ICM322-Machine Learning in Finance

Module Provider: ICMA Centre
Number of credits: 10 [5 ECTS credits]
Terms in which taught: Spring term module
Non-modular pre-requisites:
Modules excluded:
Current from: 2021/2

Module Convenor: Mr Mininder Sethi
Email: m.sethi@icmacentre.ac.uk

Type of module:

Summary module description:

In this module you will learn how machine learning techniques borrowed from artificial intelligence can be used to solve common problems in finance. With the use of Python, we will explore ways in which a computer can be trained to recognise patterns in data. The focus will be on finance applications including stock price forecasting, default prediction and market sentiment analysis. 


The module focuses on common machine learning techniques including (1) logistic regression, (2) decision trees, (3) K-nearest neighbours, (4) K-means clustering, (5) principal component analysis and (6) deep learning tools like neural networks. The emphasis will be on the use of machine learning techniques for finance applications. 

Assessable learning outcomes:

By the end of the module it is expected that students will: 

  • Explain the need for a rigorous data science approach and the concepts of training data, validation data and testing data; 

  • Be able to explain the construction of machine learning models and how to interpret the models in terms of their structure and accuracy; 

  • Discuss and evaluate how machine learning can be used to solve old and new problems in finance .

Additional outcomes:

The module will use the industry standard Python programming language. 

Outline content:

  1. Artificial intelligence, machine learning, deep learning 

  2. Linear and logistic regression models in Python and finance applications 

  3. Decision Tree Models in Python and finance applications 

  4. K-nearest neighbours and K-means clustering in Python and finance applications 

  5. Principal component analysis in Python and finance applications 

  6. Deep learning and neural networks in Python a nd finance applications 

  7. Machine learning case studies 

Global context:

The module covers industry standard techniques using international datasets. The concepts are applied in investment banks, central banks, hedge funds and asset management firms worldwide. 

Brief description of teaching and learning methods:

The core theory and concepts will be presented during lectures. Problem sets will be solved in workshops. 

Contact hours:
  Autumn Spring Summer
Lectures 10
Seminars 5
Guided independent study:      
    Wider reading (independent) 25
    Wider reading (directed) 10
    Preparation for seminars 10
    Revision and preparation 15
    Essay preparation 15
    Reflection 10
Total hours by term 0 0
Total hours for module 100

Summative Assessment Methods:
Method Percentage
Report 60
Class test administered by School 40

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Students will be asked to complete a report (60%) by week 2 of the summer term and one in class multiple choice test (40%) in week 7 of the spring term. 

Formative assessment methods:

Seminar questions are assigned for each class. The seminar leader will facilitate discussion and offer feedback. 

Penalties for late submission:

Penalties for late submission on this module are in accordance with the University policy. Please refer to page 5 of the Postgraduate Guide to Assessment for further information: http://www.reading.ac.uk/internal/exams/student/exa-guidePG.aspx 

Assessment requirements for a pass:

50% weighted average mark 

Reassessment arrangements:

Re assessment of individual report 

Additional Costs (specified where applicable):

Last updated: 8 April 2021


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