Portfolio selection OR。 Harry Markowitz The Journal of Finance, Vol. 7, No. 1.(Mar, 1952), pp. 77-91 Stable url: http://links.jstororg/sici?sici=0022-1082%28195203%297%03a1%3c77%03aps%3e2.0.co%3b2-1 The Journal of Finance is currently published by American Finance Association Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.htmlJstOr'sTermsandConditionsofUseprovidesinpartthatunlessyouhaveobtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the jsTOR archive only for your personal, non-commercial use Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at Each copy of any part of a JSTOR transmission must contain the same copyright notice that ap on the screen or printed page of such transmission The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to journals and scholarly literature from around the world. The Archive is supported by libraries, scholar ies, publishers, and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the community take advantage of advances in technology. For more information regarding JSTOR, please contact support@jstor. org Mon Sep301:12:502007
Portfolio Selection Harry Markowitz The Journal of Finance, Vol. 7, No. 1. (Mar., 1952), pp. 77-91. Stable URL: http://links.jstor.org/sici?sici=0022-1082%28195203%297%3A1%3C77%3APS%3E2.0.CO%3B2-1 The Journal of Finance is currently published by American Finance Association. Your use of the JSTOR archive indicates your acceptance of JSTOR's Terms and Conditions of Use, available at http://www.jstor.org/about/terms.html. JSTOR's Terms and Conditions of Use provides, in part, that unless you have obtained prior permission, you may not download an entire issue of a journal or multiple copies of articles, and you may use content in the JSTOR archive only for your personal, non-commercial use. Please contact the publisher regarding any further use of this work. Publisher contact information may be obtained at http://www.jstor.org/journals/afina.html. Each copy of any part of a JSTOR transmission must contain the same copyright notice that appears on the screen or printed page of such transmission. The JSTOR Archive is a trusted digital repository providing for long-term preservation and access to leading academic journals and scholarly literature from around the world. The Archive is supported by libraries, scholarly societies, publishers, and foundations. It is an initiative of JSTOR, a not-for-profit organization with a mission to help the scholarly community take advantage of advances in technology. For more information regarding JSTOR, please contact support@jstor.org. http://www.jstor.org Mon Sep 3 01:12:50 2007
PORTFOLIO SELECTION* HARRY MARKOWIT The Rand Cor poration THE PROCESS OF SELECTING a portfolio may be divided into two stages The first stage starts with observation and experience and ends with beliefs about the future performances of available securities. The second stage starts with the relevant beliefs about future performances nd ends with the choice of portfolio. This paper is concerned with the second stage. We first consider the rule that the investor does(or should maximize discounted expected, or anticipated, returns. This rule is re- jected both as a hypothesis to explain, and as a maximum to guide in vestment behavior. We next consider the rule that the investor does(or should) consider expected return a desirable thing and variance of re- turn an undesirable thing This rule has many sound points, both as a maxim for, and hypothesis about, investment behavior We illustrate geometrically relations between beliefs and choice of portfolio accord- to the " expected returns-variance of returns" ru One type of rule concerning choice of portfolio is that the investor does(or should) maximize the discounted (or capitalized) value of future returns. I Since the future is not known with certainty, it must be"expected"or"anticipated""returns which we discount. variations of this type of rule can be suggested. Following Hicks, we could let "anticipated"returns include an allowance for risk. 2 Or, we could let the rate at which we capitalize the returns from particular securities vary with risk. The hypothesis (or maxim) that the investor does (or should) maximize discounted return must be rejected. If we ignore market im perfections the foregoing rule never implies that there is a diversified portfolio which is preferable to all non-diversified portfolios. Diversi- fication is both observed and sensible: a rule of behavior which does not imply the superiority of diversification must be rejected both as a hypothesis and as a maxim Council. It will be reprinted as Cow les Commiss New Series, No. 60. 1. See, for example, J B. Williams, The Theory of Investment Value( Cambridge, Mass.: Harvard University Press, 1938), pp 55-75 J. R. Hicks, Value and Capital (New York: Oxford University Press, 1939), p. 126 licks applies the rule to a f her than a portf
