Market Segmentation: Evidence on the Individual Investor

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    Market Segmentation: Evidence on the Individual InvestorAuthor(s): Ronald C. Lease, Wilbur G. Lewellen and Gary G. SchlarbaumSource: Financial Analysts Journal, Vol. 32, No. 5 (Sep. - Oct., 1976), pp. 53-60Published by: CFA InstituteStable URL: .Accessed: 16/06/2014 23:51

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  • by Ronald C. Lease, Wilbur G. Lewellen and Gary G. Schlarbaum

    Market Segmentation:

    Evidence on the Individual Investor

    4 Is the market for securities segmented, in the sense that different groups of investors concentrate on different groups of assets? According to the authors' examination of the demographic back- grounds, investment attitudes, and portfolio compo- sitions of a retai I broker's clients, it is.

    Respondents to the authors' questionnaire fell neatly into five demographic groups. When these groups were rated on their investment goals, the kind of information they used, and the number and kind of assets in their portfolios, a definite pattern emerged. In general, the older investor was more conservative in his investment behavior, placed less emphasis on short-term capital gains and more on dividend income, relied less on broker advice, spent more time on security analysis, and had a more di- versified portfolio containing fewer high-risk assets. The portfolios of older females were especially con- servative, diversified, and dividend-oriented.

    The respondents' brokerage transactions over the period 1964 to 1970 revealed that the composi- tions of the portfolios produced by those trades varied significantly across the five groups. Groups I (retired males), IV (older females) and V (unmarried professional and managerial persons) all held corporate securities, but Group I especially emphasized savings accounts and fixed income se- curities. Groups 11 (older employed males) and III (highly educated young professionals) were strongly invested in real estate and their own busi- nesses. More than any other, Group 11 emphasized life insurance protection.

    This evidence of market fragmentation suggests that purveyors of financial services have much to gain by being selective in their appeals to various classes of customers. When a retired male walks through the door of a brokerage office, the account executive can predict with a fair degree of

    confidence the kind of investment products that are apt to strike a responsive chord. >

    UCH of contemporary capital market theo- M ry assumes that the participants in the

    marketplace are homogenous- in the nature of their search for information, in their diges- tion of that information,, and in their consequent se- curities trading patterns. To the extent, however, that the markets for particular assets are segmented, there may be impediments to the free flow of capital that interfere with the establishment of a predictable, coherent risk-return relationship among all classes of securities. We shall offer evidence on the invest- ment behavior of the individual investor that, in general, supports the notion that segmentation does indeed exist and discuss its implications for market theory, the marketing of financial services, and the future demand for various financial assets.

    The authors are, respectively, Assistant Professor of Finance at the University of Utah, Professor of In- dustrial Administration at Purdue University, and Associate Professor of Industrial Management at Purdue. Financial support was provided by the Na- tional Bureau of Economic Research, the Investment Company Institute, the University of Utah Research Fund, the Purdue Research Foundation, the broker- age house referred to in the text, the College of Busi- ness of the University of Utah, and the Krannert Graduate School at Purdue. The authors thank Pro- fessors Frank Bass, Donald King, and Edgar Pesse- mier of Purdue, Professor Ramon Johnson of Utah, and William Albring of Purdue for their con- tribution s.

    While the article comprises a portion of a larger NBER project, it should not be considered an official NBER publication.


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  • The literature suggests that market segmenta- tion-if it exists-can compromise the applicabili- ty of our standard risk-return pricing prescrip- tions'. Moreover, there is reason to believe that it may exist, because of both legal and institutional constraints and differences in investor tastes and expectations. We provide evidence on the latter point, concentrating explicitly on individual in- vestor participation in the market. We find that in- dividuals do appear to partition themselves into distinct groups in terms of investment strategies, objectives, information sources, and asset selection behavior and that prior treatments of the segmen- tation issue-most of which have dealt with insti- tutional investing-may, if anything, have under- estimated its magnitude.

