University of Technology Sydney

20509 Applied Portfolio Management

Warning: The information on this page is indicative. The subject outline for a particular session, location and mode of offering is the authoritative source of all information about the subject for that offering. Required texts, recommended texts and references in particular are likely to change. Students will be provided with a subject outline once they enrol in the subject.

Subject handbook information prior to 2024 is available in the Archives.

UTS: Business: Finance
Credit points: 6 cp

Subject level:


Result type: Grade and marks

There are course requisites for this subject. See access conditions.


This subject provides a hands-on experience of the practice of modern portfolio management. Students are introduced to the use of the Python coding language to design and test portfolio management strategies for stocks and other major financial asset classes. In terms of theory, the subject explores the economic fundamentals of the predictability of asset prices and the development process of algorithmic portfolio management strategies. The subject also explores the effects of technological innovations in the field of machine learning and artificial intelligence on portfolio management. Hands-on weekly coding workshops are based on the construction and testing of portfolio strategies based on real market data. No prior knowledge of coding is required.

Subject learning objectives (SLOs)

Upon successful completion of this subject students should be able to:
1. understand the effect of technological innovation on the portfolio management industry
2. apply knowledge of the development process of a quantitative portfolio strategy
3. analyse risk and return opportunities of a portfolio strategy using quantitative tools

Contribution to the development of graduate attributes

The subject contributes to the aim of preparing students to commence a fulfilling and effective career in business, especially in investment management. Its specific contributions are to enable students to develop their knowledge and understanding of the theory and practice of portfolio management.

Teaching and learning strategies

Every module includes 60-90 minutes of preparatory work covering the theoretical aspects of the subject (with readings and short videos), a 90 minutes live lecture where the instructor discusses the development of a number of portfolio strategies and, finally a tutorial where students, working in small groups, replicate these investment strategies based on the code presented in class.

Students will receive timely feedback after each assessment and ongoing feedback on their progress during class and tutorials.

Content (topics)

  • The quantitative portfolio management process.
  • Python coding for portfolio management and trading.
  • Factors driving equity prices.
  • Quantitative stock selection and trading.
  • Machine Learning and Artificial Intelligence in portfolio management


Assessment task 1: Assignments (Individual)


This addresses subject learning objective(s):

2 and 3

Weight: 50%
  • Precision and skill in using industry-standard coding tools to develop quantitative portfolio strategies
  • Ability to present the results of the development process in an effective way

Assessment task 2: Final Exam (Individual)


This addresses subject learning objective(s):

1 and 3

Weight: 50%

The exam is graded on the following criteria:

  • Understanding of the financial principles underpinning quantitative portfolio strategies
  • Ability to assess the impact of technology trends on the asset management industry
  • Identification of the effect of internal processes and business practices on the development of quantitative portfolio strategies
  • Precision in the understanding of the technical aspects of the most widely used quantitative tools for portfolio development

Minimum requirements

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

Required texts

The reference materials for all the lectures are taken from the following books. All the books are available for free online via the O'Reilly Learning platform accessible via the UTS library. More information available in Canvas.

[NRNG] Narang, R.K., 2013. Inside the black box: A simple guide to quantitative and high frequency trading. John Wiley & Sons.

[HILP1] Hilpisch, Y., 2019. Python for finance: mastering data-driven finance (2nd Edition). O'Reilly Media.

[HILP2] Hilpisch, Y., 2020. Python for Algorithmic Trading: From Idea to Cloud Deployment. O'Reilly Media.

[CHNC] Chincarini, L. and D. Kim, 2006. Quantitative Equity Portfolio Management. McGraw-Hill.

[CHAN] Chan, E., 2013, Algorithmic Trading: Winning Strategies and Their Rationale. John Wiley & Sons.

Recommended texts

The following books are good reference material for anybody interested in quantitative portfolio management:

  • Hilpisch, Y., 2020. Artificial Intelligence in Finance. O'Reilly Media.
  • Géron, A., 2020, Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd Edition). O'Reilly Media.
  • Lo?pez de Prado, M., 2018, Advances in financial machine learning. John Wiley & Sons.
  • McKinney, W., 2017. Python for Data Analysis (2nd Edition). O'Reilly Media.
  • Guida, T., 2019, Big Data and Machine Learning in Quantitative Investment. John Wiley & Sons.


All references should follow the same style, preferably Harvard reference style.