PORTFOLIO SELECTION* HARRYMARKOWITZ The Rand Corporation THEPROCESS OF SELECTING a portfolio may be divided into two stages. The first stage starts with observation and experience and ends with beliefs about the future performances of available securities. The second stage starts with the relevant beliefs about future performances and ends with the choice of portfolio. This paper is concerned with the second stage. We first consider the rule that the investor does (or should) maximize discounted expected, or anticipated, returns. This rule is rejected both as a hypothesis to explain, and as a maximum to guide investment behavior. We next consider the rule that the investor does (or should) consider expected return a desirable thing and variance of return an undesirable thing. This rule has many sound points, both as a maxim for, and hypothesis about, investment behavior. We illustrate geometrically relations between beliefs and choice of portfolio according to the "expected returns-variance of returns" rule. One type of rule concerning choice of portfolio is that the investor does (or should) maximize the discounted (or capitalized) value of future returns.l Since the future is not known with certainty, it must be "expected" or "anticipatded7' returns which we discount. Variations of this type of rule can be suggested. Following Hicks, we could let "anticipated" returns include an allowance for risk.2 Or, we could let the rate at which we capitalize the returns from particular securities vary with risk. The hypothesis (or maxim) that the investor does (or should) maximize discounted return must be rejected. If we ignore market imperfections the foregoing rule never implies that there is a diversified portfolio which is preferable to all non-diversified portfolios. Diversification is both observed and sensible; a rule of behavior which does not imply the superiority of diversification must be rejected both as a hypothesis and as a maxim. * This paper is based on work done by the author while at the Cowles Commission for Research in Economics and with the financial assistance of the Social Science Research Council. It will be reprinted as Cowles Commission Paper, New Series, No. 60. 1. See, for example, J.B. Williams, The Theory of Investment Value (Cambridge, Mass.: Harvard University Press, 1938), pp. 55-75. 2. J. R. Hicks, Val~eand Capital (New York: Oxford University Press, 1939), p. 126. Hicks applies the rule to a firm rather than a portfolio
The journal of finance The foregoing rule fails to imply diversification no matter how the anticipated returns are formed; whether the same or different discount rates are used for different securities: no matter how these discount rates are decided upon or how they vary over time. 3 The hypothesis implies that the investor places all his funds in the security with the greatest discounted value. If two or more securities have. the same val- ue, then any of these or any combination of these is as good as any We can see this analytically: suppose there are N securities; let ra be the anticipated return(however decided upon) at time t per dollar in vested in security i; let da be the rate at which the return on the i' security at time t is discounted back to the present; let X, be the rela tive amount invested in security i. We exclude short sales, thus X:>0 for all i. Then the discounted anticipated return of the portfolio is =∑X; dir rit is the discounted return of the ieh security, therefore R=EX, Ri where R is independent of X,. Since X:20 for all and XX:=1, R is a weighted average of R; with the X, as non-nega- tive weights. To maximize R, we let X= 1 for i with maximum R sever K are maximum then any allocation with ∑xa=1 maximizes R. In no case is a diversified portfolio preferred to all non portfolios. 4 It will be convenient at this point to consider a static model. In stead of speaking of the time series of returns from the ik security ) we will speak of“ the fow of returns”(t)from the i security. The flow of returns from the portfolio as a whole is 3. The results dey n the assumption that the anticipated returns and discount rates are independent of the particular investors portfo 4. If short sales were allowed, an infinite amount of money would be placed in the
78 The Journal of Finance The foregoing rule fails to imply diversification no matter how the anticipated returns are formed; whether the same or different discount rates are used for different securities; no matter how these discount rates are decided upon or how they vary over time.3 The hypothesis implies that the investor places all his funds in the security with the greatest discounted value. If two or more securities have the same value, then any of these or any combination of these is as good as any other. We can see this analytically: suppose there are N securities; let ritbe the anticipated return (however decided upon) at time t per dollar invested in security i; let djt be the rate at which the return on the ilk security at time t is discounted back to the present; let Xi