ECON3206 Financial Econometrics - 2022

Subject Code
ECON3206
Study Level
Undergraduate
Commencing Term
Term 2
Total Units of Credit (UOC)
6
Delivery Mode
On Campus and Online
School
Economics
The course outline is not available for current term. To view outlines from other years and/or terms, visit the archives .

1. Course Details

Summary of Course

This course is concerned with the special statistical concerns that arise when modelling time-series data, such as commodity/asset prices, stock market returns, interest rates or exchange rates. Topics include key characteristics of financial data, concepts of volatility and risk, modelling time-varying volatility (ARCH models), and modelling relationships among financial series. The knowledge and methods acquired in this course are particularly useful and sought after in the public (government) and private (industry) financial sectors.

Teaching Times and Locations

Please note that teaching times and locations are subject to change. Students are strongly advised to refer to the Class Timetable website for the most up-to-date teaching times and locations.

View course timetable

Course Policies & Support

The Business School expects that you are familiar with the contents of this course outline and the UNSW and Business School learning expectations, rules, policies and support services as listed below:

  • Program Learning Outcomes
  • Academic Integrity and Plagiarism
  • Student Responsibilities and Conduct
  • Special Consideration
  • Protocol for Viewing Final Exam Scripts
  • Student Learning Support Services

Further information is provided in the Assessment and Policies and Support sections.

Students may not circulate or post online any course materials such as handouts, exams, syllabi or similar resources from their courses without the written permission of their instructor.

Course Aims and Relationship to Other Courses

The course aims to provide students with the basic framework for modelling financial time series data. In particular, it will benefit students in terms of:

  1. Developing their ability to model the expected mean and volatility in financial data as a means to a more informed assessment of the risk and return associated with different investment strategies;
  2. Developing an awareness of the empirical evidence supporting alternative models of asset price determination;
  3. Developing skills to build forecasting models for financial assets at the short and long-run horizons;
  4. Developing skills to evaluate the accuracy of competing forecasts;
  5. Developing their proficiency with Python is required to actually model financial data in practice. By the end of the course, students should be proficient in Python to analyse financial data.

This course is offered as part of the economics streams in the BCom and BEc degrees (ECON3206). The prerequisite for ECON3206 is ECON2209 Business Forecasting (or equivalent).

A good grasp of basic mathematical statistics and linear algebra is necessary for succeeding in the course. Some familiarity with real analysis would make it easier to understand the technical details of the material in the course but is not required. A previous course in time series is not required or assumed. However, a basic knowledge of estimation and inference in linear regression models will be assumed.

Student Learning Outcomes

The Course Learning Outcomes (CLOs) are what you should be able to demonstrate by the end of this course, if you participate fully in learning activities and successfully complete the assessment items.

CLOs also contribute to your achievement of the Program Learning Outcomes (PLOs), which are developed across the duration of a program for all students. More information on coursework PLOs is available under Policies and Support. PLOs are, in turn, directly linked to UNSW graduate capabilities and the aspiration to develop “globally focussed graduates who are rigorous scholars, capable of leadership and professional practice in an international community”.

For PG research PLOs please refer to the UNSW HDR Graduate Attributes and LearningOutcomes.

The following table shows how the CLOs for this course relate to the overall PLOs and indicates where each CLO and PLO is assessed:

Course Learning Outcomes Program Learning Outcomes Course Assessment Item
On successful completion of the course, you should be able to: This course helps you to develop the following Program Learning Outcomes: This learning outcome will be assessed in the following items:

1. Explain various assumptions, concepts, principles, and methodologies underlying time series models for financial data; understand different estimation methodologies, and apply programming tools such as Python and R to real-world financial data

PLO 1: Business knowledge
PLO 6: Global and cultural competence
  • Case Study Part I & II
  • Online Quiz I & II

2. Use learned statistical techniques and skills to analyse, interpret and present relevant data

PLO 1: Business knowledge
PLO 2: Problem solving
PLO 3: Business communication
PLO 6: Global and cultural competence
  • Case Study Part I & II

3. Apply financial econometric tools to modelling, estimation, inference and forecasting of financial data

PLO 1: Business knowledge
PLO 2: Problem solving
PLO 3: Business communication
PLO 6: Global and cultural competence
  • Case Study Part I & II

4. Construct written work that is logically and professionally presented

PLO 3: Business communication
  • Case Study Part I

5. Critically evaluate empirical econometric work

PLO 3: Communication
  • Case Study Part I &II

6. Present comprehensible results to a non-technical audience, and communicate ideas in a clear and concise manner

PLO 3: Business communication
PLO 4: Teamwork
PLO 7: Leadership development
  • Case Study Part I

