Tableau and Python Visualization for finance

DATA ANALYTICS & VISUALIZATION FOR FINANCE

2024-2025

Mission 3 (Individual) – Tableau and PythonDeadline: December 16th 2024, 23:59 (CET)In this individual assignment, you will have the opportunity to apply both your Tableau and Python skills working individually with a stock price/return dataset and using the techniques learned across the

bleau and Python labs. The aim is to analyze the statistics, create interesting visualizations, buildportfolios and analyze their performance so that you can embrace various tasks through your datavisualization and analytical skills. For that, you are required to build a daily stock price dataset made of “3stocks” (based on the choices assignedby your course professor) and spanning from October/2014 toOctober/2024. You will be able to create a dataset that you will usefor this mission. To complete theproject, you need to work on some “tasks” (please see below) and submit your individual work,accordingly.

Read the instructions below carefully and follow them closely.Project Objective:Imagine that you are now a junior data analyst with a decent experience in Tableau andPython. Yourcompany manager needs your help for a top project about understanding the stock performance of U.S

companies. To conduct such an assessment, you know that (as a good data analyst) analyzing and

visualizing past historical data is key. Therefore, you received the mission tasks below that you need to

follow step by step. Conduct all the visual and analytical methods that are requested and complete yourTask 2: Now, use Python for the analytical assessment of your data. Carry out the following analyses: Plotprices and returns. Compute standard deviation and maximum drawdown (as conducted in the labs) andreport the results. Compare the estimates across stocks. Computethe annualized Sharpe ratios, based on different choices of risk-free rates (e.g., 0.01, 0.03 and 0.05).Compute VaR and CVaR and interpret thefindings in your report.

Task 3: In this step, your task is to assess risk and return relationships. Again, use Python to generatedifferent efficient frontiers, using your selected stocks. First, generatethe covariance and correlationsmatrices to display the level of relationship among your stocks. Interpret the patterns. Then, generate the“two-asset” efficient frontier by choosing two stocks among all three. Plot the two-asset efficient frontierand interpret. Next, move to “N-asset” efficient frontier and thus use all three stocks to display theefficient frontier. What do you see? Do results change? If so, how and why? If not, think about why youdo not observe differences, compared to two-asset case. Interpret these findings in your report. As a finaljob for this task, create MSR, EW and GMV portfolios and compare them. Do they look similar? What arethe key differences that you can identify visually? Interpret the curves intuitively in terms of the riskreturn relationships. What can you say about diversification? Based on your analysis and visual patternscoming from the efficient frontier, how would you make a decision in terms of achieving minimumvolatility (risk) and/or maximum return?Expected Outcome:

Your assessment will be evaluated based on creativity, clarity, and the depth of analysis presented in yourwork, as well as the quality and efforts you put into creating the Tableu dashboard and Jupyter Notebook.More specifically, the professor will assess your final packfollowing the instructions above: the strengthof visual elements, creativity, clarity, soundness of the explanations, consistency of the study/storytelling,and evidence of your individual efforts. Innovative approaches to the Tableau and Python outputs/visuals,and presentation are 代写Tableau and Python Visualization for finance more than welcome, provided the guidelines above are respected. Please also keepin mind that this is an individual work and hence no interaction with others is permitted (thanks forrespecting the rule).Deliverables:

1. Tableau file (.twbx) including the dashboard and all the worksheets containing the visuals youcreated;

2. Jupyter Notebook file (.ipynb) including all the outputs;
3. Excel file(s) containing all datasets used based on the selected stocks (which you used to imporinto Tableau and Jupyter Notebook); 4. Data analyst report: Single PDF document including insights and statements on the interpretationof the results of the tasks that have been carried out in the assignment:

5. ChatGPT/generative AI statement: Statement on the use of different generative AI tools in theproject development (template below). Please add the statement at the end of your data analyst

report.Submit the above elements (in a zipped folder) by email to your professor.Deliverables that are not submitted by December 16th 23:59 (CET) will be assigned a.Statement on the Use of Generative AI tools in Project Development:For each generative AI tool (e.g., ChatGPT) used, provide the following statement:“I acknowledge that I made use of (specify the generative AI tool) as an aid in the development of thisproject. Specifically, (specify the generative AI tool) was used for:-(list the specific tasks forwhich this tool was used)I confirm that all final content was reviewed, edited, and verified to ensure accuracy, originality, andadherence to academic standards. I took care to critically assess and modify the output from (specify thegenerative AI tool) to align with my assignment’s goals and my own academic output.”Final instructions:

Here is some extra advice for the development of you individual work:Read the instructions above carefully.

• Any additional elements that enhance the level, quality, and clarity of the final work are verywelcome and can be taken into consideration in your final evaluation.

• The above instructions are the general requirements to follow and are also meant to give you acertain level of freedom to work on the project. So, be creative!

• We welcome and encourage you if you have an idea for how the outputs or visuals which servedas examples in the labs could be enhanced for a richer understanding on the performance of thstocks and portfolios.==This project and represents 30% of your final grade on this course. Therefore, we expect a projectwhich reflects a final work compatible with a relevant effort.

Good luck, and we look forward to seeing your innovative and insightful work!