be the relative amount invested in security i . We exclude short sales, thus Xi 2 0 for all i. Then the discounted anticipated return of the portfolio is Ri = x m di, Tit is the discounted return of the ithsecurity, therefore t-1 R = ZXiRi where Ri is independent of Xi. Since Xi 2 0 for all i and ZXi = 1, R is a weighted average of Ri with the Xi as non-negative weights. To maximize R, we let Xi = 1 for i with maximum Ri. If several Ra,, a = 1, .. . ,K are maximum then any allocation with maximizes R. In no case is a diversified portfolio preferred to all nondiversified poitfolios. It will be convenient at this point to consider a static model. Instead of speaking of the time series of returns from the ithsecurity (ril, ri2) . . . ,rit, . . .) we will speak of "the flow of returns" (ri) from the ithsecurity. The flow of returns from the portfolio as a whole is 3. The results depend on the assumption that the anticipated returns and discount rates are independent of the particular investor's portfolio. 4. If short sales were allowed, an infinite amount of money would be placed in the security with highest r
Portfolio Selection R=EX/. As in the dynamic case if the investor wished to maximize anticipated"return from the portfolio he would place all his funds in that security with maximum anticipated returns There is a rule which implies both that the investor should diversify and that he should maximize expected return. The rule states that the investor does(or should) diversify his funds among all those securities which give maximum expected return. The law of large numbers will insure that the actual yield of the portfolio will be almost the same as the expected yield. This rule is a special case of the expected returns- variance of returns rule(to be presented below). It assumes that there is a portfolio which gives both maximum expected return and minimum ariance, and it commends this portfolio to the investor. This presumption, that the law of large numbers applies to a port folio of securities, cannot be accepted. The returns from securities are too intercorrelated. Diversification cannot eliminate all variance The portfolio with maximum expected return is not necessarily the one with minimum variance. There is a rate at which the investor can gain expected return by taking on variance, or reduce variance by giv- ing up expected return. We saw that the expected returns or anticipated returns rule is adequate. Let us now consider the expected returns--variance of re- turns(E-v)rule It will be necessary to first present a few elementary concepts and results of mathematical statistics. We will then show some implications of the E-V rule. After this we will discuss its plausi- pility. In our presentation we try to avoid complicated mathematical state- ments and proofs. As a consequence a price is paid in terms of rigor and generality. The chief limitations from this source are(1)we do not derive our results analytically for the n-security case; instead,we present them geometrically for the 3 and 4 security cases; (2)we assume static probability beliefs. In a general presentation we must recognize that the probability distribution of yields of the various securities is a function of time. The writer intends to present, in the future, the gen eral, mathematical treatment which removes these limitations. We will need the following elementary concepts and results of nathematical statistics Let y be a random variable i.e., a variable whose value is decided by chance. Suppose, for simplicity of exposition, that r can take on a finite number of values y WN. Let the probability that Y
Portfolio Selection 79 R = ZX,r,. As in the dynamic case if the investor wished to maximize "anticipated" return from the portfolio he would place all his funds in that security with maximum anticipated returns. There is a rule which implies both that the investor should diversify and that he should maximize expected return. The rule states that the investor does (or should) diversify his funds among all those securities which give maximum expected return. The law of large numbers will insure that the actual yield of the portfolio will be almost the same as the expected yield.5 This rule is a special case of the expected returnsvariance of returns rule (to be presented below). It assumes that there is a portfolio which gives both maximum expected return and minimum variance, and it commends this portfolio to the investor. This presumption, that the law of large numbers applies to a portfolio of securities, cannot be accepted. The returns from securities are too intercorrelated. Diversification cannot eliminate all variance. The portfolio with maximum expected return is not necessarily the one with minimum variance. There is a rate at which the investor can gain expected return by taking on variance, or reduce variance by giving up expected return. We saw that the expected returns or anticipated returns rule is inadequate. Let us now consider