C39RF程序代写、代做Python设计编程

日期：2025-02-26 09:12

Subject: C39RF Applied Financial Modelling in Python Case Study 1

Date: Submission deadline: 28th of February, 12pm UK time, 4pm Dubai time, and 8pm Malaysia

time.

Please note the following before you commence the assignment:

• You have to submit a Jupyter Notebook file (with extension ipynb) as well as a script with

the html extension which contains the solutions (output) to the tasks enumerated below.

Upload these files into the Assignment - Case Study 1 Submission. Failure to upload the

html file will result in losing 10 marks.

• Number the tasks so it is clear which one your are answering.

• You also have to submit all the csv files that contain your data - failing to do so will result in

losing marks.

• Make sure you don’t download data that was discussed in class (lectures and tutorials) such

as: IBM, META, Spotify, Apple, Nvidia, Microsoft, Google, Tesla, S&P index, FTSE100 index,

DAX index, VIX index, Bitcoin, Oil price, Gold price.

• Please remember that only four types of files are allowed to be uploaded onto Canvas/Turnitin:

ipynb, html, excel and csv. Do not upload PNG files. Make sure you download the files and

upload them well before the deadline. Practice downloading the ipynb and html files from

the Jupyter Notebook now.

• For each task, 25% of the marks will be awarded for successfully writing up the code, and

the rest of the marks (75%) will be given for explaining in-depth the results. If you are asked

to discuss for example a plot in 100 words and you only discussed it in 50 words, your mark

will reflect that. Of course, the content of your discussion matters primarily and not the

length of your discussion. Your discussion should always relate to results and you should

not discuss generic issues (such as defining what p-values or test statistics are) as those do

not carry marks.

• Discussions should be provided in a Markdown cell and not in a code cell as comments. Do

not provide definitions of statistical and econometrics terms as that will not yield marks.

• Only use code that was used in Lectures and Tutorials. Do not produce a script using

different coding techniques - otherwise, it will be assumed that external help was utilised,

which will result in your assessment being reported as academic misconduct.

• This assessment is worth 100 marks and it accounts for 50% of your final grade.

• Make sure you have read, understood and followed the Universitys Regulations on plagia rism as published on the Universitys website, that you are aware of the penalties that you

will face should you not adhere to the University Regulations:

https://www.hw.ac.uk/uk/services/academic-registry/academic-integrity/

academic-misconduct.htm

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• Make sure you have read, understood and avoided the different types of plagiarism ex plained in the University guidance on Academic Integrity and Plagiarism:

https://heriotwatt.sharepoint.com/sites/skillshub/SitePages/Academic-Integrity-and-Plagiarism.

aspx

You have to solve each task to get full marks.

1. Download daily adjusted close price of stock market data from Yahoo Finance for the period

January 2019 to December 2024 for two corporations from two different industries (choose

from: Automobile, Information Technology, Pharmaceuticals, Financial, Healthcare). The

two companies should be of high market capitalisation and they should not have been dis cussed in class. You should use a data scraping method that was used in class. 2 marks

2. Create a new dataframe (using the correct pandas method) with the two stocks. Make sure

the index column is not displayed. 1 mark

3. If the prices of the two stocks are of the same magnitude, plot a timeline of your two time

series (prices) in a single plot. However, if your two stock prices are of different magnitude,

display the two plots separately. Make sure the timeline (date) is visible. Name the axes and

give a title. Also, provide a legend. Discuss the figure in a Markdown cell in 100 words. 3

marks

4. Calculate the daily first differenced log returns for your two variables. 2 marks.

5. Check for missing values in the two returns series and remove them. Then inspect the head

of the two time series to show there are no missing values. Also, display the last 10 rows of

your returns. All these tasks should be executed in a single cell, not separately. 3 marks

