# Advanced Portfolio Construction and Analysis with Python Quiz Answers

## All Weeks Advanced Portfolio Construction and Analysis with Python Quiz Answers

The practice of investment management has been transformed in recent years by computational methods. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language.

In this course, we cover the estimation, of risk and return parameters for meaningful portfolio decisions, and also introduce a variety of state-of-the-art portfolio construction techniques that have proven popular in investment management and portfolio construction due to their enhanced robustness.

#### Advanced Portfolio Construction and Analysis with Python Coursera Quiz Answers

#### Week 1: Advanced Portfolio Construction and Analysis with Python

Q1. Load the 49 Value Weighted industry portfolio returns. Limit the analysis to returns of the 49 VW portfolios from 1991 onwards (1991 included, 1990 not included, 2018 included). Also load the Fama French Research Factors over the same period. Use the Mkt-RF as in the lab notebooks to compute the CAPM betas.

What is the CAPM (Single Factor) Beta when evaluated over the entire period (1991-2018) of Beer?

Enter the answer as a number to at least two decimal places (e.g. 0.92)

Enter answer here

Q2. Using the same data as the previous question, what is the CAPM Beta when evaluated over the entire period (1991-2018) of Steel?

Enter your answer as a number to at least 2 decimal places (e.g. 1.12)

Enter answer here

Q3. Using the same data as the previous question, what is the CAPM Beta when evaluated over the 2013-2018 (both included) period of Beer?

Enter the answer as number up to at least 2 decimal places (e.g. 1.12)

Enter answer here

Q4. Using the same data set as the previous question, what is the CAPM Beta when evaluated over the 2013-2018 (both included) period of Steel?

Enter the answer as a number up to at least 2 decimal places (e.g. 1.12)

Enter answer here

Q5. Using the same data as the previous question, which of the 49 industries had the highest CAPM Beta when evaluated over the 1991-1993 (both included) period? (Use the same industry names as in the files). Enter the name as a text string, and remember to exactly match the column headers in the data file.

Enter answer here

Q6. Using the same data as the previous question, which of the 49 industries had the lowest CAPM Beta when evaluated over the 1991-1993 (both included) period? (Use the same industry names as in the files) . Enter the answer as text and remember to exactly match the column headers in the data file.

Enter answer here

Q7. For the next 4 questions use the Full 3 Factor Fama-French model using the research data supplied in the following data file:

F-F_Research_Data_Factors.csv

and the same 1991-2018 period you have just used for the previous questions.

Of the 49 industries, which displayed the highest Small Cap tilt when analyzed over the entire 1991-2018 period?

Enter your answer as a text string, and remember to use the exact same name as the column header in the text file

Enter answer here

Q8. Using the same dataset and period as the previous question, of the 49 industries, which displayed the highest Large Cap tilt when analyzed over the entire period?

Enter the name of the industry as text, using the exact same name as the column header in the data file.

Enter answer here

Q9. Using the same data as period as the previous question, of the 49 industries, which displayed the highest Value tilt when analyzed over the entire period?

Enter the name of the industry as text, using the exact same name as the column header in the data file.

Enter answer here

Q10. Using the same data set and period as the previous question, of the 49 industries, which displayed the highest Growth tilt when analyzed over the entire period?

Enter the name of the industry as text, using the exact same name as the column header in the data file.

Enter answer here

#### Week 2: Advanced Portfolio Construction and Analysis with Python

Q1. Load the 30 Value Weighted industry portfolio returns. Limit the analysis to returns of the 30 VW portfolios from 1997 onwards (1997 included, 1996 not included, 2018 included). Also load the Market Caps of each of the 30 industries. Run a backtest of comparing a CapWeighted vs an EW portfolio over the period. Though these two weighting schemes do not need any estimation, use an estimation period of 36 months so that we can compare it in the next few questions.

Hint: If you want to use the supplied code, import the final version of the backtester in file edhec_risk_kit_206 using:

import edhec_risk_kit_206 as erk

Remember that to load the Value Weighted series you should use erk.get_ind_returns(weighting=”vw”, …)

and then use erk.backtest_ws(rets, estimation_window=36, weighting=…) The first month for which you have backtested returns should be 2000-01 and the last month should be 2018-12

What was the annualized return over period for the Cap Weighted Industry Portfolio?

Enter the answer as a percentage (e.g. for a return of 5.67% enter 5.67)

Enter answer here

Q2. Use the same data and period as the previous question.

What was the annualized volatility over the period for the Cap Weighted Industry Portfolio?

Enter the answer as a percentage (e.g. for a vol of 25.67% enter 25.67)

Enter answer here

Q3. Use the same data and period as the previous question.

What was the annualized return over period for the Equal Weighted Industry Portfolio?

Enter the answer as a percentage (e.g. for a return of 5.67% enter 5.67)

Enter answer here

Q4. Use the same data and period as the previous question.

What was the annualized volatility over the period for the Equal Weighted Industry Portfolio?