the expected returns-variance of returns (E-V) rule. It will be necessary to first present a few elementary concepts and results of mathematical statistics. We will then show some implications of the E-V rule. After this we will discuss its plausibility. In our presentation we try to avoid complicated mathematical statements and proofs. As a consequence a price is paid in terms of rigor and generality. The chief limitations from this source are (1) we do not derive our results analytically for the n-security case; instead, we present them geometrically for the 3 and 4 security cases; (2) we assume static probability beliefs. In a general presentation we must recognize that the probability distribution of yields of the various securities is a function of time. The writer intends to present, in the future, the general, mathematical treatment which removes these limitations. We will need the following elementary concepts and results of mathematical statistics: Let Y be a random variable, i.e., a variable whose value is decided by chance. Suppose, for simplicity of exposition, that Y can take on a finite number of values yl, yz, . . . ,y,~. Let the probability that Y = 5. U'illiams, op. cit., pp. 68, 69
The journal of f yi, be pi; that Y =22 be Pa etc. The expected value(or defined to be 中1y1+P2y+,十PNyN The variance of y is defined to be 九1(y1-E)2+2(y2-E)2十,十(yx-E) V is the average squared deviation of Y from its expected commonly used measure of dispersion. Other measures of dispersion closely related to v are the standard deviation, o=vv and the co- uppose we have a number r of random variables: R R. if r is a weighted sum (linear combination) of the R R=a1R1十a2R2+.,,+anR then R is also a random variable. (For example Ri, may be the number which turns up on one die; Ro, that of another die, and r the sum of these numbers. In this case n= 2, a1= a2= 1) It will be important for us to know how the expected value and riance of the weighted sum(R)are related to the probability dis- tribution of the ri,..., Ro. We state these relations below; we refer the reader to any standard text for proof.6 The expected value of a weighted sum is the weighted sum of the expected values. I. e, E(R)=a,E(R1)+agE(Ro)+..+ anE(Rn) The variance of a weighted sum is not as simple. To express it we must define "covariance. The covariance of R, and r, is d1=E{[R1-E(R1)]R2-E(R2)] i.e. the expected value of [(the deviation of Ri from its mean) times (the deviation of R2 from its mean). In general we define the covari ance between Ri and r, as σ;=E{[R;-E(R)][R;-ER)] oi may be expressed in terms of the familiar correlation coefficient (p The covariance between R, and R, is equal to [(their correlation) times(the standard deviation of R, times(the standard deviation of m1影:Jpax8 ca Prooa03l4y(New Yor
80 The Journal of Finance yl, be pl; that Y = y2 be pz etc. The expected value (or mean) of Y is defined to be The variance of Y is defined to be V is the average squared deviation of Y from its expected value. V is a commonly used measure of dispersion. Other measures of dispersion, closely related to V are the standard deviation, u = .\/V and the coefficient of variation, a/E. Suppose we have a number of random variables: R1, . . . ,R,. If R is a weighted sum (linear combination) of the Ri then R is also a random variable. (For example R1, may be the number which turns up on one die; R2, that of another die, and R the sum of these numbers. In this case n = 2, a1 = a2 = 1). It will be important for us to know how the expected value and variance of the weighted sum (R) are related to the probability distribution of the R1, . . . ,R,. We state these relations below; we refer the reader to any standard text for proof.6 The expected value of a weighted sum is the weighted sum of the expected values. I.e., E(R) = alE(R1) +aZE(R2) + . . . + a,E(R,) The variance of a weighted sum is not as simple. To express it we must define "covariance." The covariance of R1 and Rz is i.e., the expected value of [(the deviation of R1 from its mean) times (the deviation of R2 from its mean)]. In general we define the covariance between Ri and R as ~ij =E ( [Ri-E (Ri) I [Ri-E (Rj)I f uij may be expressed in terms of the familiar correlation coefficient (pij). The covariance between Ri and Rj is equal to [(their correlation) times (the standard deviation of Ri) times (the standard deviation of Rj)l: Uij = PijUiUj 6. E.g.,J. V. Uspensky, Introduction to mathematical Probability (New York: McGrawHill, 1937), chapter 9, pp. 161-81