    The Data A random sampling of some 2500 customers of a

    large national retail brokerage house provided the data for our analysis. Selected from all the cus- tomers of the firm who had maintained an open ac- count with it over the full period January 1964 through December 1970, the sample was stratified to match the geographical distribution of the gen- eral U.S. shareholder population as reported by the

    New York Stock Exchange.2 While we restricted it to individual investors-corporate, institutional and investment club accounts being excluded from consideration-no criteria of trading volume or portfolio size were imposed for eligibility. Conse- quently, the sample spans a wide range of individ- ual investment circumstances and styles.

    A questionnaire sent to these individuals in mid- 1972 requested information on demographic at- tributes as well as on a variety of aspects of invest- ment attitudes, objectives, decision processes, and portfolio compositions. They returned just under 1000 usable completed forms. These data were matched with the complete record from the brokerage firm's files of transaction activity in each account between 1964 and 1970. The latter in- cluded statistics on trading frequency, trading volume, number of different securities traded, and percentage breakdowns by particular transaction types-e.g., cash vs. margin trades, short vs. long positions, round vs. odd-lot transactions. The result is an unusually comprehensive picture of both the circumstances and market participation profiles of each individual surveyed. Table 1 por- trays certain key dimensions for the sample as a whole. Since prior analysis has shown that, in terms of demographic characteristics, the sample is highly representative of the mass of American 1. Footnotes appear at end of article.

    TABLE 1: Characteristics of the Investor Sample Age: Occupation:

    Under 21 ...... < 1% Professional/Technical .................. 27% 21-34 ...... 3% Manager/Proprietor .................. 29% 35-44 ...... 12% Clerical/Service .................. 7% 45-54 ...... 29% Craftsman/Laborer .................. 3% 55-64 ...... 26% Farm Owner/Farm Laborer .................. 2% 65 and over ....... 30% Not Employed ................... 32%

    Sex: Total Asset Holdings:

    Male...........80% Under $100,000 . 27% Female .2 0% $100,000-199,999 ........... 30%

    $200,000-299,999 ........... 15% Family Income: $300,000-399,999 . 8%

    $400,000 and over . 20% Under $5,000 ......................... 2% $5,000-9,999 .......................... 8% Common Stock Holdings: $10,000-14,999 ........... 15% Under $50,000 ............ 51% $15,000-19,999 ........... 13% $50,000-99,999 ............ 18% $20,000-24,999 ........... 18% $100,000-149,999 ............ 10% $25,000-49,999 ........... 26% $150,000-199,999 ............ 7% $50,000 and over ........... 18% $200,000 and over ............ 14%

    Education: Annual Trading Volume:

    Less than H.S ........... 11% Under $5,000 ........... 29% H.S. Graduate ........... 12% $5,000-9,999 ........... 18% Some College ........... 23% $10,000-14,999 ........... 11% BA/BS ........... 31% $15,000-24,999 ........... 12% Graduate Degree ........... 23% $25,000 and over ........... 30%


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  • shareholders, it seems reasonable to extrapolate its observed behavior patterns to that larger popula- tion.3 Partitioning the Sample

    A standard cluster-analysis procedure sorted the sample along self-defined "natural" demographic boundary lines into five cohesive groups having significantly different inter-group personal-circum- stance characteristics. Cluster analysis is used par- ticularly in situations where criteria for group membership are not easily specified in advance. While, in the present sample, it may have been possible to define some arbitrary boundaries-e.g., investor age brackets or income levels-in ad- dition to being arbitrary they would have prejudged the nature of the possible interactions among the full array of personal-circumstance at- tributes that might define commonality. Hence, we elected to let the sample, in effect, sort itself out in its own terms.

    For this purpose, we used the Howard-Harris Clustering Algorithm.4 The input demographic variables recorded for each of the 972 individuals in the sample who returned a completed question- naire were age, sex, marital status, annual family income, occupation, employer type, educational attainment, and family size. (All were standardized to remove the effect of variable scale differences.)