7. Work collaboratively to complete a task

PLO 4: Teamwork
PLO 7: Leadership development
  • Case Study Part I

8. Identify and assess ethical considerations in problems in economics, finance, and business

PLO 1: Business knowledge
PLO 2: Problem solving
PLO 5: Responsible business practice
PLO 6: Global and cultural competence
  • Case Study Part I &II
  • Video presentation

9. Formulate economic and business interactions in analytical terms and analyse them using tools provided by the theory

PLO 1: Business knowledge
PLO 2: Problem solving
PLO 5: Responsible business practice
PLO 6: Global and cultural competence
  • Online Quizzes
  • Case Study II
  • Video Presentation

2. Staff Contact Details

Position Title Name Email Location Phone Consultation Times
Lecturer-in-chargeDrRachida OuysseRoom 432, UNSW Business School9385 3321Thursdays 12:00 -14:00 (or by appointment)
TutorMsRoelien Christien Timmer
–
–
Nil
TutorMrAsror Nigmonov
–
–
Nil

Communication with staff

You should feel free to contact your lecturer(s) about any academic matter. However, it is strongly encouraged, for efficiency, that all enquiries about the subject material be made at lecture forums or tutorials or during online consultation time. Discussion of course subject material will not be entered into via lengthy emails.

You should expect responses to email correspondence within 48 hours, but not over weekends. Before communicating with staff, please check relevant components of this course outline as this will provide answers to most common questions. You should also regularly check the course website for announcements and reminders about upcoming events and deadlines. Please note that the lecturer has no advance notice of the date and time of the final exam.

Student Enrolment Requests

Students can vary their own enrolment (including switching lecture streams or tutorials) via myUNSW until the end of Week 1. In general, most other student enrolment requests should be directed to The Nucleus: Student Hub (formerly Student Central). These include enrolment in full courses or tutorials, course timetable clashes, waiving prerequisites for any course, transfer-of-credit (international exchange, transfer to UNSW, cross-institutional study, etc.), or any other request which requires a decision about equivalence of courses and late enrolment for any course. Where appropriate, the request will be passed to the School Office for approval before processing. Note that enrolment changes are rarely considered after Week 2 classes have taken place.

3. Learning and Teaching Activities

Use of your Webcam and Digital Devices: If you enrol in an online class, or the online stream of a hybrid class, teaching and associated activities will be conducted using Teams, Zoom, or similar a technology. Using a webcam is optional, but highly encouraged, as this will facilitate interaction with your peers and instructors. If you are worried about your personal space being observed during a class, we encourage you to blur your background or make use of a virtual background. Please contact the Lecturer-in-Charge if you have any questions or concerns.

Some courses may involve undertaking online exams for which your own computer or digital devices will be required. Monitoring of online examinations will be conducted directly by University staff and is bound by the University's privacy and security requirements. Any data collected will be handled accordance with UNSW policies and standards for data governance. For more information on how the University manages personal information please refer to the UNSW Student Privacy Statement and the UNSW Privacy Policy.

Approach to Learning and Teaching in the Course

​The philosophy underpinning this course and its teaching and learning strategies is based on the “Guidelines on Learning that Inform Teaching at UNSW”, which may be viewed at: https://teaching.unsw.edu.au/guidelines. Specifically, the lectures, tutorials and assessment have been designed to appropriately challenge students and support the achievement of the desired learning outcomes. A climate of inquiry and dialogue is encouraged between students and teachers and among students (in and out of class). The lecturer-in-charge and tutors aim to provide meaningful and timely feedback to students to improve learning outcomes.

This is not a course where you can become proficient just by observing. You will need to get involved in class activities - evaluating information, and asking and answering questions. You also must learn to organise your independent study and practice enough problems to gain a thorough understanding of concepts and how to apply them. You must get your hands dirty and learn by doing.

Students are expected to:

  • Put a consistent effort into learning activities throughout the term by preparing for the regular tutorial tasks;
  • Take a responsible role in participating in tutorials;
  • Develop communication skills through engaging in discussion forums and preparing for video presentation and project milestones;
  • Concentrate more on understanding how and why to use formulas, and less on memorising them.

Learning Activities and Teaching Strategies

The material given in the lecture schedule, the content of the lectures, and the content of the tutorial program define the examinable content of the course.

Lectures

Lectures will be delivered online. These lectures will mainly be in the form of synchronous live-streamed lecture on Tuesdays during the scheduled time 16-17:30 and a pre-recorded second lecture posted on Thursdays. Prior to the live-streamed lecture, there is a half an hour QandA session held online. This will enable us to debrief the content covered in previous weeks and focus the discussion during the live session. All information about lecture delivery and platforms used will be available on Moodle.

The purpose of lectures is to provide a logical structure for the topics that make up the course, to emphasise the important concepts and methods of each topic, and to provide relevant examples to which the concepts and methods are applied.

