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Advanced Financial Modeling (Python Homework Help) Fall 2021 – Unit 1 Project Due Total project grades will be out of 50

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Advanced Financial Modeling (Python Homework Help)

Fall 2021 – Unit 1 Project Due

Total project grades will be out of 50.

1. Warm-Up (5 pts). Write a line of code that prints all even numbers between 0 and 100. Do not use a loop to do so.
2. Fibonacci Sequence (10 pts). The Fibonacci sequence is a set of numbers where following the first two numbers of the sequence (0 and 1), each successive number is the sum of the prior 2 numbers. Write a function that prints the first N digits of the sequence where N is an input into the function. For example, if 10 is provided as an input {0, 1, 1, 2, 3, 5, 8, 13, 21, 34} is printed.
3. Tech Stocks and March 2020 (15 pts). The purpose of this exercise is to examine the performance of prominent tech industry stocks over the course of 2020, while better understanding indexing in Python.
   1. 1. Start by loading the main dataset into Python, techstocks.xlsx. How many columns are there / what companies are we looking at?
      2. Convert the date column to a Pandas datetime object as we have done previously in the live session. Choose either the iloc or loc commands to trim the dataset such that it starts on February 28, 2020.
      3. What is the cumulative performance of each ticker from March till the end of the dataset? To compute the cumulative performance add 1 to all returns and take the product of all returns, over the relevant period.
      4. What is the performance of each ticker over March and April combined? Which companies benefited the most from the shift in work conditions due to COVID?
4. Industry Return Properties (20 pts). The purpose of this exercise is to examine industry returns on a monthly basis and see which sectors display the greatest risks overall.
   1. Start by loading the main dataset into Python, indreturns french.xlsx. The data are monthly returns for 17 industries from Ken French’s website. Make the index column into a Pandas datetime object. How far does the data set go back?
   2. Compute the mean, standard deviation, and Sharpe ratio of monthly returns in each industry. You do not need to take into account risk free returns when computing Sharpes. Which 5 industries display the highest Sharpe ratios? What are they?
   3. Using only returns starting from January of 1970, re-compute the Sharpe ratios. Are the industries that display the highest risk-adjusted returns, different than those identified in part (b)?
   4. Sharpe ratios are only one (crude) measure of measuring risk-adjusted returns. We can also examine how industries perform when the overall market is severely underperforming. Using data from the S&P 500, one can find that the worst five performing months (after 1970) are given by:

Using the loc operator, examine the returns across industry in these 5 months. Which industry (or industries) seem to be insulated against market crashes?

1. 5. Explain, in words, why the above form of analysis is a qualitative way of examining industry beta to the market?