6. Save the dataframe as a csv file. You will have to submit this file along with your Jupyter

Notebook and html files. 0.5 mark

7. Calculate the summary statistics for the two stock market returns. Critically discuss the

summary statistics in 200 words in a Markdown cell. 3 marks

8. Calculate the correlation between the two stock returns. Discuss your results briefly (max. 3

sentences) in a Markdown cell. 2 marks

9. Plot a histogram with 70 bins for both of your stock returns. Display the two histograms in

separate figures. Also save your histograms in a png format. These tasks should be executed

in one cell. Discuss in a Markdown cell in 100 words whether the data appears normally

distributed. 4 marks

10. Plot a timeline of your two returns in a single plot. Make sure the timeline (date) is visible.

Discuss the figure in a Markdown cell in no more than 100 words. 3 marks

11. Check your two returns’ series for stationarity and discuss the results in-depth in a Mark down cell in no more than 150 words. 3 marks

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12. Check if your two returns’ series have outliers. Plot a boxplot for each of the time series

showing the outliers. Discuss in a Markdown cell the plots in 100 words. 3 marks

13. Remove the outliers and replot the two boxplots. Discuss in a Markdown cell the plots in

100 words. 3 marks

14. Download the daily adjusted prices of 30 individual stocks of a main stock market index

(stock market index constituents). You can find the list of indices here: https://finance.yahoo.com/world indices/. We’ve done a similar task for the DAX30 index stock market constituents. At this

stage you need to download the individual stocks of the index and not the index itself. The

stocks should not be the constituents of the S&P500, FTSE100 or the DAX30 indices. The

target period is January 2019 to December 2024. Discuss the index, how is calculated and its

constituents briefly in 100 words in a Markdown cell. 2 marks

15. Calculate and plot the cumulative returns time series for the index constituents. Discuss the

plot in no more than 100 words in a Markdown cell. 3 marks

16. Save the cumulative returns in a csv file. You will have to submit this file along with your

Jupyter Notebook and html files. 0.5 mark

17. Compute and plot the first principal component and discuss your results in detail (300

words). The task is to find out which stocks cause the highest degree of variability in the

index. 8 marks

18. Build a portfolio of stocks by allocating funds proportionally to the 1st principal component

in order to replicate the returns of your chosen index. You need to calculate the cumulative

returns using the weights of the top stocks that form the 1st principal component. 2 marks

19. Plot the cumulative returns of the newly created portfolio. Also, save the figure as a png file.

The two tasks should be executed in one cell. Discuss the plot in 100 words. 2 marks

20. Download the daily adjusted closing price for the index for the January 2019-December 2024

period. 2 marks

21. Calculate the first differenced log returns for the index and save them in a csv file. You will

have to submit this file as part of your assessment. 1 mark

22. Plot in one figure the portfolio of stocks you’ve created using the first principal component

as well as the returns of the index. Discuss whether the portfolio tracks the index or not in a

maximum of 200 words in a Markdown cell. 4 marks

23. Evaluate the effect of the Covid19 pandemic on individual stock returns. Discuss the results

in-depth in 250 words in a Markdown cell. 9 marks

24. Download daily adjusted closing price data for two stocks: one from the Telecom industry

(this will be your dependent variable) and one from the Energy industry (this will be your

independent variable). Both companies should be of high market capitalisation. The period

of interest is January 2000 to December 2024. 2 marks

25. Calculate first the differenced log returns, then transform the data to a dataframe and plot

both returns in one plot. The first two tasks should be executed in one cell. Discuss the plot

in 100 words in a Markdown cell. 4 marks

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26. Save the returns as a csv file. You will have to submit this file along with your Jupyter

Notebook and html files. 0.5 mark

27. Plot a histogram with 80 bins for both returns separately. Discuss the normality of your data

in a Markdown cell in 100 words. 3 marks

28. Run summary statistics on your returns dataframe and discuss the results in 100 words in a

Markdown cell. 2 marks

29. Calculate the correlation, skewness and kurtosis of the returns. Discuss the results in 150

words in a Markdown cell. 4.5 marks

30. Run an OLS regression and discuss your results in-depth in a Markdown cell in 250 marks.

9 marks

31. Calculate the regression residuals and test these for the Classical Linear Model assumptions.

Discuss your results in a Markdown cell in 300 words. Provide plots where necessary. 9

marks

Total 100 marks

Don’t forget the following:

• Make sure you show all of the outputs (solutions, plots, etc) before downloading the ipynb

and html files.

• Download the ipynb and html scripts and upload them to the Assessment page.

• Upload all the csv files to the Assessment page. Do not upload the png files onto Canvas.

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