Not all of the material in the textbook is included in the lectures, and not all of the material in the lectures is covered in the textbook. The lectures contain all the course material taught at the level required for the assessment tasks and are your guide to the course content.

As not all topics will be presented extensively, students should refer to the textbook and relevant readings for further details.

This is a lecture-based course, which will proceed as quickly or slowly as is required by the complexity of the content and students' needs.

Class attendance is very important for understanding the lecture notes. Students are expected to develop the skills and ability to derive the results on their own. Memorising formulae and final results is not a learning outcome we seek for this course; assessments in the course reflect real-life scenarios and only an ability to develop and understand these results will ensure success.

Tutorial workshops

Tutorials will be delivered live synchronously during the scheduled times for each session. Please make every effort to attend your online session. The purpose of the tutorial program is to enable you to raise questions about difficult topics or problems encountered in your studies. You must not expect another lecture but must come prepared with informed questions of your own. Tutorials afford you the opportunity to send through your questions, raise your hand and have your unanswered questions addressed by your tutor.

The more you read the more you know, but the more you practice the more you learn and understand. Accordingly, the key to the understanding of this course is problem solving. Tutorial discussion will normally be based on a sequence of exercise sheets that will be distributed regularly during the course. You are expected to make a serious attempt at all questions on an exercise sheet before attending your tutorial session. It will not be possible to discuss all the problems set in the allotted time, and you should not expect all questions to be solved in-depth at the tutorials. Some tutorial exercises will require the use of statistical software to undertake estimation of financial models and analysis of the data. This course uses Python which will be integrated into the lectures, tutorials and other activities.

In tutorials, some students will be randomly chosen to discuss their attempts to answer the tutorial problems. The aim of these discussions is to encourage discussion within the classroom and to solve the issues you and your classmates have encountered with the problems.

Out-of-Class Study

All activities and resources necessary for you to complete the necessary out-of-class study are available online.

While students may have preferred individual learning strategies, most learning will be achieved outside of class time. Lectures can only provide a structure to assist your study, and tutorial time is limited.

A recommended strategy (on which the provision of the course materials is based) would consist of:

  • Reading the relevant chapter(s) of the text/notes/slides and other required material (if any) before the lecture. This will give you a general idea of the topic area;
  • Attending lectures, where the context of the topic in the course and the important elements of the topic are identified, and the relevance of the topic is explained;
  • Attending tutorials, having attempted the tutorial questions beforehand;
  • Get dirty with Python coding and start early. You have to start attempting coding from the early weeks. Labs will be posted on Ed. Complete the Python-related activities even if they are not part of an assessment.

5. Course Resources

The website for this course is on the Ed platform: https://edstem.org. Access and details about this platform will be available on Moodle. Lecture notes, lecture slides and tutorial questions, with additional readings, will be posted on Ed. All the assessment information and links to lecture recordings and videos will also be accessed from Ed.

Submission of the assessments will be via UNSW Moodle.

Lecture notes provide concise descriptions of concepts and topics to be covered in this course, but cannot be used as a substitute for reading the textbook and assigned readings. The assigned readings expand on the concepts that can only be highlighted in the lectures.

Textbook

The main textbook for this course is:

  • Brooks, Chris, Introductory Econometrics for Finance, Cambridge University Press. Third (2014) or Fourth (2019) Edition.

This book is recommended, but it is not mandatory. The book is written at an introductory level and covers most of the material we will discuss in class.

Additional Useful References

  • Handbook of Financial Econometrics Volume 1 by Yacine Ait-Sahalia and Lars Peter Hansen ISBN-13: 978-0444508973
  • [BBL] Breitung, J., Bruggemann, R. and H. Lutkepohl, 2004, "Structural Vector
  • [Diebold] Forecasting in Economics, Business, Finance and Beyond’, by Francis X. Diebold, Edition 2015, available for free download at http://www.ssc.upenn.edu/~fdiebold/Teaching221/Forecasting.pdf
  • [Johnston] Jack Johnston and John Dinardo, Econometric Methods (fourth edition), McGraw-Hill, 1997.
  • Autoregressive Modeling and Impulse Responses", in Applied Time Series Econometrics, Lutkepohl, H. and M. Kratzig (eds.), Chapter 4.
  • [Enders] Enders, W., 2010, Applied Time Series Analysis (third edition), Wiley,
  • [Gujarati] Gujarati, D.N., and D.C. Porter, 2009, Basic Econometrics (5fth edition), McGraw-Hill,
  • [Lutkepohl] Lutkepohl, H., 2004, "Vector Autoregressive and Vector Error Correction Models", in Applied Time Series Econometrics, Lutkepohl, H. and M. Kratzig (eds.)
  • [Verbeek] Verbeek, M., 2012, A Guide to Modern Econometrics (fourth edition), John Wiley & Sons
  • Campbell, J.Y., A.W. Lo, and A.C. MacKinlay (1997). The Econometrics of Financial Markets. Princeton University Press.
  • Tsay, Ruey S. (2002), Analysis of Financial Time Series, John Willey & Sons.

