程序代写案例-FIN3018

欢迎使用51辅导,51作业君孵化低价透明的学长辅导平台,服务保持优质,平均费用压低50%以上! 51fudao.top


FIN3018
Financial Econometrics and Data Science
Group Project

November 2022
Dr. Han, Dun ([email protected])
br>The Group Project requires you to demonstrate knowledge of using R to import and
analyze a real-world data, the ability to specify and estimate appropriate models;
understanding of regression analysis and hypothesis testing and the ability to implement
these in R and interpret the output; This knowledge will be acquired by attending the
lectures and the tutorials classes.

Group:
Size: 1 to 4 (groups larger than 4 are not permitted)
All members of a given group will receive the same mark, and it is up to you to
determine the allocation of work within the group and to ensure that all group members
make a valid contribution. All members of a group should be happy with the whole
submission, as you assume joint responsibility.
The group leader should fill in the group information table (provided before Friday on
Canvas) and email me before next Wednesday (9 Nov).
Email me if your group want more member but you cannot find one or you want to join
a group but you cannot find one before next Tuesday (8 Nov). I will email all people
sharing the similar situation and let you discuss by yourselves.

Report:
Your group should prepare a report using your knowledge from the course. The word
limit for the written part of the assignment is 2,000 words (excluding tables and
references). Please note that this is the words limits, not a target. Output from R,
including charts or tables, can be pasted into the report.
You should include a copy of your code as the Appendix 1 in your report.
The template of the report will be provided before Friday on Canvas.

Deadline:
The deadline for submission of the assignment is 23:59 P.M. Friday 2nd December
2022. An electronic submission on Canvas must be made before this date and time.
Team leader from each group should upload an electronic version to the Canvas site.
Extensions to the deadline will only be given in very exceptional circumstances. Late
submissions will be penalized according to University policy.

Submit:
Reports must be submitted in PDF file. Follow the procedure below to submit your
project, which must be submitted as one single file.
1. Go to https://canvas.qub.ac.uk
2. Enter the login and password and click login.
3. Click on FIN3018 Financial Econometrics and Data Science (2022/23)
4. From the left-hand menu, go to Assignments.
5. You will see a box on the screen. You will need to add the Submission Title. Please
note that your title must include your group leader’s name with assigned number
followed by FIN3018 Project, e.g.
John Smith_group1_ FIN3018 Project
7. Click the browse button and select the file for your assignment on your computer
(Remember that this must be one file only).
8. Click the submit button. The beginning of your assignment work will be shown on
screen. Please check at this stage that you are submitting the correct file.
9. You will see on the screen a submission confirmation. You will also receive an e-
mail from Turn-It-In UK confirming the receipt of your submission. Please keep this
safe. This e-mail is also your proof of submission.
10. Log off. Click log out at the top of the screen.

Assignment

Question:
What are the factors that influence the return of the stock?

Download the data from Canvas (provided before Friday, with a general data
description). Answer the question in your report.

Some tips and steps:
1 Data preparation (Any outliers? Transforms? Descriptive statistics? Distribution?)
2 Fit the regression model with return of stocks (return rate? logarithmic return? excess
return?) as dependent variable and the variable provided in the dataset as independent
variables.
3 Find the final model.
4 What is the result of model?
5 Interpret your result.
6 Any application or suggestion based on your regression result?
7 Any disadvantage of your analysis and suggestion to further studies? (Above 15
points)
8 Add new factors by calculating the existing variables or add new data. Compare your
new model with the model above. What do you find? (15 points)

Assessment Criteria
Your work will be assessed in terms of how well you have carried out the Assignment,
in terms of e.g., appropriate construction of variables to use in the regression models;
correctness, clarity, completeness and relevance of your interpretations and
commentaries. The assignment is designed to test your understanding, and ability to
interpret the software-generated output in terms of the concepts and ideas discussed in
the lectures and classes.

欢迎咨询51作业君
51作业君

Email:51zuoyejun

@gmail.com

添加客服微信: abby12468