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7/27/2019 Patterns of Investment Strategy anInvestment Strategy and Behavior Among Individual Investorsd Behavior Amon

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Patterns of Investment Strategy and Behavior Among Individual InvestorsAuthor(s): Wilbur G. Lewellen, Ronald C. Lease and Gary G. SchlarbaumSource: The Journal of Business, Vol. 50, No. 3 (Jul., 1977), pp. 296-333Published by: The University of Chicago PressStable URL: http://www.jstor.org/stable/2352539 .

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WilburG. Lewellen,RonaldC. Lease,andGaryG. Schlarbaum*Patterns of Investment Strategy and Behavioramong Individual Investors

In recent years, a substantial amount of attention has been directed in theliterature of economics and finance to the question of the investment be-havior and portfolio performanceof institutional investors.' Those investi-gations arenatural ones, both becausethe relevant portfoliodata are reason-ably accessible and because the institutions involved are rapidly growingaccumulatorsand traders of corporateequity securities. The traditional bul-wark of the Americanequity marketplace-the individual investor-how-ever, has beenan object of scrutiny only in very aggregative terms. The trendof his apparent withdrawal rom direct market participation has been amplydocumented,2and the appropriate alarums expressed,3 but the causes ofthat phenomenonremain almost entirely conjectural. Indeed, virtually all

* Respectively, professor of management at Purdue University, associate professorof finance at the University of Utah, and associate professor of management at Purdue.The authors are indebted for financial support for the investigation to the National Bureauof Economic Research, the Investment Company Institute, the Purdue Research Founda-tion, and the brokerage house referred to in the text. The resources of the ComputationCenters at Purdue and the University of Utah have been drawn on extensively in perform-ing the analyses. Professors Frank Bass, Donald King, and Edgar Pessemier of Purdueprovided complementary intellectual and emotional assistance. The authors alone, how-ever, bear responsibility for the end product. More specifically, while the study representsa portion of a National Bureau of Economic Research project, it has not undergone thefull critical review associated with NBER efforts, and it should not be considered an offi-cial NBER publication.1. See, for example, I. Friend, M. Blume, and J. Crockett, Mutual Funds and OtherInstitutional Investors (New York: McGraw-Hill Book Co., 1970); M. Jensen, "The Per-formance of Mutual Funds in the Period 1945-64," Journal of Finance 23 (May 1968): 389-416, and "Risk, the Pricing of Capital Assets, and the Evaluation of Investment Port-folios," Journal of Business 42 (April 1969): 167-247; J. Lorie et al., Measuring the Invest-ment Performance of Pension Funds (Park Ridge, Ill.: Bank Administration Institute,1968); G. Schlarbaum, "The Investment Performance of the Common Stock Portfoliosof Property-Liability Insurance Companies," Journal of Financial and Quantitative Anal-ysis 9 (January 1974): 89-106; W. Sharpe, "Mutual Fund Performance," Journal of Busi-ness 39 (January 1966): 119-38; U.S. Securities and Exchange Commission, InstitutionalInvestor Study (Washington, D.C.: Government Printing Office, 1971).2. Economic Report of the President (Washington, D.C.: Government Printing

Office, 1973); R. Klemkosky, "Institutional Dominance of the NYSE," Financial Execu-tive 41 (November 1973): 14-20; R. Klemkosky and D. Scott, "Withdrawal of the Indi-vidual Investor from the Equity Markets," Business Topics 21 (Spring 1973): 7-14; NewYork Stock Exchange, 1973 Fact Book (New York: NYSE, 1973); R. Soldofsky, Institu-tional Holdings of CommonStock: 1900-2000 (Ann Arbor: University of Michigan, 1971);U.S. Securities and Exchange Commission.3. "Are the Institutions Wrecking Wall Street," Business Week (June 2, 1973), pp.58-66; W. Martin, Jr., "Lower Negotiated Rates Will Force Out Small Investors," Com-mercial and Financial Chronicle 215 (June 1, 1972): 1-17; C. Wood, "Why It's Hard toRaise Capital Today," Financial Executive 41 (November 1973): 21-28.296

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297 InvestmentStrategyand Behaviorwe believe we know about the individual investor's circumstancesand de-cision processeshas been inferredfrom either broad trading statistics, gen-eral security price movements, or portfolio simulations.4With few excep-tions,5very little in the way of explicit observationhas beenattempted-dueundoubtedly to the fact that the necessary data are exceedingly difficultto acquire.This gap in ourknowledgemeritsdismaynot simply becausethe objectof concern shows signs of heading for the exits, but because he has alwaysbeen-and, even at this writing, continues to be-a significant contributorto the allocational and liquidity functions of the equity markets. Accord-ingly, it is important that we understand his activities before possible re-medial steps to counteracthis withdrawalbecomemoot and jurisdictionforthe analysis passes from the economist to the archaeologist.The task of thepresent paper is so defined.In particular,we shall addressempirically the matter of the portfoliodecision processes of the individual equity investor, using data obtainedfrom a large-scalequestionnairesurvey of a representative cross-sectionofsuch individuals, together with supplementaryactual transactionsinforma-tion describingactivity in the correspondingtrading accounts. The objec-tives are, first, to identify the systematic patterns of investment behaviorexhibited and, second,to appraisethe rationality of those patterns-that is,are they internallyconsistentand do they fit with reasonablea priorinotionswith regardto appropriateresponsesto risk, liquidity, and personaltax con-siderations?

I. THE SAMPLE AND THE DATAThe investor groupon which we shall focus consists of a sample drawnfromthe customerclientele of a large national retail brokeragehouse. It is com-prised specificallyof individualswho had accounts open with the firm overthe full periodfromJanuary 1964 throughDecember 1970and representsarandomselectionof approximately10% of all the individuals who had sucha persistent relationship with the firm. It was stratified geographically tomatch the compositionof the total population of U.S. common-stockhold-ers, as reported by New York Stock Exchange (NYSE) surveys.6No cri-terion of account activity was imposed for eligibility, however, and thesampletherefore ncludesindividualswho embracea broadspectrumof bothportfoliosizes and tradingfrequencies.The preciseperiodchosenfor investi-gation reflectsan attempt to span a variety of externalequity market condi-

4. L. Fisher and J. Lorie, "Rates of Return on Investments in Common Stocks,"Journal of Business 41 (July 1968): 291-316.5. K. Baker and J. Haslem, "Toward the Development of Client-specified Valua-tion Models," Journal of Finance 29 (September 1974): 1255-63; M. Blume, J. Crockett,and I. Friend, "Stock Ownership in the United States: Characteristics and Trends," Surveyof CurrentBusiness 54 (November 1974): 16-40; I. Friend and J. DeCani, "Stock MarketExperience of Different Investment Groups," American Statistical Association: Businessand Economic Proceedings (1966), pp. 44-51; R. Potter, "An Empirical Study of Motiva-tions of Common Stock Investors," SouthernJournal of Business 6 (July 1971): 41-48.6. New York Stock Exchange.