Journal Articles (Advanced level but accessible)

  • Berndt, E., Hall, B., Hall, R. & Hausman, J. (1974), `Estimation and inference in nonlinear structural models', Annals of Economic and Social Measurement 3/4, 653-665;
  • Bollerslev, T. (1986), `Generalized autoregressive conditional heteroskedasticity', Journal of Econometrics 31, 307-327;
  • Cont, R., (2001) Empirical properties of asset returns: stylized facts and statistical issues, Quantitative Finance 1, 223–236;
  • Diebold, F. X. & Mariano, R. S. (1995), `Comparing predictive accuracy', Journal of Business and Economic Statistics 13(3), 253-263;
  • Engle, R.F., (2001) GARCH 101: The Use of ARCH/GARCH Models in Applied Econometrics, Journal of Economic Perspectives, 15(4), 157-168;
  • Kunze, F. Predicting exchange rates in Asia: New insights on the accuracy of survey forecasts. Journal of Forecasting. 2020; 39: 313– 333. https://doi.org/10.1002/for.2628
  • Lee T.-H. & Bao Y., Saltoglu B., (2007) Comparing density forecast models, Journal of Forecasting, 26(3), 203-225;
  • Engle (1982), `Autoregressive conditional heteroscedasticity with estimates of the variance of united kingdom inflation', Econometrica 50, 987-1007;
  • Giacomini, R. & White, H. (2006), `Tests of conditional predictive ability', Econometrica 74(6), 1545-1578;
  • Ng, S. & Perron, P. (2005), `A note on the selection of time series models', Oxford Bulletin Of Economics And Statistics 67, 115-134.
  • Sharpe, W.F., (1991) Capital Asset Prices with and without Negative Holdings, Journal of Finance, 46(2), 489-509;

COMPUTING

An essential component of this course is learning to apply financial analysis tools to real financial data. You will use software to execute all the tasks needed for the tutorial problems and for your Case Study part I and II.

There are many statistical software packages that are suitable for the analysis of time-series data in general, and financial data in particular. In this course we aim to equip students with strong computing skills by the completion of the term. We therefore encourage learning and using a top-of-league software like Python. Python is one of the leading software packages used in industry and financial companies. Having experience with software like Python on your CV will send a clear signal to employers.

We will provide you with support to learn Python. Answers to tutorial questions will be demonstrated in Python and helpful videos will be made available.

If you have already invested in another software like R or Stata, you can use them. However, we encourage you to take this opportunity to learn Python.

If you plan to use Stata, please check your access via MyAccess.

Python learning resources

6. Course Evaluation & Development

Feedback is regularly sought from students and continual improvements are made based on this feedback. At the end of this course, you will be asked to complete the myExperience survey, which provides a key source of student evaluative feedback. Your input into this quality enhancement process is extremely valuable in assisting us to meet the needs of our students and provide an effective and enriching learning experience. The results of all surveys are carefully considered and do lead to action towards enhancing educational quality.

​The School of Economics strives to be responsive to student feedback. If you would like more information on how the design of this course and changes made to it over time have taken students’ needs and preferences into account, please contact the Director of Education at the School of Economics.

The LIC takes students' feedback very seriously and welcomes every constructive comment. The course continues to adapt and change to meet students' expectations and aspirations.

There are two main changes undertaken this term to address some of the concerns our students have had in the past.

First, we have changed the assessment structure to reflect how students interacted with the learning process in the past. We have decided to remove the final examination in T2 2021. After the move to fully online delivery in T2 2020, the final exam in this course was designed to create independent and individual exam papers. The exam was designed to replicate a real business situation where you have to act independently to solve a problem using personalised data. The final exam was set as a 24-hour task. However, from feedback received from students, we learnt that while the exam was useful to develop those industry skills for critical thinking, the time frame was too short to learn from this task and achieve its learning objectives. Therefore, we have decided to change the timeline on this task and transform it into a task completed over 4 weeks - what you now see as the Case Study.

Second, we have decided to give more importance to the video assignment in the new structure. In the past, it only contributed 10% to students' course marks, but from observing the amount of dedication and the quality of output produced by students, it is only fair to increase that weight to 15%.

Third,  course uses Python for all of the computing work including the Case Study Parts 1 and 2. In T2 2022, we have continued to integrate more Python resources into the weekly activities in the form of weekly Python labs to support your gradual learning of the software.

Consent for De-Identified Data to be Used for Secondary Research into Improving Student Experience

To enhance your student experience, researchers at UNSW conduct academic research that involves the use of de-identified student data, such as assessment outcomes, course grades, course engagement and participation, etc. Students of this course are being invited to provide their consent for their de-identified data to be shared with UNSW researchers for research purposes after the course is completed.