    The algorithm divides a sample successively into groups in such a way that, at each stage, the ratio of the within-group to total-sample sum-of-squares across all the independent variables is minimized. This process can continue until, eventually, as many groups are created as there are subjects-or, more sensibly, until further division provides little additional classificatory benefit, as judged by the sum-of-squares criterion. In the case at hand, the improvement became miniscule after the fifth division and the analysis was terminated.5

    Table 2 portrays the demographic characteristics of the five resulting groups: Group I is comprised almost entirely of retired male investors and Group II of relatively old-but still employed-males with a heavy representation of proprietorship and partnership occupational activities. Group III con- sists predominantly of younger professional men with substantial educational backgrounds. Group IV is exclusively female, many of its members past retirement age and few actively employed. Finally, Group V is made up completely of unmarried indi- viduals, relatively young by the standards of the sample, and engaged generally in professional and managerial job responsibilities. The associated hierarchy of income levels and family sizes seems consistent with these broad inter-group character- izations.6

    TABLE 2: Individual Investor Sample Demographic Groupings: Clusters Based on Standardized Demographic Variables

    Group I II IlIl IV V

    Group Size 195 289 247 140 101 Sex 100% 97% 96% 100% 69%

    Male Male Male Female Male Mean Age 69 yrs. 60 yrs. 45 yrs. 63 yrs. 51 yrs. Per cent Married 87% 100% 99% 58% 0% Employer:

    Business Firm 3% 83% 87% 9% 72% Non-Profit Organization 1% 5% 7% 4% 14% Government 3% 7% 3% 1% 9% Not Employed 93% 5% 3% 86% 5%

    Occupation: Professional/Technical 0% 33% 47% 4% 49% Managerial 0% 27% 23% 1% 21% Proprietor/Partner 2% 25% 15% 1% 9% Sales/Service 0% 6% 11% 0% 7% Housewife 0% 0% 1% 46% 1% Retired 87% 1% 0% 32% 1% Other 11% 8% 3% 14% 12%

    Mean Family Size 2.1 2.4 4.8 1.9 1.4 Education:

    High School or Less 35% 24% 6% 34% 19% Some College 24% 32% 13% 21% 21% Bachelor's Degree 26% 24% 41% 34% 35% Advanced Degree 15% 20% 40% 11% 25%

    Mean Family Income $20,900 $28,300 $33,400 $20,900 $21,500


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  • Segmentation in Investment Strategies Do these groups differ sufficiently in their in-

    vestment behavior to suggest that they may occupy distinctly different segments of the securities mar- kets as well? Table 3 summarizes the outcome of a multiple discriminant analysis of group differences, where the independent variables are a collection of investment "strategy" descriptors taken from the responses on the returned questionnaires.

    The questionnaire required the respondent to rate, on a scale of one to four (where four denoted a "very important" goal), short-term capital gains, long-term capital appreciation, and dividend in- come as portfolio objectives. It asked him to iden- tify his primary approach to security selection, to indicate the amount of time and money spent in collecting and analyzing investment information (monthly and yearly, respectively), and to estimate the (perceived) proportionate representation in the portfolio of securities chosen primarily for their dividend-income-generating attributes. As a crude measure of diversification, the number of different companies' securities held was solicited. Finally, it asked if mutual fund shares were part of his port- folio, and whether certain "exotic" investment vehicles-margin purchases, short sales, etc. -were commonly exploited. The list, therefore, covers most of the major dimensions of strategy and tactics.

    The multiple discriminant analysis produces a function (called the discriminant function) that predicts into which group an investor will fall, depending on the values of his investment strategy descriptors. In this case, the discriminant function is sufficiently successful that it correctly classifies 43 per cent of the sample into the appropriate groups (in contrast with the 30 per cent that would be achieved by the best "naive" decision rule-i.e., simply assigning all subjects to the group having the largest single membership). A comparably suc- cessful sort would be accomplished by chance only five times in 100. Accordingly, we say that the dis- criminant function is significant at the 0.05 level, and conclude that substantial inter-group in- vestment strategy variations do in fact prevail. Fur- ther, the differences across the listed groupings of the means of 12 of the 16 separate variables em- ployed are themselves significant when judged by the same 0.05 level criterion.

