Graphical representation of the portfolio diversification effect
In finance, we could remove firm-specific risk by combining different stocks in our portfolio. First, let us look at a hypothetical case by assuming that we have 5 years' annual returns of two stocks as follows:
Year |
Stock A |
Stock B |
---|---|---|
2009 |
0.102 |
0.1062 |
2010 |
-0.02 |
0.23 |
2011 |
0.213 |
0.045 |
2012 |
0.12 |
0.234 |
2013 |
0.13 |
0.113 |
We form an equal-weighted portfolio using those two stocks. Using the mean()
and std()
functions contained in NumPy
, we can estimate their means, standard deviations, and correlation coefficients as follows:
>>>import numpy as np >>>A=[0.102,-0.02, 0.213,0.12,0.13] >>>B=[0.1062,0.23, 0.045,0.234,0.113] >>>port_EW=(np.array(ret_A)+np.array(ret_B))/2. >>>round(np.mean(A),3),round(np.mean(B),3),round(np.mean(port_EW),3) (0.109, 0.146, 0.127) >>>round(np.std(A),3),round(np.std(B),3),round(np.std(port_EW),3) (0.075, 0.074, 0.027)
In the preceding...