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FIN42110

Academic Year 2024/2025

Data Science for Trading & Risk Management (FIN42110)

Subject:
Finance
College:
Business
School:
Business
Level:
4 (Masters)
Credits:
8
Module Coordinator:
Dr Richard McGee
Trimester:
Spring
Mode of Delivery:
On Campus
Internship Module:
No
How will I be graded?
Letter grades

Curricular information is subject to change.

The module will cover aspects of financial data science in python, including:

-Novel Data Set collection
-DB creation and querying
-Steps in cleaning, checking and organisation of the data
-Data visualisation
-Alternative Data using Textual analysis
-Predictive modeling and backtesting

Students will have the opportunity to apply their skills on real world data sets using toolkits such as tensor flow and sci-kit learn.
Applications will include:
-Credit risk modelling
-Forecasting Financial Markets
-Fraud Detection
-Image analysis for forecasting

About this Module

Learning Outcomes:

1. Understand the limitations of current mainstream financial accounting data as supplied in corporate financial reports
2. Understand the limitation of current mainstream financial data as supplied by established data vendors such as Bloomberg, Factset, or S&P
3. Possess a reasonable domain knowledge and technical understanding of the techniques such as text mining, pattern recognition or data scouting needed to source alternative data
4. Have the ability to assess the real value add (beyond the marketing buzz) that certain types of innovative alternative can provide to portfolio managers or investment strategy designers
5. Demonstrate the ability to develop an investment strategy based on alternative and innovative data
6. Have developed a reasonable capability to assess an alternative data based investment strategy with advanced financial data science measures

Student Effort Hours:
Student Effort Type Hours
Autonomous Student Learning

118
Lectures

24
Tutorial

8
Total

150

Approaches to Teaching and Learning:
Learning will be with a heavy emphasis on practical content with real world examples and data sets and guided tutorials.

Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 

Assessment Strategy  
Description Timing Component Scale Must Pass Component % of Final Grade In Module Component Repeat Offered
Group Work Assignment: Group Work assignment with three separate submissions and a final in-class presentation. Week 1, Week 2, Week 3, Week 4, Week 5, Week 6, Week 7, Week 8, Week 9, Week 10, Week 11, Week 12, Week 14, Week 15 Alternative linear conversion grade scale 40% Yes

100

 Yes

Carry forward of passed components
Yes
 

Resit In Terminal Exam
Summer No
Please see Student Jargon Buster for more information about remediation types and timing. 

Feedback Strategy/Strategies

• Feedback individually to students, post-assessment

How will my Feedback be Delivered?

Students will receive feedback on the tutorials and on their final submitted project.

Name Role
Xiaomeng Wang Tutor
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