BE367-7-SP-CO:
Big Data in Finance

The details
2024/25
Essex Business School
Colchester Campus
Spring
Postgraduate: Level 7
Current
Monday 13 January 2025
Friday 21 March 2025
20
03 October 2024

 

Requisites for this module
(none)
(none)
(none)
(none)

 

(none)

Key module for

MSC N3G312 Financial Data Analytics,
MSC N3C112 Financial Technology (Finance),
MSC N3C124 Financial Technology (Finance),
MSC N3L112 Financial Technology (Economics),
MSC N3CL12 Financial Technology (Computer Science)

Module description

The primary purpose of this module is to provide the student with an understanding of data analytic approaches in finance. The first part of this module covers predictive analytics, risk modelling and corporate finance. The second part will concentrate on the application of data analytics in high frequency finance, fraud and personal finance.

Module aims

The aims of this module are:



  • To enable students to understand the applications of big data and data analytics in finance.

  • To enable students to critically evaluate the techniques above and provide a view on the future direction of innovation in the financial sector.

Module learning outcomes

By the end of this module, students will be expected to be able to:



  1. To understand the use of high frequency data in finance and its limitations.

  2. To undertake predictive estimations and tests of hypotheses using financial data.

  3. To understand how big data can be used in areas of finance such as risk analysis and corporate finance.

  4. To critically evaluate how big data and data analytics is changing the financial sector.

Module information

Big data is the term for a collection of data sets so large and complex that it becomes difficult to process using on-hand database management tools or traditional data processing applications. Given contemporary computing power and potential data collection, many firms, particularly those from the financial sector, wish to use big data. The challenges include capture, curation, storage search, sharing, transfer, data analytics and visualisation. 

Learning and teaching methods

This module will be delivered via:

  • One 2-hour lecture per week.

Bibliography

This module does not appear to have any essential texts. To see non - essential items, please refer to the module's reading list.

Assessment items, weightings and deadlines

Coursework / exam Description Deadline Coursework weighting
Coursework   Take home assignment   02/05/2025  100% 
Exam  Main exam: Remote, Open Book, 24hr during Summer (Main Period) 
Exam  Reassessment Main exam: Remote, Open Book, 24hr during September (Reassessment Period) 

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

Coursework Exam
50% 50%

Reassessment

Coursework Exam
50% 50%
Module supervisor and teaching staff
Dr Efthimios Nikolakopoulos, email: e.nikolakopoulos@essex.ac.uk.
Dr Mike Nikolakopoulos & Dr Onur Sefiloglu
ebspgtad@essex.ac.uk

 

Availability
No
No
No

External examiner

Dr Nikolaos Voukelatos
University of Kent
Senior Lecturer in Finance
Resources
Available via Moodle
Of 22 hours, 22 (100%) hours available to students:
0 hours not recorded due to service coverage or fault;
0 hours not recorded due to opt-out by lecturer(s).

 

Further information
Essex Business School

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