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298 The Journal of Businesstions (three upward and two downward major general price movements),tempered by constraints on convenient historical data availability from thebrokerage irm'sfiles.For each of the some 2,500 accounts thus selected, a complete recordof transaction activity over the 7 years was obtained. To the group was thensent a questionnaire which solicited information on demographic charac-teristics, market attitudes, investment objectives, decision processes, port-folio strategies, and asset holdings. The questionnairewas mailed in mid-1972, and just under 1,000 usable completed forms were returned.Througha coding scheme, the questionnairereplies were matched to the underlyingtransactionsrecordsfor each of the respective accounts. That map, and theoriginal address file, were thereupondestroyed to preserve the respondents'anonymity.7It is this combinedbody of evidence-some of it "hard"andsome "soft"; some of it factual and some perceptual-which comprisestheraw material for our investigation.8The key to the usefulnessof such evidence, of course, revolves aroundthe issues of the representativenessof the groupconsideredand the inherentquality of the data provided in the questionnaire replies. Because we havetreated both matters at length elsewhere,9we need only summarizethosediscussions here. They documented (1) that the survey respondents havedemographicattributes whichline up well with those of both the generalU.S.shareholderhead-of-householdpopulations and a "controlgroup" question-naire survey sample of brokeragehose customers selected without regardto account longevity; (2) that it was impossibleto distinguish the question-naire respondentsfrom the nonrespondentson the basis of the volume, fre-quency, and compositionof the trading activity in their accounts over theinterval studied;and (3) that the over-all completionrate of the interroga-tories on the returnedquestionnaireforms was roughly 95%-suggesting adesirablelevel of care and attention applied by the respondentsto the taskwith which they were confronted."

7. In the same vein, the entire data file remains with the investigators and will notbe transmitted to the brokerage house for analysis.8. For additional details on the mechanics of sample selection, questionnaire design,and data collection, see R. Lease, W. Lewellen, and G. Schlarbaum, "The Individual In-vestor: Attributes and Attitudes," Journal of Finance 29 (May 1974): 419-33.9. Ibid.10. As would be expected, given the size, geographical coverage, and strong retailorientation of the brokerage firm which supplied the sample.11. Additional support for this contention is available in the literature of surveyresearch, which indicates that the relative accuracy of questionnaire replies has been foundto be a positive function of the educational background and income level of the groupqueried (J. Lansing, G. Ginsburg, and K. Braaten, An Investigation of Response Error[Urbana: University of Illinois Press, 1961]; J. Lansing and J. Morgan, Economic SurveyMethods[Ann Arbor: University of Michigan Press, 1971]). The individuals in our sample-and shareholders generally-are well above average in both respects (J. Bossons, "TheDistribution of Assets among Individuals of Different Age and Wealth," in InstitutionalInvestors and CorporateStock, ed. R. Goldsmith [New York: National Bureau of EconomicResearch, 1973], pp. 394-428; Lease et al.).



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299 InvestmentStrategyand BehaviorFor all these reasons, we believe the data to be sufficiently reliable,'2and the sample to be sufficientlyrepresentative, to justify extrapolating tothe broaderpopulation of individual investors any significantbehaviorpat-terns whichemerge. Indeed, if the group studied is peculiar t is so in a worth-while way, that is, it is comprisedquite specificallyof individuals who havehad equity funds under consciousdirect personal management for a reason-ably extended period of time. As a clientele with whom to address issues ofstrategy formulationand investment tactics, one could hardly choosea moreappropriateset of subjects. Further, an adequate rangeof personal financialand demographiccharacteristics s encompassed within the group as to per-mit decent cross-sectional nferences to be drawn; table 1 portrays certain

of those dimensions. In all, therefore, we feel the sample to be both a viableand a valuable microcosm.II. ANALYTICAL APPROACHFrom the questionnaire replies and the transactions data associated withthe 972 individuals included therein, variables which give concrete expres-sion to four broad elements of investment activity were developed for anal-ysis. They cover (1) basic portfolioobjectives, (2) informationcollection and

Table1Characteristicsof the Investor SampleCharacteristic % Characteristic %

Age (years): Occupation:Under 21 ............... < 1 Professional/technical . 2721-34 .................. 3 Manager/proprietor . 2935-44 .................. 12 Clerical/service. 745-54 .................. 29 Craftsman/laborer. 355-64 .................. 26 Farm owner/farm laborer 265 and over ............. 30 Not employed. 32Sex: Total asset holdings ($):Male.................. 80 Under 100,000 .27Female ................. 20 100,000-199,999 .30Family income ($): 200,000-299,999 .15Under 5,000 ............. 2 300,000-399,999. 85,000-9,999 ............. 8 400,000 and over. 2010,000-14,999 ........... 15 Common stock holdings ($):15,000-19,999 ........... 13 Under 50,000 .5120,000-24,999 ........... 18 50,000-99,999 .1825,000-49,999 ........... 26 100,000-149,999 .1050,000 and over .......... 18 150,000-199,999. 7Education: 200,000 and over 14Less than H.S... . 11 Annual trading volume ($):H.S. graduate ........... 12 Under 5,000 .29Some college ............. 23 5,000-9,999 .18BA/BS .................. 31 10,000-14999 .11Graduate degree ......... 23 15,000-24,999 .1225,000 and over .3012. The transaction records, of course, are completely "hard" and make possiblea variety of internal checks on the validity of the questionnaire replies, as will be discussedbelow.



