Screening Prospects Dominance Transparencies for chapter 4

Introduction w In chapter 2 two types of dominance are introduced. Outcome dominance Mean-variance dominance Ask the students to recall them. What role dominance plays?

Outcome Dominance w Outcome dominance w Option a j dominates ak if and only if w y ij  y ik for all I w and y ij > y ik for at least one  i w This criterion is useful for eliminating options that are inferior. It reduces number of options and problem complexity.

Example for Outcome Dominance w Consider the following payoff matrix w a 1 a 2 a 3 w  w  w  w a 1 dominates a 2

Mean -Variance Dominance w Option a j dominates option a i iff w E(a j )  E(a i ) and V(a j ) ≤ V(a i ) with one of them an inequality. Option j E(a j ) V(a j ) Option 3 dominates options 2, option 1 dominates option 4. The efficient set: ES = { Options 1, 3 and 5}

Mean -Variance Dominance w Option a j dominates option a i iff w E(a j )  E(a i ) and V(a j ) ≤ V(a i ) with one of them an inequality. Option j E(a j ) V(a j ) Option 3 dominates options 2, option 1 dominates option 4. The efficient set: ES = { Options 1, 3 and 5}

Assumption For Dominance Validity w The Decision Maker is a Utility Maximazer w It is assumed if f j (y) dominates f i (y) then, In other words U(a j ) ≥ U(a i )

First-Degree Stochastic Dominance (FSD) w This is a generalization of payoff dominance to deal with a set of payoff distributions. w Assumptions: U(y) increasing, the decision maker prefers more. U(y) is smooth and differentiable. Discuss why this assumptions needed.

FSD Continued w a j dominates a i in FSD sense iff F i (y)  F i (y)  y Where F(y) is the cumulative probability distribution of the payoff. In other words option j provides a higher chance of obtaining higher pay.

FSD Example 1 w Check FSD for the following two course of actions p(  ) a 1 a

FSD Example 1 w Let us form pay off distributions w a 1 Y f 1 (y) F 1 (y) a 2 Y f 2 (y) F 2 (y)

Checking FSD Example1 w Interval F 1 (y) Sign F 2 (y) (- , 8) 0 = 0 [8, 9) 0.4 > 0 [9, 10) 0.4 > 0.3 [10, 11) 1.0 > 0.7 [11,  ) 1.0 = 1.0 F 1 (y)  F 2 (y), Therefore a 1 dominates a 2 in FSD The above can be done graphically also.

FSD Example 2 w Let a 1 provides a payoff Y 1 distributed U(-1, 1). w a 2 provides a payoff vector Y 2 distributed U(-2,2). w Check for FSD between a 1 and a 2.

Structure The Problem w f 1 (y) = ½  -1  y  1 = 0 otherwise f 2 (y) = ¼  -2  y  2 = 0  y < -1 = 0 if y < -1 F 1 (y) = ½ y + ½  -1  y  1 = 1  y > 1

FSD Example 2 = 0  y < -1 F 2 (y) = ¼ y + ½  -2  y  2 = 1  y > 2 It can be seen clearly that the two functions intersect at y=0 Therefore FSD fails or inconclusive.

Second-degree Stochastic Dominance (SSD) w Assumptions: All FSD assumptions Decision maker risk averse U(y) concave. w Let us define w a j dominates a i in SSD sense iff D i (z) ≥ D j (z)  z

SSD w Other characterization for SSD is given as: In other words the area F i (y) should not be less than that of F j (y). Therefore SSD can be checked graphically by examining the area under the two cumulative distribution functions.

SSD Graphically 0 y F 1 (y) F 2 (y) Area A Area B

SSD w If area A greater or equal to area B, then a2 dominates a1. Disadvantage of SSD is left tail sensitivity.. Example of this the following two a 1 is 99 with probability 1 a 2 : Y p 2 (y) a 2 does not dominate a 1 although it is far much better option. Graph the options to show that.

SSD Example w Check SSD dominance for the case given in example 2 of FSD. Recall the case w a 1 provides a payoff Y 1 distributed U(-1, 1). w a 2 provides a payoff vector Y 2 distributed U(-2,2). w Check for SSD between a 1 and a 2.

SSD Example = 0 if z <-1 w D 1 (z) = ¼ z 2 + ½ z + ¼ -1  z < 1 = z 1  z = 0 z < -2 D 2 (z) = 1/8 z2 + ½ z + ½ -2  z < 2 = z

Checking SSD Interval F 1 (y) Sign F 2 (y) (- , -2) 0 = 0 [-2, -1) 0 < 1/8 z 2 + ½ z + ½ [-1, 1) ¼ z 2 + ½ z + ¼ < 1/8 z 2 + ½ z + ½ [1, 2) z  1/8 z 2 + ½ z + ½ [2,  ) z = z D 2 (z)  D 1 (z ) for all z. Therefore a 1 dominates a 2 in SSD. The above can be done graphically also.