    There are sizable differences among the five in- vestor groups in their reported investment goals, in the quantity and type of information they process, and in the number and character of the instruments that ultimately comprise their portfolios. The general pattern is one of increasing conservatism in investment behavior, and more self-reliance in decision-making, the older the investor. As one proceeds across the age spectrum from youngest

    TABLE 3: Multiple Discriminant Analysis on Investment Strategy Dimensions

    Univariate Variable Mean, For Group

    Strategy Variable F I II IlIl IV V

    Investment Goal Rating: Short Term 17.63* 1.53 1.86 2.19 1.50 2.00 Long Term 2.57* 3.45 3.63 3.61 3.46 3.54 Dividends 84.55* 3.39 2.46 2.04 3.36 2.30

    Investment Approach: Fundamental 3.65* 51% 44% 42% 34% 33% Technical 1.99 3% 4% 4% 3% 9% Fund./Tech. Combined 0.44 22% 24% 23% 19% 26% Broker Advice 6.75* 14% 15% 21% 34% 25%

    Hours/Month Spent on Security Analysis 9.38* 12.2 9.7 7.1 6.6 8.8

    $/Year Spent on Information Collection 1.85 $81 $73 $75 $49 $68

    % of Portfolio in Income Securities 44.72* 56% 39% 27% 57% 34%

    Number of Securities in Portfolio 7.94* 12.1 11.6 9.4 12.1 10.4

    Own Mutual Fund Shares 2.85* 38% 41% 51% 48% 37% Have Used or Invested in:

    Margin 8.07* 42% 44% 60% 34% 45% Options/Warrants 3.04* 41% 48% 53% 38% 42% Short Sales 5.56* 20% 23% 32% 11% 24% Convertibles 0.44 37% 35% 36% 32% 31%

    *Univariate significant at 0.05 level; critical F (0.05,4,967) = 2.37. Overall F for discriminant function = 7.55; critical F (0.05,64,3729) = 1.30. Per cent correct group classification = 43%.


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  • (Group III) to oldest (Group I), short-term capital gains diminish in proclaimed importance, more emphasis is placed on dividend income, reliance on broker advice falls, more time and money are spent on security analysis, the portfolio becomes more diversified, and the use of high-risk investment vehicles declines. Moreover, the female investor group (IV) seems especially conservative, diver- sified, and dividend-oriented. While obviously this profile does not, in itself, prove conclusively that these groups occupy distinct segments of the securities marketplace, it displays sharp differences in asserted investment posture.

    Segmentation in Trading Patterns The brokerage house's transactions records over

    the period 1964 through 1970 offered more direct evidence. Those data permitted compilation of eight summary statistics for each account: the num- ber of trades executed during the seven-year in- terval, the average dollar size of those transactions, the number of different firms' securities traded, and the percentages of the total observed trading volumes that were- respectively- margin pur- chases, short sales, in stocks listed on the American Exchange, in OTC and regional-exchange securities, and "solicited" transactions. The com- plementary percentages-cash, NYSE, and un- solicited trades-were omitted since they are linearly dependent on those specified but are, of course, implicitly defined. The designation "solicited" refers to transactions undertaken on the recommendation of the account executive in-

    volved, rather than initiated by the customer; each such trade in the file was thus tagged.

    Table 4 shows the effect of adding these eight variables to the analysis of investor group dif- ferences. The overall discriminant function remains highly significant, its ability to classify the sample subjects correctly increases, and five of the eight new variables are significantly different across the groups. Expressed differences in in- vestment intentions, therefore, do seem to be reflected in differences in (and concentrations of) actual trading behavior-reinforcing the likeli- hood that self-imposed barriers to capital move- ment are effectively present within the individual- investor sector of the marketplace.