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300 The Journal of Businessdecision mechanics, (3) instrument selection and portfolio composition, (4)return perceptions and market attitudes. We regard these not only as thekey behavioral dimensions but, in the hierarchy ndicated, as a logical direc-tional model of the investment process.Thus, we see the individual as mov-ing sequentially from goals to analysis to choice to evaluation-with thefeedbackloop being closed fromthe last of these back to the first for the nextroundof decisionmaking. Our intent here is to identify those aspects of per-sonal circumstances which appear to account for differencesin behavior ateach stage, to explore the matrix of interactions among the several stages,and to assess the rationality and consistency of the patterns manifested.For a variety of reasons, such a task commends reliance on a set ofanalytical techniquesother than the populartools of correlationand regres-sion typically employedin financialresearch.To begin with, a number of therelevant variables-particularly those derived from the questionnaire-arecategorical rather than continuous, and many have no inherent ordinalcharacter.'3While dummy variables to represent these categories could beintroduced in a regression equation, the fact that the resulting error termstend not to be normally distributed compromisesthe interpretation of thefindings-and the volume of such variables requiredrapidlyconsumes avail-able degrees of freedom. Second, even in the case of attributes which aremore standard n form,the assumptionof linearity in the relationshipswhichis required for the application of regression techniques is often violated.Equally bothersomeis the presence of substantial multicollinearityamongthe likely independentvariables, coupled with the frequent lack of naturalscaling opportunities as devices for circumvention.'4Third, there is clearevidence of segmentation in the underlying behavior patterns, of a sortwhich the usualimposition of a regressionformat on the sample as a homo-geneous entity is apt to conceal-especially since solid a priori hypothesesabout many of the possible interactions we must deal with are not readilyavailable. Indeed, we are aware of that segmenting'5only as a consequenceof having exploredalternative modes of analysis that are designed explicitlyto disassemble the sample, to searchfor candidate variables, and to revealrelationships that apply only to a portion of the total group.'6Finally-and perhapsmost importantly-the thrust of regressionandother conventionaleconometric echniquesis to attempt to identify the pres-ence of systematic relationshipsby criteria nvolving the degreeof adherenceof everyobservation n the data set to the pattern in question.The associatedtests of significance,therefore,are conducted in terms of weighted-average

13. As examples, sex, occupation, security selection approach.14. Thus, if sex and income level are highly intercorrelated (as they are), and to-gether are thought of as potential independent variables in attempts to explain some aspectof portfolio construction, a legitimate common scalar to alleviate the problem is difficultto create.15. To illustrate: certain differences in investment style show up only at investorages 44 and under, and then only at annual income levels below $15,000.16. As treated in J. Sonquist, Multivariate Model Building: The Validation of aSearch Strategy (Ann Arbor: University of Michigan Press, 1970); J. Sonquist, E. Baker,and J. Morgan, Searching or Structure(Ann Arbor: University of Michigan Press, 1971).



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301 InvestmentStrategyand Behaviorindividual deviations from the hypothesized profile. For phenomena of thecharacterat issue here, however, such an approachseemsinappropriate.Be-cause we are dealing heavily with elements of internalattitude developmentand decision formulation, we should anticipate a very substantial level ofsingle-observation"noise" due to those peculiaritiesand aberrations of per-sonal makeup which originate inevitably in circumstance dimensions un-reachable by variables we can construct as measurementinputs. Hence, weshould properlybe skepticalof the possibilitiesfor explaining well the fit ofeach individual investor in the sample into a neat behavior pattern.'7 In-stead, we should aim at detecting significant differences among broadergroup investment styles and be content with any reasonable successes wecan achieve in that regard.The experienceof marketresearcherswith similarinvestigations of consumer behavior amply documents the relevant issuesand difficulties.'8Pursuantto such considerations,we shall organize our analysis aroundtechniqueswhich are in somerespectsmore relaxed in their statistical powerbut, on the otherhand, are moresuitable to the inspectionof group behavior,segmentation phenomena,and nonlinearities;accordingly, they are more ro-bust and more revealing for the objectives at hand. In particular, we shallcast up the relevant portrayals through extensive use of automatic interac-tion detection (AID) analyses and multiple cross-classifications.The formerapproach has received wide attention in the marketingand survey researchliterature; we shall have furthercomments about it later on.The latter is nothing more than a convenient device for partitioningasample across variables into groups for purposesof exposing bivariate rela-tionships. The analysis takes the form of a set of stacked cross-tabulationmatrices, with significancetests applied at each level to determine whetherthe two variables in question are related, holding all other variables constant.'9 The key to its effective implementationlies in establishing the ap-propriate control variables-in deciding initially on the dimensions of the"stacks." This is readily accomplishedby examining the patterns of ioint

17. The harsh critic will (accurately) translate this to read: We find very low R2values in the regressions on individual observations.18. In reflection of which, it has been argued that .. . studies of behavior in whichthe dependent variable has been some measure taken on individuals have frequently re-sulted in a small proportion of explained variance by the independent variables. The lowR2 values have been generally discouraging to marketing scholars. Many think that the''noise" must be the result of poor measurement methods, and that the development ofimproved measures will result in substantially reduced noise levels. While one can onlyapplaud attempts to improve measurements, and it is true that measurement error con-tributes to noise, is it not now time to give serious consideration to the proposition thatthere are substantial stochastic elements which characterize the behavior of individualconsumers? If so, then there are severe limits to the amount of noise reduction which canbe achieved by improved measurement, and criteria other than R2 are required for theevaluation of research results" (F. Bass, "Unexplained Variance in Studies of ConsumerBehavior," in Control of "Error" in Market Research Data, ed. J. Farley and J. Howard[Lexington, Mass.: D. C. Heath & Co., 1975]).19. For a full treatment, see M. Rosenberg, The Logic ofSurvey Analysis (New York:Basic Books, 1968).



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302 The Journal of Businessdependencewhich emergefrom trial multiple-regressionruns on the data ofinterest.

As an illustration, suppose it were desiredto identify those aspects ofpersonal demographiccircumstances which account for differences in theannual commonstock tradingvolume of investors. A stepwise regressionofvolume on age, sex, income, education, occupation,etc., might suggest thatonly age and income had detectable influences,but that they were substan-tially intercorrelatedand that the explanatorypower of the equation (runon individual observations) was, in any case, quite low. In seeking then todetermine whether sizable group differenceswere nonetheless present, twodistinct cross-classificationswould be executed-(1) age groups by tradingvolume groups, holding income constant; (2) income classes by volumegroups, holding age constant-and separate significancetests conducted totest independencewithin each array. Apart from its (for our needs) logicalfocus on broader investor categories and its facile accommodationof non-ordinalvariables, such an analysis has the added virtue of high informationcontent in presentation. Both the direction and magnitude of the patternsat issue appear in bold relief in the tabulations, and any nonlinearitiesareeasily discerned.For these reasons, a cross-classificationcontingency tableframework,designed throughouton the basis of preliminary regression ind-ings, will be emphasizedin our investigation.