Providing consent for your de-identified data to be used in academic research is voluntary and not doing so will not have an impact on your course grades.

Researchers who want to access your de-identified data for future research projects will need to submit individual UNSW Ethics Applications for approval before they can access your data.

A full description of the research activities aims, risks associated with these activities and how your privacy and confidentiality will be protected at all times can be found here.

If you consent to have your de-identified data used for academic research into improving student experience, you do not need to do anything. Your consent will be implied, and your data may be used for research in a format that will not individually identify you after the course is completed.

If you do not consent  for this to happen, please email the opt-out form  to seer@unsw.edu.au  to opt-out from having your de-identified data used in this manner. If you complete the opt-out form, the information about you that was collected during this course will not be used in academic research.

7. Course Schedule

Note: for more information on the UNSW academic calendar and key dates including study period, exam, supplementary exam and result release, please visit: https://student.unsw.edu.au/new-calendar-dates
Week Activity Topic Assessment/Other
Week 1: 30 MayLecture only

Topic 1 - Introduction

Topic 2 - Linear regression Model and Financial Data

Understanding Financial Data - Brooks Ch.1 + lecture notes

Basic statistical and mathematical concepts - Brooks Ch.2 + lecture notes

Brooks Ch.3 & 5 + lecture notes

Week 2: 6 JuneLecture

Topic 2 - Linear regression Model and Financial Data (continued)

 

Basic statistical and mathematical concepts - Brooks Ch.2 + lecture notes

Brooks Ch.3 & 5 + lecture notes

 

 

Tutorial

Workshop based on the Week 1 lecture material

 

Group discussion: try to talk to your peers about teaming up!

Python

Introduction to Python Workshop

zoom session TBA

–
Week 3: 13 JuneLecture

Topic 3 - Univariate Time Series Analysis:

  • Estimation
  • Forecasting with ARMA

***NOTE: Monday 14th June is a Public Holiday. NO CLASS***

 

Building ARMA models - Brooks Ch.6 (6.3-6.7, 6.11-6.12) + lecture notes

Estimation of ARMA models - Brooks Ch.6 (6.8) + lecture notes

Additional References: Enders Ch.2; Gujarati Ch. 21 & 22; Hamilton Ch. 3&4; Johnston Ch. 7

Forecasting with ARMA models -Brooks Ch.6 (6.11-6.12) + lecture notes

ML Estimation - Brooks Ch.9.9 + lecture notes

Additional References: Enders Ch.2; Gujarati Ch. 21&22; Hamilton Ch. 3&4; Johnston Ch. 7

Tutorial

Workshop based on the Week 2 lecture material

Python Activity: Linear regression

Application: Portfolio Choice and Testing the Capital Asset Pricing Model

***NOTE: Monday 14th June is a Public Holiday. NO CLASS***

Monday Tutorial class will be recorded and made available. If you wish to join any of the other Tutorial groups this weeks, please do.

–
Online Quiz I

Complete Online Quiz 1: Lecture material from Weeks 1 and 2

See details on Moodle.

Due Friday 05:00 PM

Week 4: 20 JuneLecture

Topic 3 (continued) - Univariate Time Series Analysis: Wrap up

Topic 4 - Non-stationary Time Series I

Deterministic and stochastic non-stationarity - Brooks Ch. 8 (8.1-8.3) + lecture notes

Additional References: Enders Ch 4; Gujarati Ch. 21

Unit Root - Brooks Ch. 8 (8.1-8.3)+ lecture notes

Additional References: Enders Ch 4; Gujarati Ch. 21

Tutorial

Workshop based on the Week 3 lecture material

Python activity: linear regression

Group formation: Finalising groups!

Week 5: 27 JuneLecture

Topic 5 - Long-run relationships: Cointegration and error correction models

 

Python activity: ARMA modeling

Cointegration Analysis - Brooks Ch. 8.4 + lecture notes

Error Correction models - Brooks Ch. 8 (8.5-8.7) + lecture notes

Additional References: Enders Ch 4; Gujarati Ch. 21

Tutorial

Workshop based on the Week 4 lecture material

Python: Cointegration

 

–
Week 6: FLEXIBILITY WEEK: 4 July
–

 

NO LECTURES/TUTORIALS

 

–
Week 7: 11 JulyLecture

Topic 6 - Risk and volatility Analysis: ARCH/GARCH/EGARCH/GJR

ARCH/GARCH - Brooks Ch. 9 (9.2-9.10) + lecture notes

EGARCH - Brooks Ch. 9.13 + lecture notes

Additional References: Enders Ch. 3; Gujarati Ch. 22; Verbee Ch. 8

Tutorial

Workshop based on the Week 5 lecture material

 

–
CASE STUDY PART 1

Case Study PART 1 Due Monday 05:00PM PM. See Moodle for Submission

–
Week 8: 18 JulyLecture

Topic 6 (continued) - Risk and volatility Analysis: ARCH/GARCH/EGARCH/GJR

GJR/GARCH in mean - Brooks Ch. 9 (9.12-9.15) + lecture notes

Stochastic Volatility - Brooks Ch. 9.20 + lecture notes

Tutorial

Workshop based on the Week 7 lecture material

Python activity: Volatility modeling

–
Video Submission

Video Presentation due by Monday 05:00 PM

See Moodle for submission link.

