Financial Trading Systems

General Information

This subject provides the opportunity to learn the tools and strategies used by investment and hedge fund managers to invest and trade in a number of financial instruments, including equities, futures, FX and ETFs in both low and high-frequency environments. Using financial data drawn from a variety of sources including Bloomberg, you will learn to model and benchmark these strategies using Python.  The overall aim of this applied, research-focused subject is to explore quantitative trading strategies used to capitalise on market anomalies.

  • Academic unit: Bond Business School
    Subject code: DTSC71-305
    Subject title: Financial Trading Systems
    Subject level: Postgraduate
    Semester/Year: September 2020
    Credit points: 10.000
  • Timetable: https://bond.edu.au/timetable
    Delivery mode: Standard
    Workload items:
    • Lecture: x12 (Total hours: 24) - Weekly Lecture
    • Computer Lab: x12 (Total hours: 24) - Weekly Computer Laboratory
    • Personal Study Hours: x12 (Total hours: 72) - Study time and reviewing materials
    Attendance and learning activities: Attendance at all class sessions is expected. Students are expected to notify the instructor of any absences with as much advance notice as possible.
  • Prescribed resources:

    Journals

    • Bruce J. Vanstone and Tobias Hahn (2015). Australian momentum: performance, capacity and the GFC effect. Accounting & Finance
    • George A. Akerlof (1970). The Market for "Lemons": Quality Uncertainty and the Market Mechanism,. The Quarterly Journal of Economics, 488-500
    • Martin Sewell (2011). Characterization of Financial Time Series,. UCL Research Notes
    • Brian Hurst (2012). A Century of Evidence on Trend-Following Investing. AQR Capital Management
    • Marshall, Ben R., and Rachael M. Cahan (2005). Is the 52-week high momentum strategy profitable outside the US?. Applied Financial Economics 1259-1267
    • Wilma de Groot,Joop Huij, Weili Zhou (2012). Another look at trading costs and short-term reversal profits. Journal of Banking & Finance 371-382
    • Cole Wilcox (2005). Does Trend Following Work on Stocks?. c/ Blackstar Funds, LLC
    • Tom Smith and Kathleen Walsh (2013). Why the CAPM is Half-Right and Everything Else is Wrong. ABACUS
    • Heiko Jacobs (2015). What explains the dynamics of 100 anomalies?. Journal of Banking & Finance 65-85
    After enrolment, students can check the Books and Tools area in iLearn for the full Resource List.
    iLearn@Bond & Email: iLearn@Bond is the online learning environment at Bond University and is used to provide access to subject materials, lecture recordings and detailed subject information regarding the subject curriculum, assessment and timing. Both iLearn and the Student Email facility are used to provide important subject notifications. Additionally, official correspondence from the University will be forwarded to students’ Bond email account and must be monitored by the student.

    To access these services, log on to the Student Portal from the Bond University website as www.bond.edu.au
Academic unit: Bond Business School
Subject code: DTSC71-305
Subject title: Financial Trading Systems
Subject level: Postgraduate
Semester/Year: September 2020
Credit points: 10.000

Enrolment requirements

Requisites:

Nil
Assumed knowledge:

Assumed knowledge is the minimum level of knowledge of a subject area that students are assumed to have acquired through previous study. It is the responsibility of students to ensure they meet the assumed knowledge expectations of the subject. Students who do not possess this prior knowledge are strongly recommended against enrolling and do so at their own risk. No concessions will be made for students’ lack of prior knowledge.

Possess demonstrable knowledge in basic data science concepts and techniques to the level of a unit such as DTSC71-200 Data Science
Restrictions:

Nil

Assurance of learning

Assurance of Learning means that universities take responsibility for creating, monitoring and updating curriculum, teaching and assessment so that students graduate with the knowledge, skills and attributes they need for employability and/or further study.

At Bond University, we carefully develop subject and program outcomes to ensure that student learning in each subject contributes to the whole student experience. Students are encouraged to carefully read and consider subject and program outcomes as combined elements.

Program Learning Outcomes (PLOs)

Program Learning Outcomes provide a broad and measurable set of standards that incorporate a range of knowledge and skills that will be achieved on completion of the program. If you are undertaking this subject as part of a degree program, you should refer to the relevant degree program outcomes and graduate attributes as they relate to this subject.

Find your program

Subject Learning Outcomes (SLOs)

On successful completion of this subject the learner will be able to:

  1. Apply advanced tools and algorithmic approaches to model risk/reward relationships.
  2. Critically evaluate core issues related to trading and investing, and key trends in financial markets.
  3. Critically evaluate the use of algorithmic approaches to advanced systems design.

