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Assignment Briefs 12-13-2022

Undertake an exhaustive literature search of the subject area/topic, identifying the key contributions made in finance with ML techniques


The price includes the software work/complete assessment ready for submission, no plagiarism guaranteed

General instructions to students

  • Present your coursework in a report format, referencing your literature reviews etc by utilising the Harvard reference system.
  • The assignment should be produced in Word, Excel and Jupyter Notebook with python code.
  • The assignment needs to be undertaken individually
  • Word target is 2000-3000, excluding appendices and tables.
  •  Submit your work electronically through the approved Blackboard courses link (as advised).
  • The deadline for CW submission is Thursday 1300 hours 7 December 2022.
  • All work must be submitted in PDF format via the Module Blackboard link. It will be checked via turnitin for plagiarism.

Coursework requirement (this CW carries 50% of the total module mark)

There are two parts to this assignment and each part is equally weighted.  

Marking Scheme

Part A -Review of Research literature- 50 marks in total

  • The first part of this report is designed to enable you to learn about research methods. Undertake an exhaustive literature search of the subject area/topic, identifying the key contributions made in finance with ML techniques. The review will involve demonstration of a highly developed knowledge base on data mining techniques and tools, as well as their novel applications in financial services. Devise critical responses and research enquiry to existing theory and extend the knowledge and skills base to develop ML applications in finance. (30 marks)
  • Display detailed knowledge and experience of applying Machine Learning techniques in finance and identifying research gaps in knowledge existing in the finance field. (10 marks)
  • Methodology and outcome from a potential research project/plan. (5 marks)
  • Research report. (5 marks)

Part B -Application of the ML technique- 50 marks in total

  • Demonstrate how to apply Python software to data mining and processing and to solve a real-world problem in financial services using  a portfolio optimisation algorithm (25 marks).
  • Methodology adopted, validity, quality and robustness of the ML technique used. (15 marks). 
  • Flexibility and creativity with employing the ML approach to get the appropriate results, demonstration of understanding and capabilities to use optimisation approach for forecasting and in reaching the portfolio selection decision with the model. (5 marks)  
  • Results and conclusions (5 marks)

Submission arrangements:

a) Submit one copy in PDF format only, electronically through the formal module submission system on your module link via Blackboard. Keep one for your own files.

b) There are penalties for late submission.

c) Insert a title page information on the module, name of author and date of submission.

d) Word count.

e) Please use Harvard referencing system.

Text for Postgraduate modules:

Submission of Coursework

Unless explicitly stated otherwise in writing by the module leader, all coursework on this module is submitted via Blackboard only.  It will automatically be scanned through a text matching system (designed to check for possible plagiarism).

  • YOU MUST include your name and student ID on the first page of your assignment.               
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