–
Week 9: 25 JulyLecture

Simulation methods

 

Brooks Ch. 13

 

Tutorial

Workshop based on the Week 8 lecture material

Python: simulation methods

–
Online Quiz II

Complete Online Quiz II: Material covered from Week 1 to Week 8 inclusive

Due Friday 05:00 PM

–
Week 10: 1 AugustTutorial only

Workshop based on the Week 9 lecture material

 

 

–
Week 11: 9 AugustCASE STUDY PART 2

CASE STUDY PART II DUE THIS WEEK

Submit by Friday 05:00 PM

–

8. Policies and Support

Information about UNSW Business School program learning outcomes, academic integrity, student responsibilities and student support services. For information regarding special consideration, supplementary exams and viewing final exam scripts, please go to the key policies and support page.

Program Learning Outcomes

The Business School places knowledge and capabilities at the core of its curriculum via seven Program Learning Outcomes (PLOs). These PLOs are systematically embedded and developed across the duration of all coursework programs in the Business School.

PLOs embody the knowledge, skills and capabilities that are taught, practised and assessed within each Business School program. They articulate what you should know and be able to do upon successful completion of your degree.

Upon graduation, you should have a high level of specialised business knowledge and capacity for responsible business thinking, underpinned by ethical professional practice. You should be able to harness, manage and communicate business information effectively and work collaboratively with others. You should be an experienced problem-solver and critical thinker, with a global perspective, cultural competence and the potential for innovative leadership.

All UNSW programs and courses are designed to assess the attainment of program and/or course level learning outcomes, as required by the UNSW Assessment Design Procedure. It is important that you become familiar with the Business School PLOs, as they constitute the framework which informs and shapes the components and assessments of the courses within your program of study.

PLO 1: Business knowledge

Students will make informed and effective selection and application of knowledge in a discipline or profession, in the contexts of local and global business.

PLO 2: Problem solving

Students will define and address business problems, and propose effective evidence-based solutions, through the application of rigorous analysis and critical thinking.

PLO 3: Business communication

Students will harness, manage and communicate business information effectively using multiple forms of communication across different channels.

PLO 4: Teamwork

Students will interact and collaborate effectively with others to achieve a common business purpose or fulfil a common business project, and reflect critically on the process and the outcomes.

PLO 5: Responsible business practice

Students will develop and be committed to responsible business thinking and approaches, which are underpinned by ethical professional practice and sustainability considerations.

PLO 6: Global and cultural competence

Students will be aware of business systems in the wider world and actively committed to recognise and respect the cultural norms, beliefs and values of others, and will apply this knowledge to interact, communicate and work effectively in diverse environments.

PLO 7: Leadership development

Students will develop the capacity to take initiative, encourage forward thinking and bring about innovation, while effectively influencing others to achieve desired results.


These PLOs relate to undergraduate and postgraduate coursework programs.  For PG Research PLOs, including Master of Pre-Doctoral Business Studies, please refer to the UNSW HDR Learning Outcomes

Business School course outlines provide detailed information for students on how the course learning outcomes, learning activities, and assessment/s contribute to the development of Program Learning Outcomes.

UNSW Graduate Capabilities

The Business School PLOs also incorporate UNSW graduate capabilities, a set of generic abilities and skills that all students are expected to achieve by graduation. These capabilities articulate the University’s institutional values, as well as future employer expectations.

UNSW Graduate CapabilitiesBusiness School PLOs
Scholars capable of independent and collaborative enquiry, rigorous in their analysis, critique and reflection, and able to innovate by applying their knowledge and skills to the solution of novel as well as routine problems.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 7: Leadership development

Entrepreneurial leaders capable of initiating and embracing innovation and change, as well as engaging and enabling others to contribute to change
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 6: Global and cultural competence
  • PLO 7: Leadership development

Professionals capable of ethical, self-directed practice and independent lifelong learning
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 5: Responsible business practice

Global citizens who are culturally adept and capable of respecting diversity and acting in a socially just and responsible way.
  • PLO 1: Business knowledge
  • PLO 2: Problem solving
  • PLO 3: Business communication
  • PLO 4: Teamwork
  • PLO 5: Responsible business practice
  • PLO 6: Global and cultural competence

While our programs are designed to provide coverage of all PLOs and graduate capabilities, they also provide you with a great deal of choice and flexibility.  The Business School strongly advises you to choose a range of courses that assist your development against the seven PLOs and four graduate capabilities, and to keep a record of your achievements as part of your portfolio. You can use a portfolio as evidence in employment applications as well as a reference for work or further study. For support with selecting your courses contact the UNSW Business School Student Services team.





