SUMMATIVE ASSESSMENT

Module title: Behavioural Finance

Module code: FINN3081

Assessment type: Assignment

Word limit: 3000 words maximum

Assignment Title

Critically evaluate the behavioural explanations for major financial market anomalies, including excessive trading, bubbles, and herding, with reference to both empirical evidence and experimental techniques. In your answer:

· Explain how cognitive biases, and psychological factors contribute to these anomalies.

· Assess how limits to arbitrage interact with investor behaviour in these contexts.

· Compare and critique at least two empirical or experimental papers, focusing on the robustness of their methodology and findings.

· Apply behavioural concepts to a recent real-world case (for example meme stocks, crypto bubbles, or social media-driven sentiment), using evidence from professional data sources (for example Bloomberg, WRDS, Refinitiv, Orbis).

Further guidance on how to conduct the relevant research and analysis will be discussed in lectures and workshops. Please also refer to the Assessment / Summative Assessment folder on the module’s Blackboard site for additional guidance.

Learning outcomes

This assessment addresses the following module learning outcomes:

a. Demonstrate knowledge and understanding of questions in empirical finance linked to appropriate methodologies for their analysis.

b. Explore issues in finance by using professional data sources.

c. Provide students with the opportunity to develop the ability to critically evaluate academic literature relating to empirical and computational finance.

Graduate attributes

This assessment helps develop the following graduate attributes:

Our graduates

· are open minded, embrace diversity and listen to different viewpoints

· are intellectually rigorous and courageous

· are curious and creative

· learn and grow from their experiences

· are resourceful and could apply their knowledge and skills in a rapidly changing world

Academic Integrity

Please use the “Harvard” or the “APA” method of referencing. The Guide “Cite Them Right” provides detailed guidance on the relevant referencing conventions, both for in-text citations and the compilation of the list of references/bibliography.

Citations must clearly show whether you use the authors’ actual words (direct quotes) or whether you summarise or paraphrase an argument they make (indirect quotes). When you read about an author’s arguments or findings, but you have not read the original source, you need to clearly identify this as a secondary quotation and you are not permitted to list the source you have read about (but not read yourself) in the bibliography.

You must comply with the guidance on the use and referencing of generative AI explained in the Departmental Generative AI Policy here. As generative AI provides a different reply to the same prompt, students must provide an appendix of the prompts and the responses when generative AI is used. For each prompt or series of related prompts, students must establish a separate appendix. The AI response should be provided in form. of a transcript. of the text or screen shots of graphs or pictures.

If you are conducting data analysis or empirical modelling, you must correctly identify the use of the statistical packages (such as Stata, Excel or MPlus) and databases (such as WRDS, Bloomberg) in the description of the analysis you conduct and the data you use.

Students suspected of academic misconduct will be dealt with according to Business School and University guidelines.

Marking guidelines

Performance in the summative assessment for this module is judged against the following criteria:

· Relevance to question(s)

· Organisation, structure and presentation

· Depth of understanding

· Analysis and discussion

· Use of sources and referencing

· Overall conclusions

Your assignment should be well organised and structured, using headings and sub-headings as appropriate to indicate topics discussed.

You should carefully consider the differences in the quality and validity of different sources of information, including in the context of the use of generative AI.

The assignment will be assessed based on the grade descriptors.

DUBS Grade Descriptors for Undergraduate Programmes

Word limit

Written assignments must not exceed the word count indicated above.

The word count should:

- Include all the text, including title, introduction, in-text citations, quotations, footnotes and any other item not specifically excluded below.

- Exclude diagrams, tables (including tables/lists of contents and figures), equations, executive summary/abstract, acknowledgements, declaration, bibliography/list of references and appendices. However, it is not appropriate to use diagrams or tables merely as a way of circumventing the word limit. If a student uses a table or figure as a means of presenting his/her own words, then this is included in the word count.

Students must report an accurate word count on the first page of their assignment.

Examiners will stop reading once the word limit has been reached, and work beyond this point will not be assessed. Checks of word counts will be carried out on submitted work, including any assignments or dissertations/business projects that appear to be over-length. Checks may take place manually and/or with the aid of the word count provided via an electronic submission. Where a student has intentionally misrepresented their word count, the School may treat this as an offence under Section IV of the General Regulations of the University. Extreme cases may be viewed as dishonest practice under Section IV, 5 (a) - (x) of the General Regulations.

Format

Assignments should be typed, using 1.5 spacing and an easy-to-read 12-point font.

Appendices

In addition to appendices for the referencing of AI prompts, there might be other instances, where it may be appropriate to present material which does not properly belong in the main body of the assessment but which some students wish to provide for the sake of completeness in an appendix.

Any such appendices will not have a role in the assessment – the examiners are under no obligation to read appendices, and they do not form. part of the word count. Please note that your assignment and any appendices must all form. part of the same electronic document, since only one file can be submitted using Blackboard.

Submission instructions

Your completed assignment must be uploaded to Blackboard no later than 12noon (UK time) on 16 January 2026.

The submission title for this assessment should follow this format FINN1234_Z0****** (using the FINN-Code of your module and where Z0****** is your Anonymous Candidate Code).

It is your responsibility to back up your work.  You should back up your work on more than one device.

A penalty will be applied for work uploaded after 12noon as detailed in the Late submission policy. You must leave sufficient time to fully complete the upload process before the deadline and check that you have received a receipt. At peak periods, it can take up to 30 minutes for a receipt to be generated.

Feedback and marking

Students will receive individual written feedback on the assignment and provisional marks by 13 February 2026 via the Blackboard submission site.

Students’ summative marks are subject to rigorous quality assurance practices. This includes moderation of marking for each module, in which a second marker reviews a sample of assessments. The External Examiner for the programme then reviews a selection of assessments across all modules honours level. Finally, all marks are formally approved by the Board of Examiners. The marks remain provisional and potentially subject to change, until the exam board has confirmed them.