CF969-7-AU-CO:
Machine Learning for Finance
PLEASE NOTE: This module is inactive. Visit the Module Directory to view modules and variants offered during the current academic year.
2024/25
Computational Finance and Economic Agents (Centre for)
Colchester Campus
Autumn
Postgraduate: Level 7
Inactive
Thursday 03 October 2024
Friday 13 December 2024
15
04 April 2024
Requisites for this module
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Financial services, in particular, have widely adopted big data analytics and machine learning to inform better investment decisions with consistent returns. In conjunction with machine learning, modern optimisation techniques (such as linear programming) are adopted to solve many computational finance problems ranging from asset allocation to risk management, from option pricing to model calibration. This module will be a mix of theory and practice with big data and machine learning cases in finance.
The algorithmic and data science theories will be introduced and followed by a thorough introduction of data-driven algorithms for structured and unstructured data. Modern machine learning and data mining algorithms will be introduced with particular case studies on financial industry.
The aim of this module is:
- To introduce students to the concept of financial machine learning relying on big data and the rapid growth in online storage.
By the end of this module, students will be expected to be able to:
- Understand the principles of (data-driven) algorithms such as modern machine learning and data mining algorithms
- Understand the application of (data-driven) algorithms to the financial industry
- Use software tools to build up data-driven algorithms and analyse the huge amount of historical data
The vast proliferation of data and increasing technological complexities continue to transform the way industries operate and compete. Over the last two years, 90 percent of the data in the world has been created as a result of the creation of 2.5 exabytes of data on a daily basis. Commonly referred to as big data, this rapid growth and storage creates opportunities for collection, processing and analysis of structured and unstructured data.
Financial services have widely adopted big data analytics and machine learning to better inform investment decisions. We adopt machine learning strategies to solve a number of financial problems.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
| Coursework / exam |
Description |
Deadline |
Coursework weighting |
Exam format definitions
- Remote, open book: Your exam will take place remotely via an online learning platform. You may refer to any physical or electronic materials during the exam.
- In-person, open book: Your exam will take place on campus under invigilation. You may refer to any physical materials such as paper study notes or a textbook during the exam. Electronic devices may not be used in the exam.
- In-person, open book (restricted): The exam will take place on campus under invigilation. You may refer only to specific physical materials such as a named textbook during the exam. Permitted materials will be specified by your department. Electronic devices may not be used in the exam.
- In-person, closed book: The exam will take place on campus under invigilation. You may not refer to any physical materials or electronic devices during the exam. There may be times when a paper dictionary,
for example, may be permitted in an otherwise closed book exam. Any exceptions will be specified by your department.
Your department will provide further guidance before your exams.
Overall assessment
Reassessment
Module supervisor and teaching staff
School Office, e-mail csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770.
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Dr Anna Jordanous
University of Kent
Senior Lecturer
Dr Colin Johnson
University of Nottingham
Available via Moodle
No lecture recording information available for this module.
* Please note: due to differing publication schedules, items marked with an asterisk (*) base their information upon the previous academic year.
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