Outline

Overview

This unit explores the interaction between psychology and behaviour in financial decision-making. The unit will examine how people evaluate financial risks in practice, and how behavioural biases affect financial decisions. It will also focus on the importance of trust, cooperation, and financial wellbeing in the decision-making process. The unit will then examine group behaviour in financial markets, its effects on asset prices, bubbles and crashes, and implications for financial product design. In this unit, you will learn practical skills to help you succeed in industry, including how to navigate stakeholder engagement, design optimal choice architecture, and apply behaviourally-informed strategies for corporate financial decisions, financial planning and investment advice.

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

Assessment

The census date for this unit availability is 31 August 2026

Details
Criteria

Type	Description	Weight	Due	Length	Use of AI
Written exam	Case study Open-book case analysis.	35%	Formal exam period	2 hours	AI prohibited
Outcomes assessed:
Contribution	In-class experiments and tasks. Participation on financial experiments, simulations and surveys in-class.	15%	Multiple weeks	10-30 minutes	AI allowed
Outcomes assessed:
Written test	In-class exam Open-book exam.	25%	Week 06 Due date: 12 Sep 2026 at 15:50	80 minutes
Outcomes assessed:
Interactive oral	Interactive Oral Discussion Individual interactive oral discussion.	25%	Week 11	15 minutes	AI prohibited
Outcomes assessed:

Assessment summary

In-class exam (week 6): the exam will cover part of the topics discussed in class in weeks 1-5, comprising writing sections, quantitative exercises and/or analysis of scenarios. The exam is open-book, will be held in school and delivered in the bring-your-own-device (BYOD) mode.
Interactive oral discussion (week 11): 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 - 10 calendar days before the interactive oral date. 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.
Case analysis (exams period): the case analysis will apply skills and content learned in the unit to address a proposed problem. The case analysis is open-book, will be held in school and delivered in the bring-your-own device (BYOD) mode.
In-class experiments and tasks (spread throughout semester): students will participate on several financial experiments, surveys and other activities to be offered either during lectures or tutorials. Pass/Fail mark will be given according to meaningful engagement and participation in each of the various experimental tasks. No previous preparation is required.

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.

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	Foundations of individual financial decision-making.	Lecture (2 hr)
Week 02	Topics of week 1, decision-making experiment.	Tutorial (1 hr)
Week 02	Analysis of financial decision-making, experiments in finance.	Lecture (2 hr)
Week 03	Topics of weeks 1 and 2.	Tutorial (1 hr)
Week 03	Risk-aversion, loss-aversion, ambiguity.	Lecture (2 hr)
Week 04	Topics of week 3.	Tutorial (1 hr)
Week 04	Heuristics, rules-of-thumb, and behavioral biases in financial decisions.	Lecture (2 hr)
Week 05	Topics of week 4.	Tutorial (1 hr)
Week 05	Guest Lecture.	Lecture (2 hr)
Week 06	Revision and Q&A for exam.	Tutorial (1 hr)
Week 07	Market experiment.	Tutorial (1 hr)
Week 07	Long-term personal financial planning.	Lecture (2 hr)
Week 08	Topics of week 7.	Tutorial (1 hr)
Week 08	Real humans making financial decisions under imperfect conditions and limited information.	Lecture (2 hr)
Week 09	Financial advice: professionals, robots, finfluencers and AI.	Lecture (2 hr)
Week 10	Topics of weeks 8 and 9.	Tutorial (1 hr)
Week 10	Financial markets and non-rational agents.	Lecture (4 hr)
Week 12	Topics of week 10.	Tutorial (1 hr)
Week 12	Asset pricing bubbles and crashes (1).	Lecture (2 hr)
Week 13	Topics of week 12.	Tutorial (1 hr)
Week 13	Asset pricing bubbles and crashes (2).	Lecture (2 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 and tasks: in-class experiments and tasks count towards participation marks (15%). It is not possible to complete many of the experiments and tasks outside the physical classrooms, nor to complete them outside scheduled sessions, since some of their results depend on the simultaneous decisions of other students. Please, refer
to Canvas for a detailed explanation of the in-class tasks assessment and marking policy.

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 or through 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.

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 has undergone many changes from last delivery. There was a trade-off of breadth for depth, with removal of certain corporate finance topics and a complete reorganization of the order of topics covered. Both examinations are now delivered through BYOD and are open-book. Assessments had their weights changed. Less in-time class will be used to complete individual experiments and surveys. There are more reading materials uploaded directly on Canvas at the beginning of the semester to help student with self-study and preparation.

Additional information

Disclaimer

Important: the University of Sydney regularly reviews units of study and reserves the right to change the units of study available annually. To stay up to date on available study options, including unit of study details and availability, refer to the relevant handbook.

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, 2026 [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