III. VARIABLES EMPLOYEDThat investigation will make use of some 30 variables, derived from eitherthe questionnaireresponsesor the account transactionsrecords,as descrip-tors of investment behaviorand investor characteristics. Included, for eachof the 972 individuals in the sample,20 re seven demographicattributes-age, sex, marital status, educational attainment, occupation, income, andfamily size-plus the following catalog of variables classified according toour perceptionof their fit into the four elements of the overall investmentdecision processdefinedearlier.A. Investmentgoals1. Theinvestor's ating,on a scaleof one to four,of theimportanceo him ofthe followingobjectives orhis common tockportfolioa) Short-termapitalgainsb) Intermediate-termapitalappreciationc) Long-term apitalappreciationd) Dividend ncome2. The investor'sndicationof the benchmark e usesin settinghis portfolio

returngoalsa) Dow-Jones ndustrialAverage,NYSE Index,Standard& Poor's500,or equivalentb) Averagemutual undperformancec) Portfolioresultsof friendsorfamily20. The net resultof 1,030returnedquestionnairesfroma total mailingof 2,506),less 40 that were unusabledue to incompleteor illegibleresponses,and out of which anadditional18 wereremovedbecauseno commonstock transactionevents appeared orthe correspondingccount n the brokerageirm's ilesbetween1964and 1970.



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303 InvestmentStrategyand Behaviord) Savingsaccount nterestratese) Otherpersonal tandard3. The averageannualpretaxpercentage ate of return he investorbelievesis generallyattainable romhis portfolio4. The investor'sattitude towardrisk taking, as embodied n his degreeofagreement, n a scaleof one to five,with the statement:"I like to takesub-stantial financialrisks to realizesignificant inancialgains from invest-ments"B. Decisionmethodsand technique hoices5. The investor'sndication s to whichof the following ommon tockevalua-tion approaches e employsa) Fundamental nalysisb) Technicalanalysisc) Combinationundamental/technicald) Rely on accountexecutive oradvicee) Otherpersonalapproach6. The investor's ating,on a scaleof one to four,of the usefulness o him,ofthe followingnformation ourcesa) Investmentresearch ubscriptionsb) His brokerage ouse'srecommendationsc) Financialperiodicalsd) Paid professionalounselors7. The investor's ndicationas to whetherhe has an accountwith morethanonebrokerage ouse8. The numberof hoursper monththe investorreportshe spendson invest-mentanalysisand decisionmaking9. The amountof moneythe investorreportshe spendseachyearfora) Financialperiodicalsb) Research ubscriptionsc) Professional ounseling10. The investor'sndicationas to whetherhe usesas investmentvehiclesa) Margin ransactionsb) Put orcall optionsc) Short salesd) Warrants

11. The percentage f the investor's ransactions verthestudyintervalwhichwerea) Initiatedat his accountexecutive's uggestionb) Initiatedby the investorhimself12. Thepercentage f transactionswhichtook placein NYSE common tocksoverthe studyinterval13. Thepercentage f transactions uring he studyintervalwhichweremar-gin trades14. The total number f transactions xecutedduring he studyintervalC. Portfoliocomposition esults15. The numberof different ompanies'haresheldin the investor'sportfolio16. The investor's eportas to whetherhe owns shares n any mutual unds17. The percentages f the investor's tock portfoliohe considerso consistofa) Primarilyncomesecuritiesb) Primarily apitalappreciationecurities18. The percentageof the investor'stotal asset holdingswhichhe reports sinvestedin common tocksD. Portfolioevaluation19. The investor's ndication of the averageannualpretaxrate of returnhehasrealizedoverthe last 5 yearson his portfolio



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304 The Journal of Business20. The investor's xpressed egreeof agreement, na scaleof one to five, withthe statement:"Giventhe risk level of my portfolio,my averagerealizedreturnhas been lower hanthe marketaverage"21. The investor's xpressed egree f agreementwith the statement:"Securitypricesarenot predictablen the shortrun"22. The investor'sexpresseddegreeof agreementwith the statement:"Mysecurity tradingfrequency s substantially ess than for the average n-vestor"

Those taken from the questionnaire nstruments typically called-as can beseen-for the checking of multiple-choice categories, or for scaled ratingevaluations. The exception was a question on the composition of the sub-ject's total asset portfolio in which he was asked to list the marketvalue (tothe nearest $100) of his holdings in each of 15 specific asset forms. It wasfrom those responses that the percentage figures for variable 18 were com-puted. Variables 11 through 14 were created directly from the underlyingactual transactions history in the account involved over the 7-year studyinterval.The items thus selected-all of which have either inductive appeal orempirical popularity-do not, of course, exhaust the resources available.Well in excess of 100 different interrogatorieswere on the questionnaire,and the transactionsfile itself contains an abundant variety of data elements.Nonetheless, the indicated variables on which we shall concentrate should,we believe, capture most of the critical aspects of investment behavior andallow an effective identification across the sampleof any majorpatterns andgroup differences.Moreover, even as it stands the roster offersa sufficientlylengthy array of analytical possibilitiesas already to tax the likely limits ofreaderenduranceand manuscriptsize.

IV. INVESTMENT STYLES ANDINVESTOR DEMOGRAPHICSAmong the clearest, if not necessarily the most startling, messages whichemerge from the data is that investment behavior is in fact very much adirect and systematic function of personal circumstances.Who the investoris-as definedby a relatively short list of standarddemographicattributes-heavily determines not only what he does but also how he views the processin which he is engaged. Thus, there are a numberof statistically significantsocioeconomiccross-sectionalpatterns observable;the samekey independentvariables show up reliably in connectionwith quite a broadspectrumof in-vestment phenomena;the directionsof their influencesare stable and inter-nally consistent; those effectsarerevealed equally by each of several analyti-cal approaches;and they are generallyin accord with what seem logical be-havioral scenarios. A coherent story can, in short, be discerned-and, if itcontains few dramaticsurprises,the singularresourcesof evidence availablehere for documentationprovide their own amplerationale for a portrayal.By far the dominant elements in the story are investor age, incomelevel, and sex, essentially in that descending order of importance and un-



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305 InvestmentStrategyand Behaviorquestionably as characteristicswhich override occupation, marital status,family size, and educational backgroundas significant influences.The lastfour attributes make an occasionalmodest contribution to the explanationof differences in investment style and strategy, but those contributionsare notably largely for their rarity. This hierarchy of impacts is manifestthroughoutthe analysis-in the preliminaryregressionrunswhichsuggestedsubsequentavenuesformore detailed investigation,2' n the AID runswhichsought furtherto uncovera profileof likely candidatevariables (see below),and in the cross-classificationmatrices of most immediate interest.

