University of Technology Sydney

25882 AI-powered Investment and Risk Management

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.



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.

Detailed subject description.

Access conditions

Note: The requisite information presented in this subject description covers only academic requisites. Full details of all enforced rules, covering both academic and admission requisites, are available at access conditions and My Student Admin.