Based on current dividend yields and expected capital gains, the expected rates of return on portfolios a and b are 11% and 14%, respectively. the beta of a is .8, while that of b is 1.5. the t-bill rate is currently 6%, while the expected rate of return of the s&p 500 index is 12%. the standard deviation of portfolio a is 10% annually, while that of b is 31%, and that of the index is 20%.

a. if you currently hold a market index portfolio, what would be the alpha for portfolios a and b? (negative values should be indicated by a minus sign. do not round intermediate calculations. round your answers to 1 decimal place.)

∝ for portfolio A = E(rA) - Required return predicted by CAPM

                           = 0.11 - [0.06 + 0.8 × (0.12 - 0.06)]

                           = 0.11 - [0.06 + 0.048]

                           = 0.11 - 0.108

                           = -0.002      ≈ -0.2%  to 1 decimal place

∝ for portfolio B = E(rB) - Required return predicted by CAPM

                           = 0.14 - [0.06 + 1.5 × (0.12 - 0.06)]

                           = 0.14 - [0.06 + 0.09]

                           = 0.14 - 0.15

                           = -0.01         ≈ -1.0% to 1 decimal place

the answer   is 200

step-by-step explanation:

this is a problem about poisson probabilities.   i don't know if you've

already studied poisson distributions, or if you have a place to look

to read more about the subject.  

here's how you can see a poisson distribution coming.   when there are

a lot of events that may happen at any time (or any place), and when

you're counting how many of them happen at a particular time (or a

particular place), and each event is independent of the others, then

the result is a poisson distribution.   this is roughly true for the

chocolate chips; one chip being in a cookie has only a small effect on

the probability that another chip will be in the cookie.

once you know you have a poisson distribution, you know a lot.   there

is only one parameter to a poisson distribution, so if you know the

mean, you know everything.   the form of the distribution is

  probability of n chips = {[e^(-x)]*(x^n)} / (n! )

answer: the price of one pound cabbage = $ 0.8

the point (10,8) represents that the cost of 10 pounds cabbage is $ 8

step-by-step explanation:

since, here is a proportional relation between the number of pounds of cabbage (x) and the price in dollars (y).

therefore, we can write,

⇒ y = k x

where k is the constant of proportionality.

now, point (5,4) is in the line.

thus, it will satisfies the above equation of line.

⇒ 4 = 5 k

⇒ k = 0.8

thus, the equation of the line is,

y = 0.8 x

a) for x = 1,

y = 0.8

therefore, the cost of one pound of cabbage is 0.8 dollars.

b) in the below graph (10,8) represents that the cost of 10 pounds of cabbage is 8 dollars.