    Segmentation in Portfolio Composition An examination of the investment portfolios

    produced by those trading patterns provides fur- ther reinforcement. The questionnaire asked each respondent to list, to the nearest $100 of market value, his current holdings in 15 separate categories of assets. The resulting portfolio com- position percentages for each individual comprise the final set of independent variables employed in our assessment of investor differences.

    Their inclusion in the multiple discriminant ana- lysis with the "strategy" and "transactions" variables described above improves the classifica- tion record of the discriminant function perfor- mance to 58 per cent, with 30 per cent still the best "naive" standard (see Table 5). Several of the smaller asset classes-checking accounts, warrants

    TABLE 4: Multiple Discriminant Analysis of Investor Trading Patterns Univariate Variable Mean, For Group

    Trading Dimension F I II III IV V

    Number of Security Trades, 1964-70 1.12 95 74 61 61 80

    Average Dollar Amount Per Transaction (x103) 4.97* $3.3 $2.7 $2.6 $3.3 $2.5

    Number of Different Securities Traded 1.24 28 29 22 23 26

    Margin Volume Percentage 2.37* 21% 25% 28% 24% 17%

    Short Sale Volume Percentage 2.58* 1% 1% 2% 1% 3%

    ASE Volume Percentage 3.73* 12% 15% 17% 11% 14%

    OTC/Regional Volume Percentage 1.47 20% 21% 23% 17% 23%

    Solicited Volume Percentage 6.16* 27% 28% 35% 37% 25%

    *Univariate significant at 0.05 level; critical F (0.05,4,967) = 2.37.

    Overall F for discriminant function (including strategy variables in Table 3) = 5.79; critical F (0.05,96,3742) = 1.28.

    Per cent correct group classification = 46%.


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  • and options, commodity futures, and miscellaneous assets-were omitted to avoid the problem of variable interdependency. (Together, these ac- counted for just three per cent of the sample's total assets.) Life insurance investments were requested on the questionnaire in the form of policy face values rather than cash values, since we felt that the former figure would be an easier one for the respondents to provide.

    Clearly, there are sizable differences in the man- ner in which the five groups assemble their asset portfolios. The mean percentages for all but three of the 11 recorded investment categories vary sig- nificantly across the groups, with the means for most categories varying by at least two to one: common stocks, from 45 to 23 per cent of the respective portfolio totals; savings accounts, from 11 to six per cent; bonds, from seven to two per cent; and so on down the list. Groups I, IV, and V are especially heavily invested in corporate equities; savings accounts and fixed-income securi- ties are further emphasized within Group I. Groups II and III are strong in real estate and own-busi- ness funds commitments, with Group IL standing out additionally in its life insurance protection. A variety of other distinctions can be discerned as well, all of which give evidence of a substantial degree of portfolio specialization-and all of which are suggestive of market segmentation.

    Differences in Investor Attitude The obvious rationale for such differences in

    market participation patterns, of course, would be commensurate differences in the attitudes, tastes, and expectations of the individual investors.7 These, in turn, might logically be expected to be a

    result of systematic variations in personal circum- stance. The fact that our groups display significant differences in investment posture is consistent with this expectation.

    To discern the attitudinal basis of the observed group differences, we included on the question- naire a series of "opinion" requests-some 30 statements of attitude to which the respondent was asked to indicate the extent of his concurrence on a scale of one to five, where a rating of one denoted "strong disagreement" and a rating of five "strong agreement." Further, the subject was asked to estimate the long-run average annual pre-tax rate of return that he believed was attainable from investment in a diversified portfolio of common stocks. Of the 972 respondents to the survey, 711 completed this portion of the questionnaire fully. A multiple discriminant analysis of group differ- ences for those 711, using just the 30 opinion responses as independent variables, was un- dertaken to determine whether the recorded demo- graphic variations across groups in fact found ex- pression in explicit attitude differences as well. Table 6 shows the outcome for the eight opinion elements out of the 30 for which significant inter- group differences could be detected.