Portfolio Selection The variance of a weighted sum is a;V(X)+2 ∑ If we use the fact that the variance of R is out then Let R, be the return on the i"security. Let u: be the expected value of R; oi, be the covariance between R and r,(thus ou is the variance of R.). Let X be the percentage of the investor,s assets which are al located to the i"security. The yield (R)on the portfolio as a whole is R=∑RX The R(and consequently R)are considered to be random variables The Xi are not random variables, but are fixed by the investor. Since the X: are percentages we have >:=1. In our analysis we will ex clude negative values of the X (i.e, short sales); therefore X:>0for all讠. The return(R)on the portfolio as a whole is a weighted sum of ran dom variables(where the investor can choose the weights). From our discussion of such weighted sums we see that the expected return E from the portfolio as a whole is E and the variance is σ;XX concerning these variables. In general we would expect that the he had probability beliefs atters, he would possess a system of probability beliefs We cannot expect the nt matters that have been carefully considered he will base his actions upon these probability beliefs--even though the This paper does not consider the difficult question of how investors do( or should) form
Portfolio Selection The variance of a weighted sum is If we use the fact that the variance of Ri is uii then Let Ri be the return on the iN"security. Let pi be the expected vaIue of Ri; uij, be the covariance between Ri and Rj (thus uii is the variance of Ri). Let Xi be the percentage of the investor's assets which are allocated to the ithsecurity. The yield (R) on the portfolio as a whole is The Ri (and consequently R) are considered to be random variables.' The Xi are not random variables, but are fixed by the investor. Since the Xi are percentages we have ZXi = 1. In our analysis we will exclude negative values of the Xi (i.e., short sales); therefore Xi > 0 for all i. The return (R) on the portfolio as a whole is a weighted sum of random variables (where the investor can choose the weights). From our discussion of such weighted sums we see that the expected return E from the portfolio as a whole is and the variance is 7. I.e., we assume that the investor does (and should) act as if he had probability beliefs concerning these variables. In general we ~vould expect that the investor could tell us, for any two events (A and B), whether he personally considered A more likely than B, B more likely than A, or both equally likely. If the investor were consistent in his opinions on such matters, he would possess a system of probability beliefs. We cannot expect the investor to be consistent in every detail. We can, however, expect his probability beliefs to be roughly consistent on important matters that have been carefully considered. We should also expect that he will base his actions upon these probability beliefs-even though they be in part subjective. This paper does not consider the difficult question of how investors do (or should) form their probability beliefs
The journal of finance For fixed probability beliefs(μ,σ动 the investor has a choice of vari ous combinations of E and V depending on his choice of portfolio X XN. Suppose that the set of all obtainable(E, v) combina tions were as in Figure 1. The e-v rule states that the investor would or should )want to select one of those portfolios which give rise to the (e, V) combinations indicated as efficient in the figure; i.e., those with minimum V for given E or more and maximum E for given V or less. There are techniques by which we can compute the set of efficient portfolios and efficient(E, v) combinations associated with given u E,V combinations officient FIG. 1 and oi. We will not present these techniques here We will,however, illustrate geometrically the nature of the efficient surfaces for cases in which N(the number of available securities) is small. The calculation of efficient surfaces might possibly be of practi se. Perhaps there are ways, by combining statistical techniques and the judgment of experts, to form reasonable probability beliefs a. We could use these beliefs to compute the attainable efficient combinations of(E, v). The investor, being informed of what(E, n) combinations were attainable, could state which he desired. We could hen find the portfolio which gave this desired combination
82 The Journal of Finance For fixed probability beliefs (pi, oij) the investor has a choice of various combinations of E and V depending on his choice of portfolio XI, . . . ,XN.Suppose that the set of all obtainable (E, V) combinations were as in Figure 1.The E-V rule states that the investor would (or should) want to select one of those portfolios which give rise to the (E, V) combinations indicated as efficient in the figure; i.e., those with minimum V for given E or more and maximum E for given V or less. There are techniques by which we can compute the set of efficient portfolios and efficient (E, V) combinations associated with given pi attainable E, V combinations and oij. We will not present these techniques here. We will, however, illustrate geometrically the nature of the efficient surfaces for cases in which N (the number of available securities) is small. The calculation of efficient surfaces might possibly be of practical use. Perhaps there are ways, by combining statistical techniques and the judgment of experts, to form reasonable probability beliefs (pi, aij).We could use these beliefs to compute the attainable efficient combinations of (E, V). The investor, being informed of what (E, V) combinations were attainable, could state which he desired. We could then find the portfolio which gave this desired combination
