25882 AI-powered Investment and Risk Management

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Subject handbook information prior to 2025 is available in the Archives.

UTS: Business: Finance
Credit points: 6 cp

Subject level:

Postgraduate

Result type: Grade and marks

Requisite(s): 24 credit points of completed study in spk(s): C04048 Master of Finance OR 24 credit points of completed study in spk(s): C04258 Master of Finance (Extension) OR 24 credit points of completed study in spk(s): C07021 Graduate Diploma Finance OR 24 credit points of completed study in 24Credit Points spk(s): MAJ08984 c 36cp Finance Major MBA
The lower case 'c' after the subject code indicates that the subject is a corequisite. See definitions for details.
These requisites may not apply to students in certain courses. See access conditions.

Description

Artificial Intelligence (AI) has revolutionised the investment profession, enabling investment managers to process vast amounts of data, identify patterns, and make informed decisions with unprecedented speed and accuracy. The subject provides practical skills in leveraging AI techniques for investment decision-making and risk management in the modern financial markets. Students will explore the intersection of AI and investment management, highlighting the transformative role of AI in enhancing investment strategies, risk analysis, and portfolio optimisation. For instance, robo-advisors have gained popularity as automated investment platforms that utilise AI algorithms to provide personalised investment advice and portfolio management services to individual investors. In quantitative trading, AI models analyse historical data to identify market inefficiencies and execute trades automatically, capitalising on short-term price discrepancies. In this subject, students learn the principles, methodologies, and practical applications of AI in investment and risk management. Through hands-on exercises and projects, students will apply these techniques to analyse financial market data, evaluate investment strategies, construct optimised portfolios, and quantify and manage various financial risks. The course will also address the ethical considerations associated with the use of AI in investment and risk management, such as bias and fairness, interpretability of AI models, and the impact of algorithmic decision-making on market dynamics.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:

1.	Examine AI technologies that enhance investment strategies, optimise portfolios, and improve risk management practices in the financial industry
2.	Evaluate and mitigate ethical risks associated with AI implementation in financial contexts, focusing on bias, fairness, and the interpretability of AI models
3.	Analyse and solve complex finance-related problems using AI-driven approaches
4.	Communicate AI-driven insights effectively to diverse stakeholders
5.	Critically reflect on the impact of AI on market dynamics and the overall investment landscape, considering both opportunities and potential challenges

Course intended learning outcomes (CILOs)

This subject also contributes specifically to the following program learning objectives:

Integrate high-level technical skills in AI and machine learning with an understanding of finance principles, enabling the application of AI-based models, algorithms, and tools to address complex financial problems, optimise processes, and support decision-making in diverse finance domains (4.1)

Contribution to the development of graduate attributes

The subject contributes to the aim of preparing students for fulfilling and effective careers in business and finance by bridging the gap between AI and finance, addressing the industry's emerging needs and equipping students with the necessary knowledge, skills, and ethical considerations to excel in the evolving field of AI in finance.

It contributes particularly to the development of the following graduate attribute(s):

Intellectual rigour and innovative problem solving
Communication and collaboration
Social responsibility and cultural awareness
Professional and technical competence

Teaching and learning strategies

Students will have pre-readings to complete before in class lectures/seminars and discussions, including a mix of chapters, journal articles, industry reports, and press. Additional (optional) extension materials will be provided in the form of recorded videos and further readings to allow students to delve deeper into topics of particular interest. Assessments will be used for students to get feedback on their comprehension of the subject matter. Students will present their application proposals in class to obtain instant feedback and promote the exchange of ideas. Students will work in small groups on empirical exercises to encourage cohort building and peer learning.

Content (topics)

AI in Investment Management
Robo-Advisors and Automated Investment Platforms
AI in Portfolio Optimisation
Risk Management with AI
Future Trends and Challenges

Assessment

Assessment task 1: Empirical Assignment (Group)

Objective(s):	This addresses subject learning objective(s): 3 and 4
Weight:	25%
Length:	Students will submit Python notebook (roughly 9 hours to construct)
Criteria:	Applicability of selected data to the project's objectives Appropriateness of methods used for estimating measures or engineering features Ability to draw meaningful conclusions Clear and concise expression of ideas and findings Balanced involvement of all team members (if applicable)

Assessment task 2: Hackathon and Coding Challenge (Individual)*

Objective(s):	This addresses subject learning objective(s): 1, 2, 3 and 4 This addresses program learning objectives(s): 4.1
Weight:	30%
Length:	15-20 minutes of live demonstration and question time. Students will submit a Python notebook used in their presentation (including visualizations) to be shared with the rest of the class after the presentation.
Criteria:	Applicability of selected data to the subject objectives Ability to draw meaningful conclusions and insights from the analysis Clarity, creativity, and persuasiveness of the presentation and live demonstration Demonstration of technical skills in using AI tools for financial data analysis Appropriate level of integration of AI, finance principles and methods of data analysis, including applications of AI-based models, algorithms, and tools to address complex financial problems Ability to engage with the audience and respond to questions effectively *Note: Late submission of the assessment task will not be marked and awarded a mark of zero.

Assessment task 3: AI Application Proposal (Individual)

Intent:	Part A: AI application proposal (30%) Part B: Critical reflection (15%)
Objective(s):	This addresses subject learning objective(s): 3, 4 and 5
Weight:	45%
Length:	Students will submit a (maximum) 5,000-word report
Criteria:	Feasibility of proposed AI solution Appropriate justification of choices Novelty of the AI-driven solution being proposed. Assessment criteria (Part B) Depth of critical reflection on implications of the use of AI in Investment and Risk Management

Minimum requirements

Students must achieve at least 50% of the subject’s total marks.