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25882 AI in Investment and Risk Management

Assessment 2 Hackathon and Coding Challenge

Objective

The purpose  of this  hackathon  is  to  empower  you  to  develop  an  original  Python-based application or code solution that applies Artificial Intelligence (AI) or Machine Learning (ML) techniques to a relevant issue in Investment and Risk Management. This project is an opportunity to showcase your coding skills, creativity, and understanding of AI/ML in finance.

Assignment Requirements

1. Project Scope:

o  The project does not need to be overly large but should demonstrate innovation and address  a  specific  issue.  Choose  a project  topic that leverages AI/ML techniques  with  a  clear   application  or  implication  in  investment  or  risk management.

o  Your project could cover areas such as, but not limited to:

Portfolio optimization with AI

Risk assessment using ML models

Sentiment analysis for stock predictions

Applying a novel AI approach to forecast asset prices.

Presentation and/or Assessment of Quantitative trading strategies.

Execution  of  ML/AI  model  in  a  specific  case,  but  focusing  on explainability (XAI).

2. Code and Documentation:

o  Develop the code in Python, ensuring it is well-structured and functional.

o  Include textual commentary in markdown cells within Jupyter Notebook with:

Project title and purpose

Overview of the AI/ML technique(s) used

Brief explanation of the code’s functionality

(if relevant) Instructions for running the code and any dependencies

o  Your code should be commented adequately for readability and understanding.

3. Submission:

o  Via the UTS Canvas site, submit a zipped folder or GitHub link containing:

Your Python code and all necessary files

Any data files required to run the code (or instructions to access the data).

4. Class Presentation:

o  Prepare a 5-minute presentation to demonstrate your project to the class, followed by Q&A.

o  Highlight the following:

The problem you addressed and why it’s relevant to finance.

Key AI/ML techniques applied.

Results and potential applications.

o  Showcase a quick demonstration of the code, if possible.

Evaluation Criteria

Your project will be evaluated relative to your peers, with the highest marks awarded for the best presentations and ideas.

• Innovation and Relevance (30%):

o  Originality of the project idea.

o  Relevance to investment and risk management.

• Technical Execution (30%):

o  Proper implementation of AI/ML techniques.

o  Quality and structure of the Python code.

o  Ability to effectively process and analyse data (where applicable).

• Documentation and Clarity (20%):

o  Clear, detailed explanations ofthe project’s purpose, AI/ML methods, and code functionality.

• Presentation (20%):

o  Effectiveness in communicating the project’s purpose and results.

o  Clarity in explaining the AI/ML techniques and financial relevance.

o  Engagement and quality of the demonstration.

Deadline and Submission

• In-Class Presentation: Be prepared to present your project in class on Week 9 (1 October 2025) and Week 10 (8 October 2025).

• Code Submission: Upload your completed project by Wednesday, 1 October 2025, at 8 AM on Canvas.

Good luck, and we look forward to seeing your innovative solutions in AI for Investment and Risk Management!