Outline

Overview

Behavioural Finance explores financial market 'anomalies' - factors that are not traditionally explained by efficient markets theory - such as why stock prices exhibit momentum and reversals, why large swings in stock prices occur over short time periods, and how individual investors differ from institutional investors.

Unit details and rules

Academic unit	Finance
Credit points	6
Prerequisites ?	FINC2012
Corequisites ?	None
Prohibitions ?	None
Assumed knowledge ?	None
Available to study abroad and exchange students	Yes

Teaching staff

Coordinator	Andre Lot, andre.lot@sydney.edu.au
Lecturer(s)	Danielle Kent, danielle.kent@sydney.edu.au

Assessment

The census date for this unit availability is 1 September 2025

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam ?	Case study Computer-based (BYOD - bring your own device) open-book case analysis.	30%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Contribution	In-class experiments Participation on financial experiments, simulations and surveys in-class.	15%	Multiple weeks	10-40 minutes	AI allowed
Outcomes assessed:
In-person written or creative task	In-class exam Exam covering part of content taught.	25%	Week 05 Due date: 01 Sep 2025 at 11:30	1 hour	AI prohibited
Outcomes assessed:
Interactive oral	Interactive Oral Discussion Individual interactive oral discussion.	30%	Week 10	15 minutes	AI prohibited
Outcomes assessed:

Assessment summary

In-class exam (week 5): the exam will cover part of the topics discussed in class, as announced on Canvas, comprimising writing sections, quantitative exercises and/or analysis of scenarios and cases. The exam is closed book.
Interactive oral discussion (week 10): the interactive oral discussion will comprise a scenario-based discussion. Students will receive their prepatory materials - which form the basis of the discussion in their sessions - after the last tutorial on week 8. Students must self-book their individual 15-min Interactive Oral Discussion Sessions on the Canvas Scheduler. The Canvas Scheduler will open on August 5, and the sessions are available in a first-come, first-served basis.

In-term case analysis (exams period): the digital case analysis will be held in the school, invigilated, in the bring-your-own-device (BYOD) mode. Students should bring their own laptops, which will then be fit with specific lockdown software for the examination. Detailed information and instructions will follow on Canvas.

In-class experiments (spread throughout semester): students will participate on several financial experiments to be offered either during lectures or on tutorials, in addition to short surveys and other activities. Pass/Fail mark will be given according to meaningful engagement and participation in each of the various experimental tasks. No previous preparation is required, and no marks will be given based on outcomes.

Detailed information for each assessment can be found on Canvas.

Assessment criteria

The University awards common result grades, set out in the Coursework Policy 2014 (Schedule 1).

As a general guide, a high distinction indicates work of an exceptional standard, a distinction a very high standard, a credit a good standard, and a pass an acceptable standard.

Result name	Mark range	Description
High distinction	85 - 100	Awarded when you demonstrate the learning outcomes for the unit at an exceptional standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Distinction	75 - 84	Awarded when you demonstrate the learning outcomes for the unit at a very high standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Credit	65 - 74	Awarded when you demonstrate the learning outcomes for the unit at a good standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Pass	50 - 64	Awarded when you demonstrate the learning outcomes for the unit at an acceptable standard, as defined by grade descriptors or exemplars outlined by your faculty or school.
Fail	0 - 49	When you don’t meet the learning outcomes of the unit to a satisfactory standard.

For more information see sydney.edu.au/students/guide-to-grades.

For more information see guide to grades.

Use of generative artificial intelligence (AI)

You can use generative AI tools for open assessments. Restrictions on AI use apply to secure, supervised assessments used to confirm if students have met specific learning outcomes.

Refer to the assessment table above to see if AI is allowed, for assessments in this unit and check Canvas for full instructions on assessment tasks and AI use.

If you use AI, you must always acknowledge it. Misusing AI may lead to a breach of the Academic Integrity Policy.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Late submission

In accordance with University policy, these penalties apply when written work is submitted after 11:59pm on the due date:

Deduction of 5% of the maximum mark for each calendar day after the due date.
After ten calendar days late, a mark of zero will be awarded.

This unit has an exception to the standard University policy or supplementary information has been provided by the unit coordinator. This information is displayed below:

In accordance with University policy, these penalties apply when the written work is submitted after 11:59pm on the due date: (1) Deduction of 5% of the maximum mark for each calendar day after the due date. (2) After ten calendar days late, a mark of zero will be awarded.