Some Results w If FSD holds then SSD holds. Proof is obvious. w If SSD holds FSD may not hold. The previous example shows that FSD does not but SSD holds.

Third Stochastic Dominance (TSD) w Assumptions: All the assumptions of SSD Decision maker decreasing risk averse How to check decreasing risk averse w Define the following: w Where D(z) as defined for SSD

TSD Definition w a j dominates a i in TSD iff. E[f j (y)] ≥ E[f i (y)] TD i (t) ≥ TD j (t) for all t w The second condition can be written as double integral or triple integral. Show that in class.

FSD, SSD and TSD Example w Check FSD, SSD and TSD between a 1 and a 2.  p(  ) a 1 a 2 11 2 2  3 1/ /4 44

FSD, SSD and TSD Example w a 1 : Y P 1 (y) Probability mass function F 1 (y) Distribution function a 2 : Y P 2 (y) Probability mass function F 2 (y) Distribution function

FSD, SSD and TSD Example w It is clear that FSD does not hold w Let us check SSD w Let DD(z) = D 2 (z) – D 1 (z) in computing D(z) replace integral by summation because of the discrete nature of the distribution. w DD(10) = 0.25 w DD(11) = -0.25, DD(12) = DD(13) =0 w Since DD(11) is negative, we conclude SSD inconclusive

FSD, SSD and TSD Example w Alternative way for checking SSD w Interval D 1 (z) sign D 2 (z) (- , 10) 0 = 0 [10, 11) 0 < 0.25z – 2.5 [11, 12) 0.75 z – z – 2.5 [12, 13) 0.75 z – 8.25 z [13,  ) z – 11.5 = z – 11.5 SSD inconclusive

TSD w To show TSD hold we need to show that for t = 10, 11, 12, 13 It can be seen that the sum for the above formula at t = 10 is 0.25, at t=11 it is 0 and it stays 0 for all other values. Also E(a 1 ) = E(a 2 ) = 11.5 Therefore TSD holds and a 1 dominates a 2

Nth Stochastic Dominance (NSD) w NSD can similarly be defined by integrating F i (y) and F j (y) n-1 times and examining the difference between the two integrals. If it has the same sign, then dominance holds, otherwise it does not. w IF FSD holds then NSD holds.

Applied Example in Securities w Porter and Carey (1974) applied stochastic dominance to screen 16 randomly selected companies. The companies are: w Company NumberCompany Name 1 Federal Paper Board 2 American Machine and Foundry 3 Smith Kline/French Lab 4 Rayonier 5 American Tobacco 6 Crowell-Collier 7 American Seating 8 Emerson Electric 9 Riegel Textile 10 Cero 11 American Distilling 12 American Investment 13 Johnson and Johnson 14 United Aircraft 15 Dresser Industries 16 Schering

Case Analysis w The rate of retain (ROR) for 54 periods (quarter) from the last quarter of 1963 to the first quarter of The distribution of the rate of return was developed. w ROR t = (P t – P t-1 ) + D t )/P t-1 w P t = price at period t w D t = Dividend in period t

Case Analysis Using FSD w The results of FSD is Company 2dominates 3 Company 5 dominates 3 Company 8 dominates 3, 5, 7

Case Analysis Using SSD w Results of SSD Dominance test Company 2 dominated 3 Company 4dominated10 Company 5dominated1,2,3,7. Company 7dominated1 Company 8 dominated1,2,3,4,5,6,7,9,10,11,13,14 15 Company 9 dominated10 Company 11dominated3 Company 12dominated3, 11 Company 13dominated1, 10 Company 14dominated10 Company 15dominated6, 9, 10, 14 Company 16dominated1, 4, 6, 9, 10, 13, 14, 15 Companies 1, 3, 6, 10dominated none

Case Final Analysis w FSD only screened 3 companies namely 3,5 and 7. w SSD reduced the efficient set to two companies namely companies 8 and 12. w From figure 4.3 in text firm 8 has initially a steeper cumulative distribution function and therefore can not dominate firm 12 under FSD, SSD or TSD. w From figure 4.3 it is clear firm 8 is the better choice.

Comments on Screening w Screening by dominance usually reduces the number of options to a manageable size. w In some cases screening by dominance could leave two options that may be ranked first and last when maximizing expected utility. w Assign example on page 77 for discussion by students