PortfolioGoalsThose matricessuggest some definitepatterns in the investment goal formu-lation processin several respects. Whereason an "importance"rating scaleof one (irrelevant) to four (very important), the mean responses of thesample to the four objectives posed on the questionnairewereShort-termcapital gains ................... 1.84Intermediate-termcapital gains....... ..... 2.45Long-termcapital gains................... 3.56Dividend income ..................... 2.65

the individual's age had a strong influenceon attitudes toward the first ofthese. The older the investor, whether male or female, the progressivelyless the reportedinterest in short-termcapital gains as a portfolio goal, astable 2 shows.22Thus, there is a distinct downwardshift in the relevant rat-ings with age and the likelihood that this pattern could have arisen bychance from a sample which was actually homogeneousin attitudes acrossage bracketsis well below .01 in both instances.23Accordingly,the charac-terization in folklore that age breeds conservatism (or is it wisdom?) ininvestment pursuits seems tentatively confirmed.The logicalmirror mage of this phenomenonappearsin the attitudestoward dividend receipts as an investment objective, although the relation-ship has an added income dimension.Table 3 outlines the responsepatternsin terms of both the gross age and income profilesas well as the bivariate

21. A representative sampling of these is offered in the Appendix.22. Parenthetically, an unadjusted cross-classification of these ratings by sex aloneimplied a consistently milder emphasis on short-term profits by women than by men. How-ever, when age was held constant, the phenomenon disappeared. The need to exercise suchcontrol was indicated by a preliminary multiple regression of the ratings on the seven demo-graphic variables available, wherein age and sex displayed substantial collinearity. Ineffect, the women in our sample are, on average, somewhat older than the men and, whenthat circumstance is taken into account, a number of apparent investment behavior differ-ences between the sexes, here and elsewhere, turn out to be illusory.23. The underlying test is a simple x2, on the differences between the observed cellsizes and the predicted values assuming homogeneity across categories. At the .01 con-fidence level, the critical x2 for the 9 degrees of freedom applicable here is 21.7, and thecalculated values were 82.8 and 26.8, respectively, for males and females. The test is com-promised if predicted cell sizes are as small as five, but the magnitude of our sample (N =972) easily circumvents that concern. Finally, while the test explicitly addresses only theissue of whethera relationship is present, and does not measure its strength,the tabulationswill, in each instance, provide a clear sense of the latter.



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306 The Journal of BusinessTable 2Short-TermCapital Gains as a PortfolioGoal vs. Investor Age (Cross-Classifica-tion Analysis)

Goal Importance RatingInvestorAge (Years) 1 2 3 4 TotalA. Male Investors

Under 45 ....... . 25* .30 .26 .19 1.0045-54 .......... . 37 .27 .23 .13 1.0055-64 .......... . 58 .20 .14 .08 1.0065 and over.. .65 .23 .07 .05 1.00(Probability of independence< .0001.)

B. Female InvestorsUnder 45 ....... . 28 .32 .28 .12 1.0045-54 .......... . 48 .30 .09 .13 1.0055-64 .......... . 71 .17 .05 .07 1.0065 and over . .72 .18 .05 .05 1.00(Probability of independence= .0015.)

* I.e., 25% of male investors below age 45 rate short-term capitalgains "one" n importance,on a scale of one to four, where the rating cate-gories are: 1 = irrelevant;2 = slightly important; 3 = important; 4 = veryimportant.relationship between age and goal rating, holding income constant.24Thedisplays reveal that the concernwith dividends increaseswith age but dimin-ishes with family income level, and that at all income levels greater ageproduces greater expressed relative interest.25Not only do these patternsnicely complement those for the short-term capital gains ratings, but theyhave obvious appeal as reasonablereactions to personal tax, liquidity, andcareer-cycleconsiderationsas well. Thus, an individual who already enjoyssubstantial annual income should have less need for additional immediatecash returns from his investment portfolio, and the heavy tax burden thatwould be imposed on those receipts further reduces their attractiveness. Asthe same individual passes his peak employment earnings years and movestoward and into retirement,however, a desire to shift his portfolio towardincome securities as a means of offsetting the earningsloss seems eminentlyappropriate.The evidence cited-which shows the major revisions in divi-dend importanceratings to occur beginning at age 55 and to be mildest forupper-income nvestors-suggests just such a combinedset of reactions.The other two investment goal alternatives queried, long-term and in-termediate-termcapital appreciation,exhibit no comparablesensitivity topersonal circumstances.The formerwas so consistently highly rated by thesample as an objective for their portfolios that little scope for discerning

24. Again, an approach prompted by trial regression results that identified the can-didate explanatory variables and their attendant collinearity. With age and income heldconstant, male and female investors' preferences were indistinguishable.25. The calculated x2 statistics for the tables range from 35.8 to 232.5, all well inexcess of the critical values for the .01 confidence level.



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Table3Dividend Income as a Portfolio Goal vs.Investor Age and Income (Cross-Classi-fication Analysis)

Goal Importance Rating1 2 3 4

A. Age vs Dividend GoalImportanceInvestorage (years):Under 45 .35 .38 .18 .0945-54 .27 .39 .25 .09

55-64. .10 .28 .29 .3365 and over . 05 .18 .23 .54B. Income vs. Dividend GoalImportance

Investor family in-come (:Under 15, 000. .10 .22 .22 .4615,000-24,999. .18 .28 .27 .2725,000-49,999. . .19 .28 .27 .2650XO0 up . 22 .46 .20 .12C. Age vs. Dividend Goal Importance,by Income Bracket

Investors with incomesbelow $15,000:Under 45 .29 .33 .17 .2145-54 .21 .47 .15 .1855-64. .10 .21 .30 .3965 and over . 04 .13 .21 .62Investors with incomesbetween 15,000 and$24,999:Under 45 .31 .43 .18 .0845-54 .30 .36 .26 .0855-64. .14 .26 .32 .2865 and over. .04 .12 .25 .59Investors with incomesbetween $25,000and $49, 999:Under 45 .36 .31 .23 .1045-54 .27 .33 .29 .1155-64. .08 .24 .30 .3865 and over. .08 .21 .24 .48Investors with incomesof $50,000 andover:Under 45 .43 .43 .11 .0345-54 .30 .46 .19 .0555-64. .07 .43 .22 .2865 and over . 04 .54 .27 .15