    It can be seen from the tabulation that perspec- tives on the market, on the investor's role therein, and on the nature of appropriate portfolio philoso- phies are anything but uniform. The groups differ noticeably in their assessments of the desirability of mutual funds as an investment vehicle; they react quite differently to the assumption of port- folio risk; they have different views of stock price forecasting opportunities; and, between the least and most optimistic of the five groups, the returns

    TABLE 5: Multiple Discriminant Analysis of Investor Portfolio Composition Univariate Variable Mean, For Group

    Portfolio Element F I II IlIl IV V

    Common Stock 14.10* 37% 29% 23% 38% 45% Preferred Stock 1.64 2% 2% 1% 2% 1% Mutual Fund Shares 2.14 5% 3% 3% 4% 3% Government Bonds 3.19* 2% 2% 1% 5% 2% Corporate Bonds 7.12* 5% 4% 1% 4% 2% Savings Accounts 5.49* 11% 8% 6% 9% 11% Personal Residence 18.98* 16% 21% 30% 18% 11% Other Real Estate 1.31 9% 12% 12% 9% 8% Interest in Own Business 14.11* 1% 7% 10% 1% 1% Personal Possessions 4.90* 6% 8% 9% 7% 10% Life Insurance Policy

    Face Value (X103) 27.51 $21 $42 $92 $21 $28

    *Univariate significant at 0.05 level; critical F (0.05,4,583) = 2.37.

    Overall F for discriminant function (including strategy and transactions variables in Tables 3 and 4) = 4.31; critical F (0.05,144,2185) = 1.00.

    Per cent correct group classification = 58%.


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  • they believe can be achieved by managing their own portfolios vary by more than 50 per cent.8 On one hand, the pattern of the mean ratings is in- ternally consistent; on the other, it corresponds with the evidence on proclaimed investment policies and trading behavior. Thus the risk-averse groups also seek diversification; they have modest rate of return expectations; they downgrade short- term speculative trading and emphasize dividend income as an investment goal; and they engage in activities like margin purchases and short sales very infrequently.9 The attitudes expressed, there- fore, line up logically with the behavior observed

    -and both display considerable heterogeneity across the sample.

    Implications While none of these findings represent absolute

    proof of market segmentation, of course, they all argue strongly for that conclusion. Apparently in- vestors do align themselves with particular invest-

    ment philosophies and distinct market segments, and apparently that alignment is systematically related to their individual circumstances. Certainly the success of arbitragers in enforcing uniformity in securities pricing patterns can only be more limited in such an environment.

    Various other tasks, however, become somewhat easier. The marketing literature, for example, views customer segmentation as providing an opportunity rather than posing a problem, in that product sales efforts can be profitably differentiated. Smith de- fines segmentation as "viewing a heterogenous market... as a number of smaller homogenous markets [with] differing product preferences," the suggested response to which is to inform those seg- ments "of the availability of goods or services pro- duced for, or presented as, meeting their needs with precision."10 Engel, Fiorillo, and Cayley similarly hold out substantial benefits from a concept of seg- mentation based on three propositions: (1) con- sumers are different; (2) their differences are

    TABLE 6: Multiple Discriminant Analysis of Investor Attitudes Univariate Mean Agreement Rating, For Group

    Opinion Statement F* I II III IV v

    The individual investor who manages his own portfolio of stocks is likely to fare better financially than the investor who puts his money into mutual funds. 2.37* 3.71 3.67 3.39 3.59 3.57

    Mutual funds' investment policies are not conservative enough. 4.33* 2.92 2.96 2.62 2.79 2.72

    Security prices are not predictable in the short run. 3.90* 4.07 3.90 3.70 4.07 3.69

    The level of RISK - i.e., variability of returns - in my portfolio is substantially lower than for the average investor. 6.04* 3.30 3.17 2.83 3.47 3.21

    The degree of DIVERSIFICATION - i.e., the number and kind of

    different securities held in my portfolio - is substantially more than for the average investor. 5.06* 3.25 3.21 2.81 3.39 3.00

    I prefer to take substantial financial risks to realize significant financial gains from investments. 7.97* 2.33 2.89 2.97 2.34 3.08

    The individual investor tends to be a more important force irn the financial markets than the institutional investor. 3.44* 2.31 2.21 1.87 2.11 2.15

    Annual average return before taxes believed attainable on a regular basis from investments in common stocks. 9.95 * 8.8% 9.6% 11.7% 7.6% 10.6%

    *All univariate significant at 0.05 level; critical F (0.05,4,706) = 2.37.

