Overview:
- Credits:
- 7.5
- Level:
- 4
- Semester:
- Spring
- Subject:
- Finance
- School:
- Business
- Coordinator:
- Dr Richard McGee
FIN42110 Data Science for Trading & Risk Management
Academic Year 2023/2024
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
Show/hide contentOpenClose All
Curricular information is subject to change
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 Type | Hours |
|---|---|
| Autonomous Student Learning | 118 |
| Lectures | 24 |
| Tutorial | 8 |
| Total | 150 |
Requirements, Exclusions and Recommendations
Not applicable to this module.
Module Requisites and Incompatibles
Not applicable to this module.
Assessment Strategy
| Description | Timing | Component Scale | % of Final Grade | ||
|---|---|---|---|---|---|
| Continuous Assessment: Tutorials will include small programming tasks that cover the course material and should prove useful in the individual project. | Throughout the Trimester | n/a | Pass/Fail Grade Scale | No | 30 |
| Assignment: The individual project will cover all aspects of the module: data collection/web scraping, DB creation, Data visualisation and applications of modeling packages. | Coursework (End of Trimester) | n/a | Graded | Yes | 70 |
| Resit In | Terminal Exam |
|---|---|
| Summer | No |
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 |
|---|---|
| Charity Delmus Alupo | Tutor |
| Dr Emmanuel Eyiah-Donkor | Tutor |
| Gabija Zdanceviciute | Tutor |
Discover our Rankings and Accreditations