NOTE.-Probability of independence


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308 The Journal of Businesspatterns was permitted. The latter had some variation across investorgroups, but in neither a systematic nor a statistically significant manner.We suspect, as a concept, it simply was not sufficiently well defined in thesurvey subjects' minds as to elicit a strong response and that perceptionsof it overlapped other, sharpergoal characterizations.In any case, the port-folio objectives that shouldhave conveyed clear (and polar) images-short-run tradingprofits and dividend income-did generate both substantial andreasonable ntrasample attitude differences.26When asked about the performancecriterion used in assessing theirinvestment success, some 42% of the respondents ndicated that one of themajor public indices-the DJIA, NYSE, and S &P 500 were offeredas illus-trations-was selected. Another 45% reported that they had internalizedinstead a personalstandardof return as an amalgam of experience,evidence,and concepts of "fair" yield. The scattered remainder employed mutualfunds, savings account interest rates, or friends'and family experiencesasbenchmarks. By and large, all these proportionsvaried little among demo-graphicsubgroups.The one exception was a noticeable shift in the directionof adopting one of the major price indices as the predominantcriterionbyinvestors aged 65 and over. That move was easily significantat the .01 leveland would seem to go hand in hand with the message above that simul-taneously there evolves a more stable portfolio strategy which directs in-creasingallocations to the blue-chip, dividend-payingsecurities that domi-nate the popular indices.A translation by the sample of these qualitative performance tandardsinto a quantitative measureof "adequate"or "reasonable"return,however,produceda very broad-basedand intriguingpattern: the older the investor,the progressively lower his estimate of-as our question phrased it-theannual percentagerate of return, before taxes, which he consideredattain-able on a regular basis from investments in common stocks. Table 4 dis-plays the results. While investor age was the only demographicattributehaving a detectable influence on these estimates across the sample,27 hatinfluencewas obviouslystrong;views of attainableequity-investmentreturnwere more than half again as high among investors below age 45 as amongthose 65 and older. The extent to which this phenomenon is a reflection

26. Anissuewhich bearsnot only on thesebut on all the questionnaireesponses,of course, s thatof externalmarketandeconomic onditionsat the timeof the surveyandthe biases that might therebyhave been introduced.Little concernon this scoreseemsmerited,partlybecause he focushere s onpatternsacross he sample-presumably,mostoutside influenceswould have impactedall subgroups n roughlysimilarfashion-andpartly because the environmentn mid-1972when the questionnairewas mailedwas infact a fairly"neutral" ne.The securitiesmarketshad recoveredrom the 1969-70excite-ment quite well, but were neitherstronglybullishnor bearishat the time; the dramaticinternationalmonetary,energy,and commodity-pricepheavalswereyet to appear;anddomestic nflationat the recenthighrateswasnot thenthe objectof scrutinyand concernthat it since has become.27. Neitherincome evel nor sex displayedan impact,onceage was taken nto ac-count.The age relationships, n the otherhand,held for both sexesseparatelyand in allincomerages,at the .001 level or better.



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309 InvestmentStrategyand BehaviorTable 4Annual Before-Tax Rate of Return ThoughtAttainable from a Common Stock InvestmentPortfolio vs. Investor Age (Cross-ClassificationAnalysis)

Attainable Annual Return Estimate (%) AverageInvestor Age Estimate*(Years) 0-5 6-10 11-15 Over 15 (%)Under45.......... .10 .35 .35 .20 11.345-54............. .11 .47 .25 .17 10.355-64 ............ .23 .55 .14 .08 8.165 andover ....... .29 .54 .11 .06 7.4

NOTE.-Probabilityof independence< .0001.* Calculated rombracketmidpointsand assuming he midpoint of the "over 15%"bracket to be 20%.simply of associated changes in investment strategy, or also includes thelessons of bitter experience, is difficult to assess. Certain findings to be dis-cussed below would argue for at least some role for the "learning"effect.Nonetheless, if the explanationis open to debate the evidence is clear.Equally clear, finally, is a pattern in the respondents' explicit charac-terizations of their portfolio intentions. On a scale of one to five, rangingfrom "strongly disagree" (1) to "strongly agree" (5), they were asked theirreactionsto the statement: "I like to take substantial financialrisks to real-ize significant financial gains from investments." Table 5 documents a pro-file which is consistent with the portfolio goal ratings depicted earlier, inthat age and expressed risk-taking propensities are inversely related, withthe major shifts again taking place at age 55 and beyond. Of interest as wellis a pattern which did not emerge, that is, between income level and atti-tudes toward risk. Upper-incomeinvestors saw themselves as no more orno less inclined to assume exposed portfolio positions than did their moremodestly situated contemporaries. Evidence on the facts of their tradingactivities, however-as we shall see-would belie those assertions and sug-gest that risks are indeed more regularlyundertaken as income rises.28

Table5Expressed Investment Risk-taking Desires vs.Investor Age (Cross-ClassificationAnalysis)Scaled Desire to Take RisksInvestor Age(Years) 1 2 3 4 5

Under45 20 .18 .17 .27 .1845-54 .20 .18 .17 .28 .1755-64 .29 .24 .12 .23 .1265 and over 33 .26 .17 .15 .09NOTE.-Probability of independence = .0021.

28. See also R. Cohn, W. Lewellen, R. Lease, and G. Schlarbaum, "Individual In-vestor Risk Aversion and Investment Portfolio Composition," Journal of Finance 30 (May1975): 605-20.



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310 The Journal of BusinessInvestingMethodologyThe investment tactics and technology employed within the sample displaya somewhat richer variety of demographic differences than do the goalstoward which the several subgroupsappearto be aiming. In particular,cer-tain style and process distinctions between male and female investors be-come apparent. The areas of information gathering and decision makingoffers pertinent illustrations.Table6Investment Analysis and Investor Demo-graphics (Cross-ClassificationAnalysis)A. Primary Security Selection Approachvs. Age and Sex

Primary Approach to AnalysisFundamental, Rely onInvestor Age Technical, Broker(Years) or Both Advice Other

Male investors:Under 55 .................... .71 .20 .0955-64 ....................... .71 .17 .0965 and over .................. .75 .11 .14(Probability of independence=.0192.)Female investors:Under 55 ................... .56 .35 .0955-64 ....................... .52 .36 .1265 and over .................. .55 .34 .11(Probability of independence=.9778.)