    Overall F for discriminant function (30 independent variables) = 1.84; critical F (0.05,120,2694) = 1.22.

    Per cent correct group classification = 44%.


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  • reflected in product demand differences; and (3) the consumer groups can, in practice, be success- fully isolated."

    The finding here, therefore, that a relatively short list of standard, objective demographic at- tributes provides significant insight into the likely preferences of individuals for particular financial instruments and investment approaches, suggests a powerful opportunity for purveyors of financial services to be selective and persuasive in their ap- peals to various classes of customers. Simply put, when an individual fitting the description of the members of, say, the "retired male" investor group (I) arrives at the door of a brokerage house, the ac- count executive can predict with a fair degree of confidence the kinds of investment products that are apt to strike a responsive chord. Advertising and promotion campaigns to attract customers can have correspondingly differentiated emphases for different clienteles.

    In the same fashion, one can speculate on the impact of evolving population profiles. Demog- raphers forecast a marked increase, by the year 2000, in the proportion of Americans in the over- 65 age bracket, and a commensurate rise in the mean and median ages of the entire population.'2 If present social currents are any guide, the relative proportion of female workers who will attain sig- nificant job responsibilities, incomes, and thereby sizable investment portfolios will also increase. Since a conservative investment posture and an at- traction for dividend income seem to mark our groups I and IV, it is not inconceivable that prices of low-risk securities will be bid up, on balance, and those of high-risk ones bid down, as these pop- ulation contingents become more significant in the capital marketplace over the next quarter century. A sharper slope to the risk-return relationship across financial assets would be the concomitant result. Crude as this particular scenario may be, the presence of a clear demographic profile in in- vestment strategies and behavior does offer some opportunites for the contemplation of possible changes in the future character of the American se- curities market. .


    1. Lintner suggests that, if various groups of investors envision different optimal portfolios of risky assets and thereby hold different collections of such instruments, the market price of risk may vary among securities. (See "Expectations, Mergers, and Equilibrium in Purely Competitive Securities Markets," American Economic Review (May 1971).) Rubinstein points out that this circumstance would lead to a breakdown of the so-called "separation" property that is central to pre- vailing capital market models ("Corporate Financial Policy in Segmented Securities Markets," Journal of Financial

    and Quantitative Analysis (December 1973)). In the same vein, Friend and Blume have criticized the ability of the "pure" Capital Asset Pricing Model to explain adequately the empirical pattern of differential returns across the spec- trum of financial assets, and attribute part of that deficiency to the likelihood of segmented markets ("Measurement of Portfolio Performance Under Uncertainty," American Eco- nomic Review (September 1970) and "A New Look at the Capital Asset Pricing Model," Journal of Finance (March 1973)). Glenn has addressed the matter of legal restrictions on the portfolio composition opportunities of institutional investors, concluding that those constraints can lead to seg- mentation and a non-uniform risk-return relationship (in "Institutional Portfolio Restrictions, Capital Market Equi- librium, and Corporate Financial Decisions," (Dissertation, Stanford University, 1974)). Rosenberg further examined the concentration of institutional portfolios in the shares of large corporations, and the commensurate disproportionately heavy ownership of smaller firms by individual investors, suggesting a potential for pricing inconsistencies between the two categories of securities ("Institutional Investors: Hold- ings, Prices, and Liquidity," Financial Analysts Journal (March/April 1974)).

