代写BMA0092 Quantitative Financial Analysis 2023/24代写留学生R程序

ASSESSMENT:  Assessed coursework – BMA0092 - QFA

Module Code:	BMA0092
Module Title:	Quantitative Financial Analysis
Assessment Type (Initial/ Resit) ML to delete as appropriate	Individual Assignment (Report)
Academic Year	2023/24

Assessment Task

Pension fund investment decisions

This assignment consists of a small empirical research project, where you will apply the tools learned in Quantitative Financial Analysis to answer three related research questions.

Your report must answer ALL of the following research questions:

1. To what extent can companies’ financial characteristics determine pension asset allocations?

a. Question (1) must be answered by independently selecting 5 or more financial ratios. You are expected to justify your choice of financial ratios.

2. Can these models based on accounting and finance data be improved by including corporate governance information?

3. Given your analyses, what evidence can you provide as to the main cause(s) for companies to change their pension asset allocations?

You are required to answer all the research questions using empirical analysis. The dataset to be used in the analysis is provided via Brightspace. The companies in the dataset are the focus of your analyses.

Structure:

Using the tools learned in the module, you are required to conduct independent research to investigate and answer the research questions. Your final written document should adhere to the following structure:

• Abstract

• Brief Introduction with short Literature Review

• Methodology

• Data

• Ethics, Sustainability & Responsibility

• Results

• Conclusion

• References

• Appendices

The report has a similar structure to a research paper. The recommended reading provides a guide of what should be included in each section of the report.

Recommended reading:

As this is a research project, you will have to make yourself familiar with the relevant literature in order to decide your empirical strategy and answer the research questions. These are a few papers that you can read as a starting point. However, you will benefit from further readings on the subject.

– Amir, E., & Benartzi, S. (1999). Accounting recognition and the determinants of pension asset allocation. Journal of Accounting, Auditing & Finance, 14(3), 321- 343.

– Amir, E., Guan, Y., & Oswald, D. (2010). The effect of pension accounting on corporate pension asset allocation. Review of Accounting Studies, 15(2), 345-366.

– An, H., Lee, Y. W., & Zhang, T. (2014). Do corporations manage earnings to meet/exceed analyst forecasts? Evidence from pension plan assumption changes. Review of Accounting Studies, 19(2), 698-735.

– Anantharaman, D., & Lee, Y. G. (2014). Managerial risk taking incentives and corporate pension policy. Journal of Financial Economics, 111(2), 328-351.

– Chen, G., Firth, M., Gao, D. N., & Rui, O. M. (2006). Ownership structure, corporate governance, and fraud: Evidence from China. Journal of Corporate Finance, 12(3), 424-448.

– Phan, H. V., & Hegde, S. P. (2013). Corporate governance and risk taking in pension plans: Evidence from defined benefit asset allocations. Journal of Financial and Quantitative Analysis, 48(3), 919-946.

– Tang, D. Y., & Zhang, Y. (2018). Do shareholders benefit from green bonds?.

Journal of Corporate Finance.

Level of AI-Use permitted for this Assessment

X Level 1- Not Permitted. The use of AI tools is not permitted in any part of this assessment.

Level 2 – Some use Permitted. Some use of AI tools is permitted in the research/early stages of this assignment but you must ensure that the work you submit is your own. If you use AI tools, you should acknowledge or reference this in your work.  Use the Text reference builder to learn how to reference AI generated ideas. The sorts of questions to consider when using AI are:

• Is it accurate?

• Are the references genuine?

• Has it reproduced bias?

Level 3 – Integrated. The use of AI tools is integrated in this assessment. Further guidance is included in this assessment brief.

Duration: NA

Word Count: 2500 words (do not exceed this word count). The word limit does not include tables, figures, footnotes and references.

Task specific guidance:

A minimum requirement to pass this module is that students should be able to identify and justify their choice of key financial characteristics of companies (at least 5 of your choice). Students should demonstrate the ability to include descriptive analysis and regression analysis in order to answer all questions.

Students should be able to comment on the results of their analyses and refer to the prior literature.

As a minimum requirement, the empirical analysis should include an initial multivariate model This initial multivariate model must be compared in terms of accuracy to a further model which incorporates corporate governance measures in order to answer Question 2.

Please submit your report in a single document. Create an appendix section at the end which contains all the Stata code needed to reproduce your results. You must not include the code that failed to run, only include a cleaned-up version.

Your code must work when the module leader runs it in the relevant software). Failure to include the Stata code means that the coursework will be marked incomplete.

Plagiarism in any form. is not tolerated by the University of Huddersfield Business School. Marking criteria are specified in appendix 1.

General study guidance: Cite all information used in your work which is clearly from a source. Try to ensure that all sources in your reference list are seen as citations in your work, and all names cited in the work appear in your reference list.

• Reference and cite your work in accordance with the APA 7th system – the University’s chosen referencing style. For specific advice, you can talk to your Business librarians or go to the library help desk, or you can access library guidance via the following link:

o APA 7th referencing: https://library.hud.ac.uk/pages/apareferencing/

• The University has regulations relating to academic misconduct, including plagiarism. The Learning Innovation and Development Centre can advise and help you with how to avoid ‘poor scholarship’ and potential academic misconduct. You can contact them at [email protected].

• If you have any concerns about your writing, referencing, research or presentation skills, you are welcome to consult the Learning Innovation Development Centre team [email protected]. It is possible to arrange 1:1 consultation with a LIDC tutor once you have planned or writtena section of your work, so that they can advise you on areas to develop.

• Do not exceed the word / time / other limit.

Assessment criteria

• The Assessment Criteria are shown the end of this document. Your tutor will discuss how your work will be assessed/marked and will explain how the assessment criteria apply to this piece of work. These criteria have been designed for your level of study.

• These criteria will be used to mark your work and will be used to support the electronic feedback you receive on your marked assignment. Before submission, check that you have tried to meet the requirements of the higher-grade bands to the best of your ability. Please note that the marking process involves academic judgement and interpretation within the marking criteria.

• The Learning Innovation Development Centre can help you to understand and use the assessment criteria. To book an appointment, either visit them on The Street in the Charles Sikes Building or email them on [email protected]

Learning Outcomes

This section is for information only.

The assessment task outlined above has been designed to address specific validated learning outcomes for this module. It is useful to keep in mind that these are the things you need to show in this piece of work.

On completion of this module, students will need to demonstrate:

Learning Outcomes

1. Demonstrate a critical understanding of econometric techniques that can be applied to cross section, time series and panel data.

2. Critically apply econometric techniques to analyse financial data.

Ability Outcomes

1. Justify the selection of quantitative techniques in financial research.

2. Communicate quantitative data appropriately in writing using academicconventions.

3. Able to utilise the relevant quantitative analysis software.

Please note these learning outcomes are not additional questions.

Submission information

Word Limit:

2500 words (do not exceed this word count). The word limit does not include tables, figures, footnotes and references.

Submission Date:

dd/mm/yyyy Friday of Week 4 of course.

Feedback Date:

dd/mm/yyyy (3 weeks from date of submission)

Submission Time:

15.00

Submission Method:

Electronically via module site in Brightspace. Paper/hard copy submissions are not required. For technical support, please contact: [email protected]