Portfolio selection Two conditions--at least--must be satisfied before it would be prac- tical to use efficient surfaces in the manner described above First, the investor must desire to act according to the e-v maxim. Second, we must be able to arrive at reasonable ui and oi. we will return to these matters later Let us consider the case of three securities. In the three security case our model reduces to 1)E x X;=1 4)X;≥0fo et 3′)X3=1-X If we substitute(3) in equation(1)and(2)we get E and V as functions of X1 and X2. For example we find 1)E=H3+X1(41-43)+X2(2-3) The exact formulas are not too important here(that of V is given be- low).We can simply write a) E=E(X1, X2) b) V=V(X1, X2) c)X1>0,X2≥0,1-X1-X2≥0 By using relations (a),(b),(c), we can work with two dimensional geometry The attainable set of portfolios consists of all portfolios which satisfy constraints(c)and(3)(or equivalently(3)and (4)). The at tainable combinations of X1, X2 are represented by the triangle abc in Figure 2. Any point to the left of the X2 axis is not attainable because it violates the condition that X1> 0. Any point below the X1 axis is not attainable because it violates the condition that X2>0. Any 2x on xi o+2x ti a 2 *a x+ 2o3a+ oa)+2x1 xa(0ua-0na-oa t oa
Portfolio Selection 83 Two conditions-at least-must be satisfied before it would be practical to use efficient surfaces in the manner described above. First, the investor must desire to act according to the E-V maxim. Second, we must be able to arrive at reasonable pi and uij. We will return to these matters later. Let us consider the case of three securities. In the three security case our model reduces to 4) Xi>O for i=l,2,3. From (3) we get 3') Xs= 1-XI--Xz Ifwe substitute (3') in equation (1)and (2) we get E and V as functions of X1 and Xz. For example we find 1') E' =~3 +x1(111 -~ 3 +) x2 (112 - 113) The exact formulas are not too important here (that of V is given below).8 We can simply write a) E =E (XI, Xd b) V = V (Xi, Xz) By using relations (a), (b), (c), we can work with two dimensional geometry. The attainable set of portfolios consists of all portfolios which satisfy constraints (c) and (3') (or equivalently (3) and (4)). The attainable combinations of XI, X2 are represented by the triangle abc in Figure 2. Any point to the left of the Xz axis is not attainable because it violates the condition that X1 3 0. Any point below the X1 axis is not attainable because it violates the condition that Xz 3 0. Any
The Journal of finance t above the line(1-Xi-X2=0) is not attainable because it violates the condition that X3=1-X1-X2>0. We define an isomean curve to be the set of all points (portfolios) with a given expected return. Similarly an isovariance line is defined to be the set of all points (portfolios)with a given variance of return. An examination of the formulae for e and v tells us the shapes of the isomean and isovariance curves. Specifically they tell us that typically the isomean curves are a system of parallel straight lines; the isovari- ance curves are a system of concentric ellipses(see Fig. 2). For example if u2* u3 equation 1 can be written in the familiar form X2=a+ bX1; specifically(1) E Thus the slope of the isomean line associated with e Eo is-(u1 u3)/(u2-43)its intercept is(Eo-43/(u2 -u3). If we change E we change the intercept but not the slope of the isomean line. This con firms the contention that the isomean lines form a system of parallel Similarly, by a somewhat less simple application of analytic geome try, we can confirm the contention that the isovariance lines form a family of concentric ellipses. The "center"of the system is the point which minimizes V. We will label this point X. Its expected return and variance we will label E and V Variance increases as you move away from X. More precisely, if one isovariance curve, Cl, lies closer to X than another, C2, then Cu is associated with a smaller variance than C2 With the aid of the foregoing geometric apparatus let us seek the efficient sets. X, the center of the system of isovariance ellipses, may fall either inside or outside the attainable set Figure 4 illustrates a case in which X falls inside the attainable set In this case: X is efficient For no other portfolio has a V as low as X; therefore no portfolio can have either ller V(with the same or greater E)or greater E with the same or smaller V. No point (portfolio) with expected return E less than E is efficient. For we have e> e and v< v Consider all points with a given expected return E; i.e., all points on the isomean line associated with E. The point of the isomean line at which V takes on its least value is the point at which the isomean line 9. The isomean“curv e as described above except when u u= ua. In the latter case all portfolios have the same expected return and the invest l s to the assumptions implicit in our description of the isovariance curves see footnote