B. Rating of Value of Investment Informa-tion, by SexInformation Useful

Source of Information Never Occasionally Generally AlwaysBroker:Males ................. .07 .32 .41 .20Females .............. .07 .23 .43 .27(Probability of independence=.0712.)Research service subscrip-tions:Males ................. .23 .45 .21 .11Females ............... .31 .54 .10 .05(Probability of independence=.0001.)

C. Time Spent per Month on InvestmentAnalysis and Decision Making, by SexTime Spent (Hours)

Sex 0-5 5-10 10-20 Over 20Males................... .51 .19 .14 .16Females ................. .68 .16 .10 .06(Probability of independence=


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312 The Journal of Businessvestors who designate "broker advice" the primary source of their securityselection decisions is well below that of their female counterparts. The de-cline with age in that proportion is also statistically significant for malesbut not for females. Male investors, correspondingly,are marginally lesssanguine in a direct rating of the value of their brokers' counsel and sub-stantially more entranced by the usefulness of paid external researchser-vices. They commit detectably more time to digesting and evaluating in-formation to reach decisions, at least by their own reckoning.Reflecting these assertions, they report both greater information-collecting expendituresat all income levels as well as a willingnessto spendincreasing amounts as their financialsituation permits. And, as a last mani-festation, they are much more prone to have accounts with more than onebrokeragehouse rather than accede to the recommendationsof a single firm.Thus, though income level is the dominant influence on the popularity ofmultiple accounts, the pattern is a peculiarly male one, and the frequencyin the group exceeds that for females in every earningsbracket.29The wis-dom of all these search and analysis procedures,of course, is an open ques-tion, especially since we observed that the ultimate portfolio objectives in-volved seem congruentwith those of female investors. Only hard evidenceon portfolio performancehistories can resolve that issue-an investigationwhich must await another forum.The hard evidence that is immediately available-on various rawdimensionsof account trading activity-would support the notion that dif-ferences in proclaimed analytical style do have at least a few portfolio as-sembly and turnoverimplications.As an example, it would be reasonabletohypothesize, on the basis of the cited statements about greater attentionto security analysis and decision making, that male investors would trademore often and that trading frequencyon theirpart might well increase withincome. Table 7 suggests such a profileof heavier "management,"althoughthe significancelevel is rather modest. On the other hand, if we integratethese figures with the finding of a greater incidence of multiple brokerageaccounts by males, and in higherincome brackets,we can be more confidentthat a pattern exists, since additional transactions which we cannot observedirectly will take place in those other accounts. The stability of the tradingpattern for females, Qfcourse,wouldmerely be reinforcedby such an adjust-ment. In terms of the dollar magnitude of transactions, incidentally, thestory is unambiguous:for both males and females, the relationshipbetweendollar volume and income is significant at the .0001 level. In short, tradesappear to occur more often, and they clearly are in larger denominationsamong higher-income-particularly male-investors.30

29. No significant occupation, education, or family-status effects emerged in thecross sections. To the degree there was a preliminary suggestion thereof, it uniformly dis-appeared when income and age were controlled for.30. A commensurate age vs. frequency profile is not discernible, almost certainlyas the result of two conflicting forces. As observed, the older the investor, the more timeand energy he spends on security analysis-presumably, therefore, the more inclined hewould be to transact. Countering that influence, however, is the evolution of a more stable



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314 The Journal of Businessmight legitimately be regarded as embodying rather higher degrees of riskthan the standard "long" cash position in equities: warrants, put and/orcall options, margin purchases, and short sales." Definite cross-sectionalprofiles of differential use were manifest; table 8 presents the findings formargin transactions.The table shows substantial drop in the frequency ofsuch trades with age-especially at 55 and beyond-and an increase withinvestor income level. The former pattern fits exactly the investment goaland risk-preferenceevidence cited earlier.32The latter would imply thatrisk aversion diminisheswith greaterpersonalaffluenceand is, in some sense,even more compellingwhen combined with the indicationsthat high-income

Table 8Frequency of Margin Trades vs. In-vestor Demographics (Cross-Classifi-cation Analysis)

Use Do NotMargin UseA. Margin Use byAge Bracket

Investor age (years):Under 45 ............... .57 .4345-54 ................... .58 .4255-64 ................... .37 .6365 and over ............. .38 .62(Probability of inde-pendence= .0001.)

B. Margin Use byIncome LevelFamily income ($):Under 15,000 ............ .40 .6015,000-24,999 ........... .42 .58

25,000-49,999........... .49 .5150,000 up ............... .58 .42(Probability of inde-pendence= .0014.)C. Margin Use byFamily Size

Family size (N):1 ................... .61 .392 ................... .61 .393 ................... .47 .534.................... .47 .535 or more ................ .38 .62(Probability of inde-pendence= .0001.)

31. These were not, however, termed"risky" items on the questionnaire. The charac-terization is our own.32. As does the supplementary finding that no male/female differences in marginuse are apparent when age and income are held constant. Recall that, unlike the case foranalytical styles, male and female investor goal structures and expressed risk-taking de-sires were indistinguishable within comparable age and income circumstances.



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317 InvestmentStrategyand Behaviordocuments. At all income levels, the reportedincome-securityfraction risessteadily across increasing age groups. Investor assertions about underlyinggoals therefore continue to appearinternally consistent.A broadenedconcept of the portfolio, of course, is if some interest aswell, and among the questionnaire nterrogatorieswas a request for a listingof the investor's dollar holdings in some 15 asset forms in addition to equitysecurities under direct management. Included on the roster were savingsand checking accounts, mutual fund shares, corporate and governmentbonds, commodity futures, personalresidenceand property, otherrealestate,life insurance, and ownership interests in the individual's own professionalor small-business enterprise. Although a complete analysis is beyond ourpurview here, two dimensions of the responses deserve some attention forthe perspective they provide. The first-the role of the directlyheld commonstock portfolio in the total collection of assets-is summarizedin table 11.It is apparent that the proportionatecommitment to this mode of invest-ment is positively and strongly age related,35 he correspondingreductions(not shown) occurring argely in the personalresidence, life insurance,andown-business categories. All three have obvious life-cycle rationales. Theadded negative influence of family size on the emphasison direct equity in-vestments buttresses the risk-averting stereotype mentioned above. Also,since family size did not emerge as a factor in the income-security patternwithin the equity portfolio, the inference would be that whatever trade-off