Academic Integrity and Plagiarism

Academic Integrity is honest and responsible scholarship. This form of ethical scholarship is highly valued at UNSW. Terms like Academic Integrity, misconduct, referencing, conventions, plagiarism, academic practices, citations and evidence based learning are all considered basic concepts that successful university students understand. Learning how to communicate original ideas, refer sources, work independently, and report results accurately and honestly are skills that you will be able to carry beyond your studies.

The definition of academic misconduct is broad. It covers practices such as cheating, copying and using another person’s work without appropriate acknowledgement. Incidents of academic misconduct may have serious consequences for students.

Plagiarism

UNSW regards plagiarism as a form of academic misconduct. UNSW has very strict rules regarding plagiarism. Plagiarism at UNSW is using the words or ideas of others and passing them off as your own. All Schools in the Business School have a Student Ethics Officer who will investigate incidents of plagiarism and may result in a student’s name being placed on the Plagiarism and Student Misconduct Registers.

Below are examples of plagiarism including self-plagiarism:

Copying: Using the same or very similar words to the original text or idea without acknowledging the source or using quotation marks. This includes copying materials, ideas or concepts from a book, article, report or other written document, presentation, composition, artwork, design, drawing, circuitry, computer program or software, website, internet, other electronic resource, or another person's assignment, without appropriate acknowledgement of authorship.

Inappropriate Paraphrasing: Changing a few words and phrases while mostly retaining the original structure and/or progression of ideas of the original, and information without acknowledgement. This also applies in presentations where someone paraphrases another’s ideas or words without credit and to piecing together quotes and paraphrases into a new whole, without appropriate referencing.

Collusion: Presenting work as independent work when it has been produced in whole or part in collusion with other people. Collusion includes:

  • Students providing their work to another student before the due date, or for the purpose of them plagiarising at any time
  • Paying another person to perform an academic task and passing it off as your own
  • Stealing or acquiring another person’s academic work and copying it
  • Offering to complete another person’s work or seeking payment for completing academic work

Collusion should not be confused with academic collaboration (i.e., shared contribution towards a group task).

Inappropriate Citation: Citing sources which have not been read, without acknowledging the 'secondary' source from which knowledge of them has been obtained.

Self-Plagiarism: ‘Self-plagiarism’ occurs where an author republishes their own previously written work and presents it as new findings without referencing the earlier work, either in its entirety or partially. Self-plagiarism is also referred to as 'recycling', 'duplication', or 'multiple submissions of research findings' without disclosure. In the student context, self-plagiarism includes re-using parts of, or all of, a body of work that has already been submitted for assessment without proper citation.

To see if you understand plagiarism, do this short quiz: https://student.unsw.edu.au/plagiarism-quiz

Cheating

The University also regards cheating as a form of academic misconduct. Cheating is knowingly submitting the work of others as their own and includes contract cheating (work produced by an external agent or third party that is submitted under the pretences of being a student’s original piece of work). Cheating is not acceptable at UNSW.

If you need to revise or clarify any terms associated with academic integrity you should explore the 'Working with Academic Integrity' self-paced lessons available at: https://student.unsw.edu.au/aim.

For UNSW policies, penalties, and information to help you avoid plagiarism see: https://student.unsw.edu.au/plagiarism as well as the guidelines in the online ELISE tutorials for all new UNSW students: http://subjectguides.library.unsw.edu.au/elise. For information on student conduct see: https://student.unsw.edu.au/conduct.

For information on how to acknowledge your sources and reference correctly, see: https://student.unsw.edu.au/referencing. If you are unsure what referencing style to use in this course, you should ask the lecturer in charge.



Student Responsibilities and Conduct

​Students are expected to be familiar with and adhere to university policies in relation to class attendance and general conduct and behaviour, including maintaining a safe, respectful environment; and to understand their obligations in relation to workload, assessment and keeping informed.

Information and policies on these topics can be found on the 'Managing your Program' website.

Workload

It is expected that you will spend at least ten to twelve hours per week studying for a course except for Summer Term courses which have a minimum weekly workload of twenty to twenty four hours. This time should be made up of reading, research, working on exercises and problems, online activities and attending classes. In periods where you need to complete assignments or prepare for examinations, the workload may be greater. Over-commitment has been a cause of failure for many students. You should take the required workload into account when planning how to balance study with employment and other activities.