84 The Journal of Finance point above the line (1 -X1 - Xz = 0) is not attainable because it violates the condition that X3 = 1 -XI -Xz > 0. We define an isomean curve to be the set of all points (portfolios) with a given expected return. Similarly an isovariance line is defined to be the set of all points (portfolios) with a given variance of return. An examination of the formulae for E and V tells us the shapes of the isomean and isovariance curves. Specifically they tell us that typicallyg the isomean curves are a system of parallel straight lines; the isovariance curves are a system of concentric ellipses (see Fig. 2). For example, if ~2 p3 equation 1' can be written in the familiar form X2 = a + bX1; specifically (1) Thus the slope of the isomean line associated with E = Eois -(pl - j~3)/(.~2- p3) its intercept is (Eo - p3)/(p2 - p3). If we change E we change the intercept but not the slope of the isomean line. This confirms the contention that the isomean lines form a system of parallel lines. Similarly, by a somewhat less simple application of analytic geometry, we can confirm the contention that the isovariance lines form a family of concentric ellipses. The "center" of the system is the point which minimizes V. We will label this point X. Its expected return and variance we will label E and V. Variance increases as you move away from X. More precisely, if one isovariance curve, C1, lies closer to X than another, Cz, then C1 is associated with a smaller variance than Cz. With the aid of the foregoing geometric apparatus let us seek the efficient sets. X, the center of the system of isovariance ellipses, may fall either inside or outside the attainable set. Figure 4 illustrates a case in which Xfalls inside the attainable set. In this case: Xis efficient. For no other portfolio has a V as low as X; therefore no portfolio can have either smaller V (with the same or greater E) or greater E with the same or smaller V. No point (portfolio) with expected return E less than E is efficient. For we have E > E and V < V. Consider all points with a given expected return E; i.e., all points on the isomean line associated with E. The point of the isomean line at which V takes on its least value is the point at which the isomean line 9. The isomean "curves" are as described above except when = pz = pa In the latter case all portfolios have the same expected return and the investor chooses the one with minimum variance. As to the assumptions implicit in our description of the isovariance curves see footnote 12
is tangent to an isovariance curve. We call this point X (E). If we let E vary, X(E traces out a curve Algebraic considerations(which we omit here) show us that this cu a straight line. We will call it the critical line Z. The critical line passes through X for this point minimizes V for all points with E(X1, X2)=E As we go along Z in either direction from X, v increases. The segment of the critical line from X to the point where the critical line crosses direction of increasing E the boundary of the attainable set is part of the efficient set. The rest of the efficient set is (in the case illustrated) the segment of the ab line from d to b b is the point of maximum attainable E. In Figure 3, X lies outside the admissible area but the critical line cuts the admissible area. The efficient line begins at the attainable point with minimum variance(in this case on the ab line). It moves toward b until it inter- sects the critical line, moves along the critical line until it intersects a boundary and finally moves along the boundary to b. The reader may
Portfolio Selection 85 A is tangent to an isovariance curve. We call this point X(E). If we let h E vary, X(E) traces out a curve. Algebraic considerations (which we omit here) show us that this curve is a straight line. We will call it the critical line I. The critical line passes through X for this point minimizes V for all points with E(X1, Xz) = E. As we go along l in either direction from X, V increases. The segment of the critical line from X to the point where the critical line crosses *direction of increasing E depends on p,. p:. p3 FIG.2 the boundary of the attainable set is part of the efficient set. The rest of the efficient set is (in the case illustrated) the segment of the 3 line from d to b. b is the point of maximum attainable E. In Figure 3, X lies outside the admissible area but the critical line cuts the admissible area. The efficient line begins at the attainable point with minimum variance (in this case on the Z line). It moves toward b until it intersects the critical line, moves along the critical line until it intersects a boundary and finally moves along the boundary to b. The reader may