Enter the answer as a percentage (e.g. for a vol of 15.67% enter 15.67)

Enter answer here

Q5. Now using the same data and same period, re-run the EW backtest but this time create a tethered EW portfolio by removing Microcap industries using a threshold of 1% and a max cap-weight multiple of 2X

What was the annualized return over the period for the tethered Equal Weighted Industry Portfolio?

Enter the answer as a percentage (e.g. for a return of 5.67% enter 5.67)

Enter answer here

Q6. Use the same tethered EW portfolio (by removing Microcap industries using a threshold of 1% and a max cap-weight multiple of 2X) as in the previous question.

What was the annualized volatility over the period for the tethered Equal Weighted Industry Portfolio?

Enter the answer as a percentage (e.g. for a vol of 5.67% enter 5.67)

Enter answer here

Q7. What was Tracking Error between the Pure EW (without any tethering) portfolio and the CW portfolio?

Enter the answer as a percentage (e.g. for a Tracking Error of 5.67% enter 5.67)

Enter answer here

Q8. Now consider the Tethered EW portfolio you previously created (by removing Microcap industries using a threshold of 1% and a max cap-weight multiple of 2X)

What was Tracking Error between the Tethered EW portfolio and the CW portfolio?

Enter the answer as a percentage (e.g. for a Tracking Error of 5.67% enter 5.67)

Enter answer here

Q9. Run a backtest for the same period (1997 onwards i.e. 1997 included, 1996 not included, 2018 included), using an estimation window of 36 months as above, to build the Global Minimum Variance Portfolio by estimating the Covariance matrix using Sample Covariance. (This might take a minute or so to run depending on your computer power!)

What was the annualized return over the period for the GMV Portfolio?

Enter the answer as a percentage (e.g. for a return of 5.67% enter 5.67)

Enter answer here

Q10. Use the same GMV portfolio as the previous question. What was the annualized volatility over the period for the GMV Portfolio?

Enter the answer as a percentage (e.g. for a vol of 5.67% enter 5.67)

Enter answer here

Q11. Run a backtest for the same period as the previous question, and again using an estimation window of 36 months as above, to build the Global Minimum Variance Portfolio but this time, estimating the Covariance matrix using Shrinkage between the Constant Correlation and Sample Covariance estimates using a delta of 0.25. (This might take a minute or so to run depending on your computer power!)

What was the annualized return over the period for this new Shrinkage-GMV Portfolio?

Enter the answer as a percentage (e.g. for a return of 5.67% enter 5.67)

Enter answer here

Q12. Using the same Shrinkage-GMV portfolio return series you used in the previous question, what was the annualized volatility over the period for the Shrinkage-GMV Portfolio?

Enter the answer as a percentage (e.g. for a vol of 5.67% enter 5.67)

Enter answer here

#### Week 3: Advanced Portfolio Construction and Analysis with Python

Q1. Load the 49 industries Value weighted returns and cap weights, and use the period 2013-2018 both included. For the period, use the starting cap weights of the period. Limit yourself to the following 5 industry sectors: ‘Hlth’, ‘Fin’, ‘Whlsl’, ‘Rtail’, ‘Food’.

You will need to compute the correlation matrix as well as the volatilities. (Hint: Remember to annualize the volatilities by multiplying the volatility you get from the monthly data by the sqrt iof 12)

Using the same value of delta used in the He-Litterman paper of 2.5 and using the same sigma prior methodology used in the notebook and in the paper, compute the implied returns vector.

Which industry sector has the highest capweight?

Enter your answer as text, exactly as they are named in the Data file (i.e. ‘Hlth’, ‘Fin’, ‘Whlsl’, ‘Rtail’, or ‘Food’)

Enter answer here

Q2. Use the same data as the previous question, which industry sector has the highest implied return?

Enter your answer as text, exactly as they are named in the Data file (i.e. ‘Hlth’, ‘Fin’, ‘Whlsl’, ‘Rtail’, or ‘Food’)

Enter answer here

Q3. Use the same data and assumptions as the previous question.

Which industry sector has the lowest implied return?

Enter your answer as text, exactly as they are named in the Data file (i.e. ‘Hlth’, ‘Fin’, ‘Whlsl’, ‘Rtail’, or ‘Food’)

Enter answer here

Q4. Impose the subjective relative view that Hlth will outperform Rtail and Whlsl by 3% (Hint: Use the same logic as View 1 in the He-Litterman paper)

What is the entry you will use for the Pick Matrix P for Whlsl. (Hint: Remember to use the correct sign)

Enter the number as a positive or negative number correct to at least two decimal places (e.g. -0.23 or +0.23)

Enter answer here

Q5. Impose the subjective relative view that Hlth will outperform Rtail and Whlsl by 3% (Hint: Use the same logic as View 1 in the He-Litterman paper)

What is the entry you will use for the Pick Matrix P for Rtail. (Hint: Remember to use the correct sign)

Enter the number as a positive or negative number correct to at least two decimal places (e.g. -0.234 or +0.234)

Enter answer here

- Impose the subjective relative view that Hlth will outperform Rtail and Whlsl by 3% (Hint: Use the same logic as View 1 in the He-Litterman paper)

Once you impose this view (use delta = 2.5 and tau = 0.05 as in the paper), which sector has the lowest implied return?

