as they are not only able to analyse complex models, but also explain and apply them in a real life context.

This module provides students with a solid background in modern econometrics. Students will cover both regression analysis and time series in depth. The main focus will be on application and interpretation of econometric models rather than the theoretical derivation of such models. Students will make continuous use of the Bloomberg Trading machines and will carry out their analysis using statistical package and occasionally using Excel spreadsheets. This module will prepare students to complete their dissertation/project to a high standard.

Intended Learning Outcomes

identify and justify the basic components of research frameworks, relevant to the tackled research problem and develop a credible research proposal: 1
demonstrate and apply knowledge of modern probability theory, probability distributions and their application to modern business: 1
visualise and interpret data, demonstrating comprehensive understanding of key statistical concepts, sampling, estimation and hypothesis testing: 1
demonstrate a critical appreciation of the strengths and weaknesses of single and multiple regression models to solve business problems: 1
undertake modern time series modelling, estimation of complex time series models and appreciate their wider application in portfolio management and risk modelling: 1
apply advanced econometric methods such as panel data, machine learning and big data: 1

Study hours

2 hour lectures per week for 12 weeks.
126 hours independent study

School Rules

None

Description of Module Assessment