Investment Management with Python and Machine learning Specialization lecture Notes Part-I

I recently started learning about the Portfolio Management course on Coursera through the EDHEC Risk Management course. This is a brilliant course for someone who would like investment management roles requiring programming skills. So in this series of writings, I am documenting my learnings from the course. In this blog post, I will cover the first two weeks of Course 1.

WEEK 1

• In this week, we covered topics on annualized return, volatility, Sharpe ratio, VaR (Value at Risk)
• Return is the difference in portfolio value from time t to time t+1 divided by the portfolio value at time t. So this would (V_{t+1}-V_t)/V_t. We don’t consider the average return of a portfolio over a time period because the results might be skewed. If a portfolio gave sub-optimal results for the most part of the time and outstanding results for some part, it may have a higher average return, but it is not a preferable portfolio, so it may be a misleading KPI.
• Also, dividends are included as part of the portfolio value to calculate the return, ((V_{t+1}+D_t)-V_t)/V_t
• Suppose a portfolio gives a return of 10% in the first year and 3% in the second year. So compounding works in the following way if you invest $1 in the portfolio, after one year you will have 1 + 0.1*1 dollars, so 1.1. Again this is used as principal for the next year, hence 1.1 + 0.03*1.1. This is equal to 1.067. So…