We strongly encourage you to connect with your Moodle course websites in the first week of semester. Local and international research indicates that students who engage early and often with their course website are more likely to pass their course.

View more information on expected workload

Attendance and Engagement

Your regular attendance and active engagement in all scheduled classes and online learning activities is expected in this course. Failure to attend / engage in assessment tasks that are integrated into learning activities (e.g. class discussion, presentations) will be reflected in the marks for these assessable activities. The Business School may refuse final assessment to those students who attend less than 80% of scheduled classes where attendance and participation is required as part of the learning process (e.g. tutorials, flipped classroom sessions, seminars, labs, etc.). If you are not able to regularly attend classes, you should consult the relevant Course Authority.

View more information on attendance

General Conduct and Behaviour

You are expected to conduct yourself with consideration and respect for the needs of your fellow students and teaching staff. Conduct which unduly disrupts or interferes with a class, such as ringing or talking on mobile phones, is not acceptable and students may be asked to leave the class.

View more information on student conduct

Health and Safety

UNSW Policy requires each person to work safely and responsibly, in order to avoid personal injury and to protect the safety of others.

View more information on Health and Safety

Keeping Informed

You should take note of all announcements made in lectures, tutorials or on the course web site. From time to time, the University will send important announcements to your university e-mail address without providing you with a paper copy. You will be deemed to have received this information. It is also your responsibility to keep the University informed of all changes to your contact details.




Student Support and Resources

The University and the Business School provide a wide range of support services and resources for students, including:

Business School Learning Support Tools
Business School provides support a wide range of free resources and services to help students in-class and out-of-class, as well as online. These include:

  • Academic Communication Essentials – A range of academic communication workshops, modules and resources to assist you in developing your academic communication skills.
  • Learning consultations – Meet learning consultants who have expertise in business studies, literacy, numeracy and statistics, writing, referencing, and researching at university level.
  • PASS classes – Study sessions facilitated by students who have previously and successfully completed the course.
  • Educational Resource Access Scheme – To support the inclusion and success of students from equity groups enrolled at UNSW Sydney in first year undergraduate Business programs.

The Nucleus - Business School Student Services team
The Nucleus Student Services team provides advice and direction on all aspects of enrolment and graduation. Level 2, Main Library, Kensington 02 8936 7005 / https://nucleus.unsw.edu.au/en/contact-us

Business School Equity, Diversity and Inclusion
The Business School Equity, Diversity and Inclusion Committee strives to ensure that every student is empowered to have equal access to education. The Business School provides a vibrant, safe, and equitable environment for education, research, and engagement that embraces diversity and treats all people with dignity and respect. BUSEDI@unsw.edu.au

UNSW Academic Skills
Resources and support – including workshops, individual consultations and a range of online resources – to help you develop and refine your academic skills. See their website for details.
academicskills@unsw.edu.au

Student Support Advisors
Student Support Advisors work with all students to promote the development of skills needed to succeed at university, whilst also providing personal support throughout the process.
John Goodsell Building, Ground Floor.
advisors@unsw.edu.au
02 9385 4734

International Student Support
The International Student Experience Unit (ISEU) is the first point of contact for international students. ISEU staff are always here to help with personalised advice and information about all aspects of university life and life in Australia.
Advisors can support you with your student visa, health and wellbeing, making friends, accommodation and academic performance.
International.student@unsw.edu.au
02 9385 4734

Equitable Learning Services
Equitable Learning Services (formerly Disability Support Services) is a free and confidential service that provides practical support to ensure that your health condition doesn't adversely affect your studies. Register with the service to receive educational adjustments.
Ground Floor, John Goodsell Building.
els@unsw.edu.au
02 9385 4734

UNSW Counselling and Psychological Services
Provides support and services if you need help with your personal life, getting your academic life back on track or just want to know how to stay safe, including free, confidential counselling.
Level 2, East Wing, Quadrangle Building.
counselling@unsw.edu.au
02 9385 5418

Library services and facilities for students
The UNSW Library offers a range of collections, services and facilities both on-campus and online.
Main Library, F21.
02 9065 9444

Moodle eLearning Support
Moodle is the University’s learning management system. You should ensure that you log into Moodle regularly.
externalteltsupport@unsw.edu.au
02 9385 3331

UNSW IT
UNSW IT provides support and services for students such as password access, email services, wireless services and technical support.
UNSW Library Annexe (Ground floor).
02 9385 1333



Support for Studying Online

The Business School and UNSW provide a wide range of tools, support and advice to help students achieve their online learning goals. 

The UNSW Guide to Online Study page provides guidance for students on how to make the most of online study.

We recognise that completing quizzes and exams online can be challenging for a number of reasons, including the possibility of technical glitches or lack of reliable internet. We recommend you review the Online Exam Preparation Checklist of things to prepare when sitting an online exam.

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