IC318: Machine Learning in Finance

Module code: IC318

Module provider: ICMA Centre; Henley Business School

Credits: 20

Level: 6

When you'll be taught: Semester 2

Module convenor: Dr Mininder Sethi, email: m.sethi@icmacentre.ac.uk

Pre-requisite module(s): BEFORE TAKING THIS MODULE YOU MUST TAKE IC208 (Compulsory)

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: Yes

Talis reading list: No

Last updated: 19 November 2024

Overview

Module aims and purpose

This module introduces students to the fundamentals of Machine Learning (ML), which is an important innovation behind many changes in Business and Finance in recent years, and its applications in Business and Finance. Students will learn about Machine Learning in general, including ML algorithms which provide useful tools for extractions of intelligence in the era of big data. The module aims to present topics in ML such as classification, clustering and probabilistic classification models, neural networks, dimensionality reduction, decision trees, K-nearest neighbours, as well as k-means clustering. The module also provides hands-on experience with analysing and solving a variety of practical problems encountered in business and finance using ML. Python will be used as the main programming language in this module. In addition, Structured Query Language (SQL) will be used for managing large datasets. Students will get a chance to reflect on how ML have changed business landscape in recent years.

Module learning outcomes

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

• demonstrate an appropriate academic knowledge of the fundamental concepts in Machine Learning and its applications in Finance such as Banking, Investments, FinTech, and business in general;
• analyse and handle big datasets from various sources via Structured Query Language such as World Bank datasets, the U.S. Securities and Exchange Commission (SEC) Electronic Data Gathering Analysis and Retrieval system (EDGAR), social media datasets and others;
• develop team-working skills via the module group project.
• apply and evaluate leading-edge practices in finance.

Module content

1. Introduction to programming for Machine Learning using Python
2. Structured and unstructured data
3. Data management and processing in SQL
4. Data visualisation in Python
5. Linear, probit/logit and ordered probit/logit regression models in Python and relevant applications in Finance
6. Bayesian inference, clustering models in Python and applications in Finance
7. Decision Tree and Random Forest in Python and relevant applications
8. Principal component analysis in Python and applications in Finance
9. Deep learning and neural networks in Python and applications in Finance

Structure

Teaching and learning methods

Lectures will combine theoretical frameworks as well as the practical aspects of ML programming and relevant applications. Students will directly apply what they are being taught during seminars. In-person teaching will be supplemented with digital learning such as discussion boards, polling and video recordings.

This module may be taught in a different Semester if you are studying at our campus in Malaysia.
For students studying at our campus in Malaysia: This module may be taught in a different semester and the breakdown of study hours may differ to those set out in the Study Hours table (please refer to the Module Handbook for the correct breakdown). In addition, you will be required to complete an additional 40 hours of study, taking the total number of study hours to 240 for this module. This is to comply with the Malaysian Quality Agency (MQA)

Study hours

At least 30 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

To pass, students need to obtain a mark of 40% or more.

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.

Reassessment

Additional costs