ICM337: Econometric Analysis for Finance

Module code: ICM337

Module provider: ICMA Centre; Henley Business School

Credits: 20

Level: 7

When you'll be taught: Semester 1

Module convenor: Professor Mike Clements, email: m.p.clements@icmacentre.ac.uk

Module co-convenor: Dr Lisa Schopohl, email: l.schopohl@icmacentre.ac.uk

Pre-requisite module(s): This module builds on the content covered in the pre-sessional module 'Future of work: coding with Python for business & finance' and students are expected to have completed this pre-sessional module prior to the start of ICM337 (Open)

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 19 November 2024

Overview

Module aims and purpose

This module equips you with the quantitative tools used by market participants. It is an introductory applied econometrics module with an emphasis on finance. 

The aims and objectives of the module are to give you an introduction to econometrics so that you can understand the econometric techniques used in the finance research literature. Via illustrations from the empirical finance literature and using an econometric software package, you then learn how to apply these techniques to real data. Emphasis is placed on practical applications of the techniques in the global financial markets. As such, the module aims to encourage the development of IT and data handling skills: in particular, the use of Python as the software to apply the econometric techniques to data, e.g. from Eikon and Bloomberg. 

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

• explain the standard approaches to model formulation, estimation and testing, and how to check the statistical validity of a model   
• formulate and apply econometric models to test financial theories and hypotheses 
• interpret and analyse the results from estimated econometric models 
• critically evaluate the use of econometrics in the published academic finance literature. 

Module content

1. Introduction to econometrics and simple regression assumptions 
2. Hypothesis Testing 
3. Multiple regression, t-ratios and over-reaction hypothesis 
4. Goodness of fit statistics and hedonic models 
5. Heteroscedasticity and autocorrelation 
6. Multicollinearity, functional form, normality, 
7. Seasonality, parameter stability tests 
8. Panel Data 
9. Conditional Variance models 
10. Large scale data modelling and analysis 

Structure

Teaching and learning methods

The module uses a mixture of (1) lectures where the theory and concepts are introduced and (2) computer-lab based seminars where we apply the knowledge to practical cases and use Python as the econometric software. 

Study hours

At least 34 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

50% weighted average mark 

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Weekly practice MCQ tests via Blackboard 

Reassessment

Additional costs