Academic integrity

The University expects students to act ethically and honestly and will treat all allegations of academic integrity breaches seriously.

Our website provides information on academic integrity and the resources available to all students. This includes advice on how to avoid common breaches of academic integrity. Ensure that you have completed the Academic Honesty Education Module (AHEM) which is mandatory for all commencing coursework students

Penalties for serious breaches can significantly impact your studies and your career after graduation. It is important that you speak with your unit coordinator if you need help with completing assessments.

Visit the Current Students website for more information on AI in assessments, including details on how to acknowledge its use.

Learning support

Simple extensions

If you encounter a problem submitting your work on time, you may be able to apply for an extension of five calendar days through a simple extension.  The application process will be different depending on the type of assessment and extensions cannot be granted for some assessment types like exams.

Special consideration

If exceptional circumstances mean you can’t complete an assessment, you need consideration for a longer period of time, or if you have essential commitments which impact your performance in an assessment, you may be eligible for special consideration or special arrangements.

Special consideration applications will not be affected by a simple extension application.

Using AI responsibly

Co-created with students, AI in Education includes lots of helpful examples of how students use generative AI tools to support their learning. It explains how generative AI works, the different tools available and how to use them responsibly and productively.

Support for students

The Support for Students Policy reflects the University’s commitment to supporting students in their academic journey and making the University safe for students. It is important that you read and understand this policy so that you are familiar with the range of support services available to you and understand how to engage with them.

The University uses email as its primary source of communication with students who need support under the Support for Students Policy. Make sure you check your University email regularly and respond to any communications received from the University.

Learning resources and detailed information about weekly assessment and learning activities can be accessed via Canvas. It is essential that you visit your unit of study Canvas site to ensure you are up to date with all of your tasks.

If you are having difficulties completing your studies, or are feeling unsure about your progress, we are here to help. You can access the support services offered by the University at any time:

Support and Services (including health and wellbeing services, financial support and learning support)
Course planning and administration
Meet with an Academic Adviser

Weekly schedule

WK	Topic	Learning activity
Week 01	Trust and Cooperation (I)	Lecture (2 hr)
Week 02	Trust and Cooperation (II)	Lecture and tutorial (3 hr)
Week 03	Stakeholder Engagement	Lecture and tutorial (3 hr)
Week 04	Guest Lecture	Lecture (2 hr)
Week 04	Revision and Exam preparation	Tutorial (1 hr)
Week 05	Topics discussed on guest lecture and previous readings	Tutorial (1 hr)
Week 06	The Science of Wellbeing for Finance	Lecture and tutorial (3 hr)
Week 07	Classical Paradigm of Individual Financial Decision-Making	Lecture (2 hr)
Week 07	Decision-making experiment	Computer laboratory (1 hr)
Week 08	Guest Lecture - Learning from Experiments	Lecture and tutorial (3 hr)
Week 09	Behavioural Biases and Non-Standard Preferences	Lecture and tutorial (3 hr)
Week 11	Asset Price Bubbles and Crashes (I)	Lecture (2 hr)
Week 11	Market experiment	Computer laboratory (1 hr)
Week 12	Asset Price Bubbles and Crashes (II)	Lecture and tutorial (3 hr)
Week 13	Financial Product Design and Advice for Humans	Lecture and tutorial (3 hr)

Attendance and class requirements

Lecture recordings: Students should ensure they attend and participate in-person in all classes to the maximum possible extent. Lectures will be recorded and made available asynchronously on Canvas for student use. Please note the Business School records lectures passively, and cannot guarantee that the system will operate or that every lecture will be recorded. No replacement record will be provided by the lectures if technical issues prevent a given lecture from being recorded. Tutorials will not be recorded.

In-class experiments: in-class experiments and surveys count towards participation marks (15%) and can mostly only be completed in class during lectures and/or tutorials. In some weeks, they will be done on computer labs where teaching or tutorials will take place. In other weeks, students will use their internet-connected laptops or tablets to complete the experiments. It is not possible to complete the experiments outside the physical classrooms, nor to complete them outside scheduled sessions, since some of their results depend on the simultaneous decisions of other students.

Study commitment

Typically, there is a minimum expectation of 1.5-2 hours of student effort per week per credit point for units of study offered over a full semester. For a 6 credit point unit, this equates to roughly 120-150 hours of student effort in total.