Generative Artificial Intelligence in Assessment

The University acknowledges that Generative Artificial Intelligence (Gen-AI) tools are an important facet of contemporary life. Their use in assessment is considered in line with students’ development of the skills and knowledge which demonstrate learning outcomes and underpin study and career success. Instructions on the use of Gen-AI are given for each assessment task; it is your responsibility to adhere to these instructions.

  • Type Task % Timing* Outcomes assessed
    Computer-Aided Examination (Open) Mid-Semester examination 20% Week 8 (Mid-Semester Examination Period) 2,3
    Skills Assignment Case analysis of financial data using Python 10% Week 7 1
    Skills Assignment Case analysis of financial data using Python 10% Week 12 1
    Skills Assignment Case analysis of financial data using Python 30% Week 12 1
    Computer-Aided Examination (Open) Comprehensive final examination 30% Week 13 2,3
    • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
    • C = Students must reach a level of competency to successfully complete this assessment.

  • Assessment criteria

    High Distinction 85-100 Outstanding or exemplary performance in the following areas: interpretative ability; intellectual initiative in response to questions; mastery of the skills required by the subject, general levels of knowledge and analytic ability or clear thinking.
    Distinction 75-84 Usually awarded to students whose performance goes well beyond the minimum requirements set for tasks required in assessment, and who perform well in most of the above areas.
    Credit 65-74 Usually awarded to students whose performance is considered to go beyond the minimum requirements for work set for assessment. Assessable work is typically characterised by a strong performance in some of the capacities listed above.
    Pass 50-64 Usually awarded to students whose performance meets the requirements set for work provided for assessment.
    Fail 0-49 Usually awarded to students whose performance is not considered to meet the minimum requirements set for particular tasks. The fail grade may be a result of insufficient preparation, of inattention to assignment guidelines or lack of academic ability. A frequent cause of failure is lack of attention to subject or assignment guidelines.

    Quality assurance

    For the purposes of quality assurance, Bond University conducts an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Type Task % Timing* Outcomes assessed
Computer-Aided Examination (Open) Mid-Semester examination 20% Week 8 (Mid-Semester Examination Period) 2,3
Skills Assignment Case analysis of financial data using Python 10% Week 7 1
Skills Assignment Case analysis of financial data using Python 10% Week 12 1
Skills Assignment Case analysis of financial data using Python 30% Week 12 1
Computer-Aided Examination (Open) Comprehensive final examination 30% Week 13 2,3
  • * Assessment timing is indicative of the week that the assessment is due or begins (where conducted over multiple weeks), and is based on the standard University academic calendar
  • C = Students must reach a level of competency to successfully complete this assessment.

Study Information

Submission procedures

Students must check the iLearn@Bond subject site for detailed assessment information and submission procedures.

Policy on late submission and extensions

A late penalty will be applied to all overdue assessment tasks unless an extension is granted by the subject coordinator. The standard penalty will be 10% of marks awarded to that assessment per day late with no assessment to be accepted seven days after the due date. Where a student is granted an extension, the penalty of 10% per day late starts from the new due date.

Academic Integrity

University’s Academic Integrity Policy defines plagiarism as the act of misrepresenting as one’s own original work: another’s ideas, interpretations, words, or creative works; and/or one’s own previous ideas, interpretations, words, or creative work without acknowledging that it was used previously (i.e., self-plagiarism). The University considers the act of plagiarising to be a breach of the Student Conduct Code and, therefore, subject to the Discipline Regulations which provide for a range of penalties including the reduction of marks or grades, fines and suspension from the University.

Bond University utilises Originality Reporting software to inform academic integrity.

Feedback on assessment

Feedback on assessment will be provided to students within two weeks of the assessment submission due date, as per the Assessment Policy.

Accessibility and Inclusion Support

If you have a disability, illness, injury or health condition that impacts your capacity to complete studies, exams or assessment tasks, it is important you let us know your special requirements, early in the semester. Students will need to make an application for support and submit it with recent, comprehensive documentation at an appointment with a Disability Officer. Students with a disability are encouraged to contact the Disability Office at the earliest possible time, to meet staff and learn about the services available to meet your specific needs. Please note that late notification or failure to disclose your disability can be to your disadvantage as the University cannot guarantee support under such circumstances.

Additional subject information

As part of the requirements for Business School quality accreditation, the Bond Business School employs an evaluation process to measure and document student assessment as evidence of the extent to which program and subject learning outcomes are achieved. Some examples of student work will be retained for potential research and quality auditing purposes only. Any student work used will be treated confidentially and no student grades will be affected.

Subject curriculum

Approved on: Oct 21, 2020. Edition: 1.3
Last updated: Oct 10, 2022
By browsing this site you accept that cookies are used to improve and personalise our services to you for a better experience on our website and for social activity. If you continue, we will assume that you agree to our use of cookies statement
I Agree