FIN40090 Advanced Statistical Computing Methods for Finance

Academic Year 2023/2024

Advanced Topics course in Modeling Financial Data and using the result for Investing and Decisionmaking. The topics typically focus on Managing and Modeling Financial Series data, Classification, Forecasting, and Optimisation Methods, with an emphasis on Statistical Reliability of forecasts. Specific tools and methods include Bayesian Decision Theory, Linear Discriminant Analysis, Support Vector Machines, Neural Networks, Genetic Algorithms/Optimisation, Regularisation and Resampling Methods, Regression and Classification Trees and Forests, Deep Learning, and some Information-Theoretic criteria for combining forecasting and investing.
A section on Large Language Models (LLM's) such as ChatGPT will be include (source: TBA)

Next-Generation frontiers such as Quantum Machine Learning may be discussed at an abstract level.

Computer platforms utilised will be Julia (a freely downloadable package for numerical computation and machine learning, with syntax very similar to MATLAB) and either Python or the statistical computing package R (where previous knowledge of these languages is not assumed except possibly Python).

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Curricular information is subject to change

Learning Outcomes:

On completing the module, students will (i) be able to demonstrate understanding of a range of optimising, modelling and forecasting techniques, (ii) be able to apply these techniques to a range of financal problems especially algorithmic trading, (iii) have acquired a high-level of expertise in using the JULIA &R programming packages, (iv) be able to critically assess the results of research studies which employ the algorithms covered on the course.
Students should understand the basics of Large Language Models (LLM's) such as ChatGPT and be aware of the general issues
their recent emergence has raised, as well as their direct applicability to Finance.
Additionally, students should demonstrate a high-level understanding of current capabilities of and directions in Machine Learning and Artificial Intelligence (including LLM's) as well as having some idea about their likely impact on Business and Society in the near future.

Indicative Module Content:

Introduction to Statistical Learning in R by Hastie and Tibshirani, et. al (2nd ed.) and R package and R Studio
Jupyter Notebooks (for JUlia PYThon R)
Machine Learning Texts TBA
Julia package release 1.9.3 or higher
Python - recent release (TBC)

Student Effort Hours: 
Student Effort Type Hours
Small Group

36
Specified Learning Activities

44
Autonomous Student Learning

85
Total

165
Approaches to Teaching and Learning:
Lectures, in-class coding lab, in-class discussions
Tutorials and problem-based learning
Peer and group work
Critical writing
Presentation 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Class Test: Optional -
Near Middle of Term
-may be replaced by Final Exam mark		 Unspecified n/a Alternative linear conversion grade scale 40% No

20
Continuous Assessment: Various Statistical/Machine Learning/Forecasting Group Projects.
with Individual component (TBA)		 Varies over the Trimester n/a Alternative linear conversion grade scale 40% No

40
Examination: Final Examination 2 hour End of Trimester Exam No Alternative linear conversion grade scale 40% No

40

Carry forward of passed components
Yes
 
Resit In Terminal Exam
Summer Yes - 2 Hour
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment
• Online automated feedback
• Peer review activities
• Self-assessment activities

How will my Feedback be Delivered?

All Continuous Assessment will be Group-Based according to the following steps: 1. Each member of the group submits online a draft of required work or individual contribution to group work (time schedule provided, prior to due date) 2. Groups complete required Assignment or Task 3. Group Submission Members of a Group shall all receive the same grade, except in cases where one or more members' early drafts are consistently lacking relative to the others'. Feedback will be online (with comments and explanation where needed) Final Examination will be assessed with online automated and partially individualised feedback.

Students should download Introduction to Statistical Learning in R (2nd Ed.) by James, Witten, Hastie, Tibshirani:
The Original version of the Book was in R, but we may use the Python version (TBC). Both are freely available online.

Students should download and install (or update) Julia (latest Stable Release - currently 1.9.3 or above):
https://julialang.org/downloads/

Students should download and install one of the following:
1. Visual Studio Code (preferred, from https://code.visualstudio.com/Download) add Julia and Jupyter Extensions from menu
2. JupyterLab (from: https://jupyter.org/ or included automatically with Anaconda)
3. Jupyter Notebook, (from: https://jupyter.org/ or included automatically with Anaconda)

Before running any of the above, start Julia directly and type the following commands

] add CSV
] add IJulia

quit by typing (Ctrl-D)

The portion of the class not taught in Julia will either be taught in Python or R (TBC)

Students should have a recent version of Python installed.

If we should decide to use R, students will need to download and install R and R Studio (free version):
https://www.r-project.org/
https://www.rstudio.com/
Name Role
Mr Shivam Agarwal Tutor
Mr Stephen Keenan Tutor
Illia Kovalenko Tutor
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 
Spring
      
Lecture Offering 1 Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 Wed 14:00 - 15:50