Required readings

All readings for this unit can be accessed on the Library eReserve link available on Canvas.

Ackert, L and R. Deaves (2010) Behavioral Finance, Cengage Learning, Singapore.
Bachmman, K, T. Hens and E. Di Giorgi (2018). Behavioral Finance for Private Banking: From the Art of Advice to the Science of Advice. 2nd ed. Wiley.
lecture notes and readers (posted on Canvas)

Learning outcomes

Learning outcomes are what students know, understand and are able to do on completion of a unit of study. They are aligned with the University's graduate qualities and are assessed as part of the curriculum.

Outcomes
Graduate qualities

At the completion of this unit, you should be able to:

LO1. Apply the basic framework of expected utility as the classical normative approach to model financial decision-making.
LO2. Consider the role of bounded rationality, limited attention and other phenomena that restricts, hinder or limit the ability of individuals to make efficient financial decisions, and how they adopt different heuristics and behaviours to make actual decisions or follow professional financial advice.
LO3. Assess how rules-of-thumb or perceived wisdom of simple financial advice for lay persons might interact, reinforce or stave off effects that arise from individual biased beliefs or inconsistent preferences.
LO4. Determine how the combination of institutional design, external determinants and individual financial decision-making behaviour combine and interact to produce market-wide phenomena such as price bubbles, crashes and excessive trading volume.
LO5. Integrate the role of social interactions and social preferences with financial decision-making in different corporate domains.

Graduate qualities

The graduate qualities are the qualities and skills that all University of Sydney graduates must demonstrate on successful completion of an award course. As a future Sydney graduate, the set of qualities have been designed to equip you for the contemporary world.

GQ1	Depth of disciplinary expertise Deep disciplinary expertise is the ability to integrate and rigorously apply knowledge, understanding and skills of a recognised discipline defined by scholarly activity, as well as familiarity with evolving practice of the discipline.
GQ2	Critical thinking and problem solving Critical thinking and problem solving are the questioning of ideas, evidence and assumptions in order to propose and evaluate hypotheses or alternative arguments before formulating a conclusion or a solution to an identified problem.
GQ3	Oral and written communication Effective communication, in both oral and written form, is the clear exchange of meaning in a manner that is appropriate to audience and context.
GQ4	Information and digital literacy Information and digital literacy is the ability to locate, interpret, evaluate, manage, adapt, integrate, create and convey information using appropriate resources, tools and strategies.
GQ5	Inventiveness Generating novel ideas and solutions.
GQ6	Cultural competence Cultural Competence is the ability to actively, ethically, respectfully, and successfully engage across and between cultures. In the Australian context, this includes and celebrates Aboriginal and Torres Strait Islander cultures, knowledge systems, and a mature understanding of contemporary issues.
GQ7	Interdisciplinary effectiveness Interdisciplinary effectiveness is the integration and synthesis of multiple viewpoints and practices, working effectively across disciplinary boundaries.
GQ8	Integrated professional, ethical, and personal identity An integrated professional, ethical and personal identity is understanding the interaction between one’s personal and professional selves in an ethical context.
GQ9	Influence Engaging others in a process, idea or vision.

Outcome map

Learning outcomes	Graduate qualities
Learning outcomes

Responding to student feedback

This section outlines changes made to this unit following staff and student reviews.

The unit content was thoroughly restructured, with changes in topics and their weekly schedule. There is a greater emphasis on the use of financial experiments as a learning tool. The assessment schedule was adjusted so that assessments are more evenly spread throughout the semester. The Interactive Oral Discussion no longer has a group project component.

Additional information

Disclaimer

The University reserves the right to amend units of study or no longer offer certain units, including where there are low enrolment numbers.

To help you understand common terms that we use at the University, we offer an online glossary.

Current students

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links

FINC3023: Behavioural Finance

Semester 2, 2025 [Normal day] - Camperdown/Darlington, Sydney

Overview

Overview

Unit details and rules

Teaching staff

Assessment

Assessment

Assessment summary

Assessment criteria

Use of generative artificial intelligence (AI)

Late submission

Academic integrity

Learning support

Learning support

Simple extensions

Special consideration

Using AI responsibly

Support for students

Weekly schedule

Weekly schedule

Attendance and class requirements

Study commitment

Required readings

Learning outcomes

Learning outcomes

Graduate qualities

Outcome map

Responding to student feedback

Responding to student feedback

Additional information

Additional information

Disclaimer

On this page

Useful links