EFIM20040R Financial Data 2023
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EFIM20040R
Financial Data
August 2023
Overview
• Your summative coursework represents 100% of the final mark for the unit.
• The coursework is in the form of a report and its supplementary files, e.g., R code and data files.
• Penalties will apply if the coursework is submitted late.
• The coursework is an individual piece of work – you should work on this yourself and not as a group. You will be required to make a plagiarism statement and your submission will be tested for originality.
• The report should be submitted along with an R project folder (compressed) that is comprised of an R project file, an executable R script (it needs to be clearly commented), and any data that is not provided but used in your analysis. Penalties will apply if the submission is not in this format.
• The background and brief below give important information of the tasks you are expected to perform, read them carefully.
• A piece of data from CRSP will be provided for you to start with.
Coursework requirement
Background:
You are brought in as a consultant to advise an investment company that is reviewing one of their existing portfolios. Thus far they have simply created a portfolio equally invested in all NYSE-listed US common stocks belonging in the retail trade sector (according to the Standard Industrial Classification Manual by the US Government, seehttps://www.osha.gov/data/sic-manual) since 06 Feb 2013.
The Brief:
Using the information above and the data provided, your task is to write a report addressing three separate requirements:
1. Provide a comprehensive review of the portfolio’s performance since its inception, starting with a summary of its holdings.
2. Advise on a different portfolio construction approach of your choice using the same assets. It will need to be backed up by detailed back-testing and analysis of the risk and return features of your method against the equal-weighted approach.
3. For reasons of risk control, the firm also asks you to identify a simple and low-cost strategy to reduce the volatility of the current portfolio. Hence you are asked to select at most two assets (broadly defined) that you expect will provide large diversification benefits whilst being easy to combine with the existing strategy. These “assets” may be anything that is easily traded -- including single stocks, assets drawn from other asset classes such as cryptocurrencies, an index ETF covering a geographical area or an industry sector. Your recommendation of assets to include must be made based on economic intuition and supported by formal analysis using historical data.
N.B.:
o Your first step will be to construct the portfolio itself from the data provided.
o You are expected to produce at least two plots supporting each requirement above, tables may be used at your choice. Your graphs must be informative and honest (not overstate the benefits of your recommendations).
o When reviewing portfolio performance, simple summary statistics of returns will not be sufficient, measures of portfolio performance or benchmarks should be involved.
o You need to give economic and academic justification for every decision you have made (e.g., the metric you choose to use to measure performance, alternative portfolio construction methods you would pursue and the way you propose to reach risk reduction).
o Your report should be properly sectioned with section titles reflecting the contents of each section.
o Proper academic citations and a reference list in the end are expected.
o Any data source used in your analysis should be properly referenced, making it clear where the data is from.
o The accompanying R script should be well commented. For example, the code should be divided into sections with comments explaining key steps.
o In addition to your report, you should also submit a compressed zip file of your project folder that contains your script and any relevant data.
Distribution of marks:
1. Q1 (20 marks)
2. Q2 (20 marks)
3. Q3 (30 marks)
4. Inclusion and quality of data visualisation (10 marks)
5. Inclusion and executability of R code (10 marks)
6. Inclusion and clarity of commenting to R script (5 marks)
7. Proper citation of sources (3 marks)
8. Good presentation of report (2 marks)
Essay Submission
Your final essay must be submitted on Blackboard before the date and time stated above. If you have any queries when submitting your work please contact the Accounting and Finance Undergraduate Office (acfi- ug@bristol.ac.uk)
Coursework Extension Request
If you require a coursework extension, please complete the online form. You must provide supporting evidence for an extension request. Please see the University website for information onhow to submit an extension request.
Late submission of coursework
You must submit assessed work before the deadline. Work that has been submitted late without extenuating circumstances is penalised by a reduction of ten marks (out of 100) for each 24-hour period after the agreed deadline, not including UK public holidays or University closure days. If the work is submitted late such that at least four such 24-hour periods have elapsed, the mark awarded will automatically default to zero. (Please see section 17 of theUniversity Regulations and Code of Practicefor further details)
Extenuating Circumstances
If you experience circumstances that may impact your ability to submit the essay by the deadline, please submit extenuating circumstances via the online form. Please see the University website for information on how to submit extenuating circumstances.
Plagiarism and Cheating
The University rules and guidance aboutplagiarism and cheating, and how they are dealt with by the university inthe regulations.
Marking Criteria and Scales
The University rules and guidance aboutMarking Criteria and Scales via this link.
2023-07-16