As you can see from my previous post, I currently own positions in 6 stocks. About 20% of my portfolio value was in CSCO. Why is this a bad thing? If the stock performs well, my portfolio does proportionally well. The flip-side is that if it performs poorly, my portfolio will also do poorly. That single fact is what concerns me the most.

To illustrate the problem, let's consider a more extreme situation. Suppose I had all of my wealth invested equally in a single stock, which we will call AAA. Now let's say our 1 year prospects for this stock is normally distributed an expected value of 10% gain and a standard deviation of 20%.

We can expect a 10% gain

*on average*. However the law of large numbers only holds true over the course of many samples. In this case, there is 34% that you will

*lose*money this year. In fact, there is a 1% chance that you will lose over half of your money. The risk is simply too high to justify this kind of investment strategy.

The probability that you will lose money |

### Diversifying

However, we can lower this risk by taking a greater number of samples. One way of doing this is by investing in multiple independent stocks with positive expected returns. So now suppose we have two stocks, AAA and BBB, both with expected yearly returns of 10% and a standard deviation of 25%. We'll call these two random variables \(A\) and \(B\), and our portfolio return \(P\).\[ P = 0.5A + 0.5B \] We scale \(A\) and \(B\) by 0.5 because we are now investing only 50% of our portfolio in each.

\[ \mu_{0.5A} = 0.5\mu_A = 5 \\ \sigma_{0.5A} = 0.5 \sigma_A = 12.5

\] The sum of two independent, normally distributed random variables is also normally distributed with

\[ \mu = \mu_X + \mu_Y \\ \sigma^2 = \sigma_X^2 + \sigma_Y^2 \] So in our case

\[ \mu_P = 5+5 = 10 \\ \sigma_P = \sqrt{12.5^2 + 12.5^2} \approx 17.7

\] As you can see, by simply investing in two independent stocks, we can reduce our standard deviation by 30% while still maintaining the same expected value. Now the probability of losing more than 50% of your portfolio value is less than \(3.5 \times 10^{-4}\).

So what happens when you diversify even more? Here is a table illustrating the effects:

# of Stocks | Mean | Std Dev | Prob. Losing Money | Prob. Losing Over Half |
---|---|---|---|---|

1 | 10 | 25.0 | 0.34 | \(8.2 \times 10^{-3}\) |

2 | 10 | 17.7 | 0.29 | \(3.5 \times 10^{-4}\) |

3 | 10 | 14.4 | 0.24 | \(1.6 \times 10^{-5}\) |

5 | 10 | 11.2 | 0.19 | \(4.0 \times 10^{-8}\) |

10 | 10 | 7.9 | 0.10 | \(1.5 \times 10^{-14}\) |

100 | 10 | 2.5 | \(3.2 \times 10^{-5}\) | \(1.4 \times 10^{-127}\) |

### Additional Considerations

- While you are protected from losing a large sum of money, you are also preventing yourself from having an extraordinary return.
- The above analysis only holds when the variables are independent. This is not always true in the stock market. In particular, market sectors like "tech" or "automotive" are highly correlated.
- We used the normal distribution as an example. In reality, the distribution varies (and in particular, the tails of the distribution are much higher than theoretical models suggest). On the other hand, by the central limit theorem, the sum of iid random variables will converge to a normal distribution.
- Different stocks have different expected returns and standard deviation. You should take this into account when diversifying of your portfolio.
- When stock prices change, the proportions of your portfolio also changes. You must rebalance regularly.
- This applies not only to stocks! Diversifying can also mean investing in bonds, real estate, education, or anything at all!