5 (Continued) Portfolio of Investment C and Investment D

EXHIBIT 10.5 (Continued) Portfolio of Investment C and Investment D

Probability

Deviation

Squared Deviation

Weighted

Squared Probability

Deviations Scenario

Return

Return

Expected Value

Expected Value

2 n 2 x n – E(x) (x n – E(x)) p n (x n – E(x))

σ 2 ( x) = 0.002275 σ(x) = 0.477 or 4.77% Notes:

E(x) = 0.0400

p n = probability of outcome n occurring x n = outcome n E(x) = expected value σ(x) = standard deviation 2 σ ( x) = variance

Risk and Expected Return

Step 1: Deviation of Return on Step 2: Step 3: Investment from its Expected Return

Multiply Weight the Investment Investment Deviations

Product by the Scenario Probability

C D Together Probability

–0.02520 As you can see in these calculations, in a boom economic environ-

Step 4: Covariance =

ment, when Investment C is above its expected return (deviation is posi- tive), Investment D is below its expected return (deviation is negative). In a recession, Investment C’s return is below its expected value and Investment D’s return is above its expected value. The tendency is for the returns on these portfolios to co-vary in opposite directions—pro- ducing a negative covariance of –0.0252.

Let’s see the effect of this negative covariance on the risk of the portfolio. The portfolio’s variance depends on:

■ The weight of each asset in the portfolio. ■ The standard deviation of each asset in the portfolio. ■ The covariance of the assets’ returns.

Let cov 1,2 represent the covariance of two assets’ returns. We can write the portfolio variance as:

Portfolio variance = w 2 2 2 1 2 σ 1 + w 2 σ 2 + 2cov 12 , w 1 w 2 (10-9) The portfolio standard deviation is:

Portfolio standard deviation = Portfolio variance (10-10) We can apply this general formula to our example, with Investment C’s

characteristics indicated with a 1 and Investment D’s with a 2, w 1 = 0.50 or 50%

w 2 = 0.50 or 50% σ 1 = 0.1400 or 14.00% σ 2 = 0.1997 or 19.97%

cov 1,2 = -0.0252 Then:

THE FUNDAMENTALS OF VALUATION

Portfolio variance = 0.50 2 0.1400 2 ( 2 ) 0.50 + ( 0.1997 2 ) 2 0.0252 + ( – ) 0.50 ( ) 0.50 ( ) = 0.002275

and:

Portfolio standard deviation = 0.002275 = 0.0477or 4.77% which, not coincidentally, is what we got when we calculated the standard

deviation directly from the portfolio returns under the three scenarios. 2 As we saw above, the standard deviation of the portfolio is lower than the standard deviations of each of the investments because the returns on Investments C and D are negatively related: When one is doing well the other may be doing poorly, and vice-versa. That is, the covariance is negative. The investment in assets whose returns are out of step with one another is the whole idea behind diversification. Diversifi- cation is the combination of assets whose returns do not vary with one another in the same direction at the same time.

If the returns on investments move together, we say that they are correlated with one another. Correlation is the tendency for two or more sets of data—in our case returns—to vary together. The returns on two investments are:

■ Positively correlated if one tends to vary in the same direction at the same time as the other. ■ Negatively correlated if one tends to vary in the opposite direction with respect to the other. ■ Uncorrelated if there is no relation between the changes in one with changes in the other.

Statistically, we can measure correlation with a correlation coeffi- cient. The correlation coefficient reflects how the returns of two securi- ties vary together and is measured by the covariance of the two securities’ returns, divided by the product of their standard deviations:

Correlation coefficient Covariance of two assets’ returns

= ------------------------------------------------------------------------------------------------------------------------------- (10-11)  Standard deviation of  Standard deviation of    returns on first asset   returns on second asset 

2 If we can calculate the standard deviation directly from the portfolio’s returns, why calculate it using the individual assets’ standard deviations and the covariance? We

did it to illustrate the role of the assets’ covariance in the portfolio’s risk.

Risk and Expected Return

By construction, the correlation coefficient is bounded between –1 and +1. 3 We can interpret the correlation coefficient as follows:

A correlation coefficient of +1 indicates a perfect, positive correlation between the two assets’ returns. ■

A correlation coefficient of –1 indicates a perfect, negative correlation between the two assets’ returns. ■

A correlation coefficient of 0 indicates no correlation between the two assets’ returns. ■

A correlation coefficients falling between 0 and +1 indicates positive, but not perfect positive correlation between the two assets’ returns.

A correlation coefficient falling between –1 and 0 indicates negative, but not perfect negative correlation between the two assets’ returns.

In the case of Investments C and D, the covariance of their returns is:

Correlation of returns on Investments C and D

Covariance of returns Investments C and D = -----------------------------------------------------------------------------------------------------------------------------------------

 Standard deviation of  Standard deviation of    returns on Investment C   returns on Investment D 

– 0.0252 = ---------------------------------------------- = – 0.9014 ( 0.1400 ) 0.1997 ( )

Therefore, the returns on Investment C and Investment D are negatively correlated with one another.

By investing in assets with less than perfectly correlated cash flows, you can get rid of—diversify away—some risk. The less correlated the cash flows, the more risk you can diversify away—to a point.

Let’s think about what this means for a company. Consider Proctor & Gamble whose products include Tide detergent, Prell shampoo, Pampers diapers, Jif peanut butter, and Old Spice cologne. Are the cash flows from these products positively correlated? To a degree, yes. The cash flows from these products depend on consumer spending for consumption goods. But are they perfectly correlated? No. For example, diaper sales depend on the diaper wearing population, whereas cologne products depend on the male cologne-wearing population. The cash flows of these different products also depend on the actions of competitors—the degree of competition may be different for the diaper market than for the peanut butter market. Further,

3 Dividing the covariance by the product of the standard deviations insures (mathe- matically) that this statistic is bounded by –1 and +1, allowing a cleaner interpreta-

tion of the relation between assets’ returns.

THE FUNDAMENTALS OF VALUATION

the cash flows of the products are affected by different input pricing—the costs of the raw inputs to make these products. If there is a bad year for the peanut crop, the price of peanuts may increase substantially, reducing cash flows from Jif—but this increase in peanut prices is not likely to affect the costs of, say, producing laundry detergent.