Big-Data for Computational Finance
Computational Finance and Economic Agents (Centre for)
Postgraduate: Level 7
Monday 22 April 2024
Friday 05 July 2024
17 June 2022
Requisites for this module
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, in particular, have widely adopted big data analytics to inform better investment decisions with consistent returns. In conjunction with big data, 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. The continued adoption of big data will inevitably transform the landscape of financial services.
The module will be a mix of theory and practice with big data cases in finance.
For the theoretical part, 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.
For the pratical part, the big data in finance cases will be introduced together with the study of relevant software tools.
The aims of this module are to introduce students to the concept of big data and the rapid growth in online storage. Financial services have widely adopted big data analytics to better inform investment decisions. We adopt big data strategies to solve a number of financial problems.
After completing this module, students will be expected to be able to:
1) Understand the principles of (data-driven) algorithms such as modern machine learning and data mining algorithms
2) Understand the application of (data-driven) algorithms on financial industry
3) Use software tools to build up data-driven algorithms and analyse the huge amount of historical data
No additional information available.
No information available.
This module does not appear to have a published bibliography for this year.
Assessment items, weightings and deadlines
|Coursework / exam
|Successful Completion of Selected Labs
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.
Module supervisor and teaching staff
Dr Panagiotis Kanellopoulos, email: email@example.com.
Dr Panagiotis Kanellopoulos
School Office, email: csee-schooloffice (non-Essex users should add @essex.ac.uk to create full e-mail address), Telephone 01206 872770
Dr Anna Jordanous
University of Kent
Dr Colin Johnson
University of Nottingham
Available via Moodle
Of 40 hours, 20 (50%) hours available to students:
20 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s), module, or event type.
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