ICM322-Machine Learning in Finance

Module Provider: ICMA Centre
Number of credits: 10 [5 ECTS credits]
Level:7
Terms in which taught: Spring term module
Pre-requisites:
Non-modular pre-requisites:
Co-requisites:
Modules excluded:
Current from: 2020/1

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 ofÌýPython,ÌýweÌýwillÌýexplore ways in whichÌýa computer can be trained toÌýrecognise patterns inÌýdata. The focus will be onÌýfinance applicationsÌýincludingÌýstock price forecasting, default prediction andÌýmarketÌýsentiment analysis.Ìý

Aims:

The module focuses onÌýcommon machine learning techniquesÌýincluding (1)Ìýlogistic regression,Ìý(2)Ìýdecision trees,Ìý(3)ÌýK-nearest neighbours,Ìý(4) K-means clustering,Ìý(5)Ìýprincipal component analysis andÌý(6)Ìýdeep learning tools like neural networks.ÌýTheÌýemphasisÌýwill be on the use of machine learning techniques forÌýfinance applications.Ìý

Assessable learning outcomes:

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

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


• Be able to buildÌýmachine learningÌýmodelsÌýand interpret the modelsÌýin terms ofÌýtheirÌýstructure and accuracy;Ìý


• Understand how machine learning can be used to solveÌýold and new problems inÌýfin anceÌýÌý

Additional outcomes:

The module will use the industry standardÌýPython programming language.Ìý

Outline content:

1. 
   

   Artificial intelligence, machine learning, deep learningÌý

   
   

2. 
   

   Linear and logistic regressionÌýmodels inÌýPythonÌýand finance applicationsÌý

   
   

3. 
   

   Decision Tree Models inÌýPythonÌýand finance applicationsÌý

   
   

4. 
   

   K-nearest neighbours andÌýK-means clustering in Python and finance applicationsÌý

   
   

5. 
   

   Principal component analysis in Python and finance applicationsÌý

   
   

6. 
   

   Deep learning and neural networks in Python and finance applicationsÌý

   
   

7. 
   

   Machine learning case stu diesÌý

   
   

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.Ìý

Brief description of teaching and learning methods:

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

Summative assessment- Examinations:

Summative assessment- Coursework and in-class tests:

Students will be asked to complete a report (40%)ÌýbyÌýweek 2 of the summer term andÌýoneÌýin class multiple choice test (60%)Ìýin week 7Ìýof the spring term.Ìý

Formative assessment methods:

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

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.aspxÌý

Assessment requirements for a pass:

50% weighted average markÌý

Reassessment arrangements:

Re assessment of individualÌýreportÌý

Additional Costs (specified where applicable):

Last updated: 4 April 2020