The main problem of empirical analysis is comparing the right “apples to apples”. The Sharpe Ratio is a statistical method for converting oranges to apples. Check out my example code on Github here. The higher the Sharpe Ratio, the greater the returns per unit of risk.

### Notes

1.) I briefly mention in the Jupyter Notebook that I use S&P 500 over treasury bills as the risk free benchmark data. The use of the benchmark data *changes the degree* of the Sharpe Ratio, but it does not change the* relative comparison* of two Sharpe Ratios using the same benchmark data. If A has a greater Sharpe Ratio than B, it will always be true regardless of the benchmark data. However, depending on the benchmark data, the degree of how much greater A is than B will change.

2.) This code can be scaled and automated relatively easily. One of the things that should happen is adding the Google Stock Market API to be read over hardcoded csv files. The current data is from 2016. Then it should be a simple process to compare several different companies in real time.

3.) I’m still thinking of ways to correct the risk measurement problem. By adding a regression line that fits the growth and then calculating standard deviation, that should help mitigate some of the problem. For this point, I would need to write a proof to show that one way is better than the other.