Enter your answer as text, exactly as they are named in the Data file (i.e. ‘Hlth’, ‘Fin’, ‘Whlsl’, ‘Rtail’, or ‘Food’)

Enter answer here

Q7. Impose the subjective relative view that Hlth will outperform Rtail and Whlsl by 3% (Hint: Use the same logic as View 1 in the He-Litterman paper)

Which sector now has the highest weight in the MSR portfolio using the Black-Litterman model

Enter answer here

Q8. Impose the subjective relative view that Hlth will outperform Rtail and Whlsl by 3% (Hint: Use the same logic as View 1 in the He-Litterman paper)

Which sector now has the lowest weight in the MSR portfolio using the Black-Litterman model?

Enter answer here

Q9. Now, let’s assume you change the relative view. You still think that it Hlth will outperform Rtail and Whlsl but you think that the outperformance will be 5% not the 3% you originally anticipated.

Which of the arrays will you need to update?

- Neither P nor Q but a different parameter
- Both P and Q
- Q and not P
- P and not Q

Q10. Now, let’s assume you change the relative view. You still think that it Hlth will outperform Rtail and Whlsl but you think that the outperformance will be 5% not the 3% you originally anticipated.

Under this new view which sector has the highest expected return?

Enter answer here

Q11. Now, let’s assume you change the relative view. You still think that it Hlth will outperform Rtail and Whlsl but you think that the outperformance will be 5% not the 3% you originally anticipated.

Under this new view which sector does the Black-Litterman model assign the highest weight?

Enter answer here

#### Week 4: Advanced Portfolio Construction and Analysis with Python

Q1. Load the 49 Value Weighted industry portfolio returns. Limit the analysis to returns of the 49 VW portfolios from the most recent 5 years for which you have data i.e 2014-2018 both years inclusive. Also load the Market Caps of each of the 49 industries. Assume that the cap-weights as of the first month (2014-01) are the cap-weights we’ll use for this analysis.

Which Industry had the highest risk contribution in the cap-weighted portfolio?

Enter your answer as text, exactly as they are named in the Data file (i.e. Hlth, Fin, Whlsl, Rtail, or Food)

Enter answer here

Q2. Use the same data as the previous question. What was the highest risk contribution from any one industry in the cap-weighted portfolio?

Enter the answer as a percentage (e.g. for 5.67% enter 5.67)

Enter answer here

Q3. Use the same data set as the previous question.

Which Industry had the highest risk contribution in the equal-weighted portfolio?

Enter your answer as text, exactly as they are named in the Data file (i.e. Hlth, Fin, Whlsl, Rtail, or Food)

Enter answer here

Q4. Use the same data as the previous question.

What was the highest risk contribution from any one industry in the equal-weighted portfolio?

Enter the answer as a percentage (e.g. for 5.67% enter 5.67)

Enter answer here

Q5. Using the Sample Covariance over the 5 year period, compute the weights of the ERC portfolio. What sector portfolio is assigned the highest weight in the ERC portfolio?

Enter your answer as text, exactly as they are named in the Data file (i.e. Hlth, Fin, Whlsl, Rtail, or Food)

Enter answer here

Q6. Using the same data as the previous question (Sample Covariance over the 5 year period, and the initial capweights over that period) compute the weights of the ERC portfolio. What is the weight of the sector portfolio that is assigned the highest weight in the ERC portfolio?

Enter the answer as a percentage (e.g. for 5.67% enter 5.67)

Enter answer here

Q7. Use the same data as the previous question (Sample Covariance over the 5 year period, and the initial capweights over that period) and the weights of ERC portfolio. What sector portfolio is assigned the lowest weight in the ERC portfolio?

Enter answer here

Q8. Use the same data as the previous question (Sample Covariance over the 5 year period, and the initial capweights over that period) and the weights of ERC portfolio.

What is the weight of the sector portfolio that is assigned the lowest weight in the ERC portfolio?

Enter the answer as a percentage (e.g. for 5.67% enter 5.67)

Enter answer here

Q9. Use the same data as you used in the previous question (Sample Covariance over the 5 year period and the initial capweights over that period).

In the cap-weighted portfolio what is the difference in risk contributions between the largest contributor to portfolio risk and the smallest contributor to portfolio risk?

Enter the answer as a percentage (e.g. for 5.67% enter 5.67)

Enter answer here

Q10. Use the same data as you used in the previous question (Sample Covariance over the 5 year period and the initial capweights over that period).

In the equal-weighted portfolio what is the difference in risk contributions between the largest contributor to portfolio risk and the smallest contributor to portfolio risk?

Enter the answer as a percentage (e.g. for 5.67% enter 5.67)

Enter answer here

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