Need a Python Workbook for a Financial Econometrics Project mostly to do with Stocks in Python
Budget: $30 – $250 USD
You are an equity analyst group.
Your boss asks to you to focus on one stock market sector and provide a model that explains
its excess returns. In particular, he lets you choose one among the 10 Industry Portfolios
available at the Kenneth French’s website.
You will have to conduct the analysis at the monthly and the annual frequencies and you will
have to come up with a linear model that explains the returns in the industry you selected.
Your linear model should have the form:
(Rsec,t −Rf,t) = Xtβ + ut (1)
where Rsec,t is the return on the sector you chose, Rf,t is the risk-free rate and Xt is
a vector of variables of your choice (including a constant). For example, you could use
simple CAPM regressions, CAPM regressions augmented with the Fama-French factors,
CAPM regressions that incorporate the inflation rate etc... Please be creative! Financial
markets are always looking for people with new ideas!
The ultimate goal of the exercise is to understand what economic forces explain the excess
returns of a given industry.
Your boss wants you to be precise in answering the following questions:
1. Why did you decide to focus on a certain sector?
2. What sample did you use? You do not have to use data from 1926 until today: you
can decide on what sample you want to use for your analysis, but you have to explain
your choice.
3. What variables did you consider initially? Were they macroeconomic variables? Finan-
cial variables? Both? (You should initially consider at least 10-15 regressors). Were
the initial regressors you considered correlated among each other?
4. What variables did you discard along the way? Why were they discarded? Were their
coefficients insignificant? Did the coefficients make economic sense?
5. Do you find that similar variables explain monthly and annual returns, or do they
differ?
6. Are the linear regression assumptions satisfied in your final model? Do the coefficients
in your final model make economic sense?
3
Part II
Your boss also wants you to come up with a predictive model of the form:
(Rsec,t+1 −Rf,t+1) = Xtβ + ut+1, (2)
whereby you predict the returns next month (or next year; at time t + 1), using financial
variables and/or economic variables available today (at time t).
1. What variables seem to predict the returns on your sector?
2. What variables did you discard along the way? Why were they discarded? Were their
coefficients insignificant? Did the coefficients make economic sense?
3. Do you find that similar variables explain monthly and annual returns, or do they
differ?
4. How much of the variation in your sector’s excess returns’ can you explain with your
model?
5. Can you make any recommendation as to what is going to be the sector performance
next month? How about next year? Should your boss BUY or SELL the stocks
contained in the sector you have studied?
Has to be relevant to the coding at the level specified in the python notebook.
https://colab.research.google.com/drive/1bFMRk6emPQ5f1DYU0JMZpli3UojtddGc?authuser=2
Your boss asks to you to focus on one stock market sector and provide a model that explains
its excess returns. In particular, he lets you choose one among the 10 Industry Portfolios
available at the Kenneth French’s website.
You will have to conduct the analysis at the monthly and the annual frequencies and you will
have to come up with a linear model that explains the returns in the industry you selected.
Your linear model should have the form:
(Rsec,t −Rf,t) = Xtβ + ut (1)
where Rsec,t is the return on the sector you chose, Rf,t is the risk-free rate and Xt is
a vector of variables of your choice (including a constant). For example, you could use
simple CAPM regressions, CAPM regressions augmented with the Fama-French factors,
CAPM regressions that incorporate the inflation rate etc... Please be creative! Financial
markets are always looking for people with new ideas!
The ultimate goal of the exercise is to understand what economic forces explain the excess
returns of a given industry.
Your boss wants you to be precise in answering the following questions:
1. Why did you decide to focus on a certain sector?
2. What sample did you use? You do not have to use data from 1926 until today: you
can decide on what sample you want to use for your analysis, but you have to explain
your choice.
3. What variables did you consider initially? Were they macroeconomic variables? Finan-
cial variables? Both? (You should initially consider at least 10-15 regressors). Were
the initial regressors you considered correlated among each other?
4. What variables did you discard along the way? Why were they discarded? Were their
coefficients insignificant? Did the coefficients make economic sense?
5. Do you find that similar variables explain monthly and annual returns, or do they
differ?
6. Are the linear regression assumptions satisfied in your final model? Do the coefficients
in your final model make economic sense?
3
Part II
Your boss also wants you to come up with a predictive model of the form:
(Rsec,t+1 −Rf,t+1) = Xtβ + ut+1, (2)
whereby you predict the returns next month (or next year; at time t + 1), using financial
variables and/or economic variables available today (at time t).
1. What variables seem to predict the returns on your sector?
2. What variables did you discard along the way? Why were they discarded? Were their
coefficients insignificant? Did the coefficients make economic sense?
3. Do you find that similar variables explain monthly and annual returns, or do they
differ?
4. How much of the variation in your sector’s excess returns’ can you explain with your
model?
5. Can you make any recommendation as to what is going to be the sector performance
next month? How about next year? Should your boss BUY or SELL the stocks
contained in the sector you have studied?
Has to be relevant to the coding at the level specified in the python notebook.
https://colab.research.google.com/drive/1bFMRk6emPQ5f1DYU0JMZpli3UojtddGc?authuser=2