    Any tendency toward discriminatory asset pricing resulting from the specialized portfolio emphases of various market groups would induce arbitragers to become active, thereby restoring the "proper" risk-return relationships. Glenn and Blume and Friend, however, show how imperfections in the short-selling mechanism can preclude such a desired result. (See Glenn, "Institutional Portfolio Restrictions" and Blume and Friend, "A New Look at the Capital Asset Pricing Model.")

    2. New York Stock Exchange Fact Book (NYSE, 1971). 3. R. Lease, W. Lewellen, and G. Schlarbaum, "The Individual

    Investor: Attributes and Attitudes," Journal of Finance (May 1974).

    4. N. Howard and B. Harris, A Hierarchical Grouping Routine (Philadelphia: University of Pennsylvania Com- puter Center, 1966).

    5. Decisive evidence of clear-cut inter-group heterogeneity was provided by two tests: a one-way analysis of variance on each demographic attribute involved, and a multiple discriminant analysis using those same attributes as the in- dependent variables. The resulting ANOVA F values ranged from 32.66 to 686.74, all highly significant at well beyond the 0.05 level. Similarly, the derived discriminant function was significant at the 0.05 level, and correctly classified 95 per cent of the sample subjects into the groups defined origi- nally by the clustering procedure.

    6. We presume that the mean family size of 1.4 for Group V reflects a representation of divorced individuals, or widows and widowers, having minor children still in the household. The alternative explanation, of course, merits little elabora- tion here.

    7. Rubinstein. 8. The mean expectation for the sample as a whole was 9.3 per

    cent per annum-a not unreasonable figure, judging by published studies of long-run realized rates of return on common shares (see Friend and Blume, "Measurement of Performance"). In that regard, the securities market milieu in mid-1972 was a fairly "neutral" one. The 1969-70 stock price debacle had been recouped, and the severe monetary, energy, and commodity-price upheavals were yet to come.

    9. For a further discussion of these phenomena, see U.S. Department of Commerce Demographic Projections.

    10. W. Smith, "Product Differentiation and Marketing Segmen- tation as Alternative Marketing Strategies," Journal of Marketing (July 1956).

    11. J. Engel, H. Fiorillo, and M. Cayley, Market Segmenta- tion: Concepts and Applications (New York: Holt, Rine- hart and Winston, 1972).

    12. U.S. Department of Commerce, Bureau of the Census, De- mographic Projections for the United States (Series P-25, Number 470, November 1971 and Series P-25, Number 476, February 1972).


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    Article Contentsp. 53p. 54p. 55p. 56p. 57p. 58p. 59p. 60

    Issue Table of ContentsFinancial Analysts Journal, Vol. 32, No. 5 (Sep. - Oct., 1976), pp. 1-80Front Matter [pp. 1-24]Editor's Comment: Objectives of Financial Reporting [p. 4]Letters to the EditorCorrection: A Financial Analysis of the ESOT [p. 8]Risk and Return in Option Trading Revisited [pp. 8-9]A Matter of Interpretation [p. 10]Offers a Wider Range of Extrapolation [pp. 10+73]

    Securities Law and Regulation: Disclosure of Questionable Foreign Payments [pp. 12-14+74-75]Accounting for Financial Analysis [pp. 16-17+76]Editorial Viewpoint: The Accountant's Job [p. 18]A Conversation with Benjamin Graham [pp. 20-23]Negotiated Commissions and the Structure of the Institutional Brokerage Industry [pp. 25-26]Managing Change in the Modern Investment Institution [pp. 27-34]How Well Do Banks Manage Pooled Pension Portfolios? [pp. 35-40]The Correlation between Market Penetration and Potential Profits [pp. 41-46]Markowitz Revisited [pp. 47-52]Market Segmentation: Evidence on the Individual Investor [pp. 53-60]The Performance of Equity Real Estate Investment Trusts [pp. 61-66]Some Factors Related to the Depreciation Switchback [pp. 67-73]Letters to the EditorResults Confirmed Though Tests Differ [p. 73]

    FAF Newsletter [pp. 77-80]Back Matter


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