Table 11CommonStocks as a Percentage of TotalPersonal Assets vs. Investor Demographics(Cross-ClassificationAnalysis)Percent of Total Assets

0-12 12-28 28-50 Over 50A. Common Stock Percentage vs. Age

Investor age (years):Under 45 .................... .36 .30 .22 .1245-54 ....................... .29 .29 .22 .2055-64 ....................... .19 .29 .31 .2165 and over .................. .19 .17 .27 .37B. Common Stock Percentagevs. Family Size

Family size (N):1 ....................... .09 .17 .27 .472 ....................... .23 .24 .26 .273 ....................... .30 .23 .29 .184 ....................... .29 .35 .25 .115 or more .................... .34 .34 .21 .11

NoTE.-Probability of independence < .0001 in both cases.35. The percentage categories were chosen to divide the sample roughly intoquartiles.



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318 The Journal of Businessfor income and safety is made in the large-familycontext occurs via a shiftinto savings accounts and bonds rather than into dividend-paying commonstocks.The findings for the asset that is in principle the closest substitute fora personally managed collection of equities-mutual fund shares-are asclear, but noticeably less congenial to interpretation. Table 12 indicatesthat the incidence of such holdings is concentrated n the 45-54 age bracketof oursample, other age ranges being virtually indistinguishableand no otherdemographic attributes displaying any impact. Conceivably, this is merelyhistoricalaccident in that these were the individuals who were most suscep-tible to the appeal of funds duringthe halcyon days of fund sales in the late1950s and early to mid-1960s. They would have been some 10-15 yearsyoungerat that stage, less confidentof their own security-analytic capacitiesif our previous portrayals are accurate, but yet beginning to accumulateinvestable funds. Whatever the rationale,no neat pattern of substitution offund shares for direct equity holdings can be discerned.6

Opinionsand EvaluationThe portfolio behavior observed, of course,both influencesand in influencedby the investor'sreaction to the market environmentwhich surroundshim.An important element in those assessmentspresumably would be the degreeto which he believes he can accuratelyforecast the outcome of his decisions.As a means of assaying the profile of such beliefs, we requested each of thesurvey participants to evaluate-again on a scale of one to five, where onedenoted strong disagreementand five strongagreement-the statement "se-curity prices are not predictable in the short run." The results, which giveevidence of a systematic learning response, are contained in table 13. Theolder the investor, the more skeptical he becomes of his predictive powers.

Table12Mutual Fund Ownershipvs. InvestorAge (Cross-ClassificationAnalysis)Frequency of Ownership

Investor Age (Years) Do Own Do Not OwnUnder 45 .................. .38 .6245-54 .................... .51 .4955-64 .................... .42 .5865 and over ................ .41 .59NoTE.-Probability of independence = .0260.

36. In fact, something like the reverse is implied by a cross-classification of fundownership frequency vs. the number of different securities in the direct portfolio, holdingconstant on age brackets. The data show a positive relationship between the two, indicat-ing that the investors in our sample tend actually to use fund purchases as a complementto personal diversification.



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319 InvestmentStrategyand BehaviorTable 13Opinions of Stock Price Predictability vs.Investor Age (Cross-ClassificationAnalysis)Prices Are Not Predictable

Investor Age Strong Mild Mild Strong(Years) Disagree Disagree Neutral Agree AgreeUnder 45 ............. . 04 .19 .16 .40 .2145-54 ............... .04 .15 .09 .45 .2755-64 ............... .01 .10 .12 .44 .3365 and over ........... .01 .07 .12 .32 .41

NoTE-Probability of independence = .0009.

The documented moves toward a longer-term nvestment horizon with age,therefore, would seem to be attributable not simply to tax and income-replacement considerations,but also to an experience-inducedreappraisalof one's ability to outguess the rest of the investment community. No otherdemographiccharacteristics-sex and income included-appeared to evokeany similarreactionpatterns across the sample.Further indication of an educational process, as well as commensurateportfolio strategy revisions, emerges in the reports of realized investmentresults. The question posed was: "What is your impression of your actualaverageannual before-taxportfolioreturnover the last five years?"Table 14clearly suggests a narrowingof the returndistributionwith age-significantat the .0001 level. Thus, the younger investor who engages most heavily inshort-run speculation does record the widest range of consequences. Thelogic is compelling,and its reflection n the repliesincreases confidence n therespondents' careful reporting. Little, however, can be said at this stageabout the average eturns identified. We might well have expected them todecline with investorage, given the other elementsin the portfolio story, butnot necessarily so, and we have not yet assembledthe data to appraise theaccuracy of those assessments.

Table 14Reported Realized Pretax Annual PortfolioReturns vs. Investor Age (Cross-ClassificationAnalysis)Reported Annual Return (%)Investor Age(Years) Loss 0-5 5-10 10-15 Over 15

Under 45 ............. . 12 .18 .31 .17 .2245-54 ............... .12 .15 .33 .28 .1255-64 ............... .09 .14 .47 .19 .1165 and over ........... .05 .20 .42 .22 .11NoTE.-Probability of independence = .0001.



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320 The Journal of BusinessSummaryAn overviewof the full set of demographicrelationshipsportrayed thereforereveals both a reasonable and consistent collection of phenomena. Strongindications of systematic changes in investment objectives and risk prefer-ences across age brackets-and, to milder extent, income classes-are ap-parent. These are mirroredin differences in investment tactics, portfoliocomposition, and environmental attitudes. Though analytical styles are di-verse, especiallybetweenthe sexes, the ultimate goals and resulting decisionshave an underlying harmony. None of the patterns violates any tenets ofrational behavior; by and large, they fit traditional hypotheses; evidenceof incongruity is sparse. For all that, the findings were by no means in-

evitable, nor has appropriate documentation heretoforebeen available.V. PORTFOLIO GOALS AND

PORTFOLIO PROCESSESThe matterof the congruencebetween stated goalsand observableactivities,of course,is a major preoccupationof the behavioralresearcher.The areaofpersonal investing in particularis one which is replete with expressions ofboth concern and skepticismas to whether what people say is a decent guideto what they do. We have addressed that issue for our sample above buthave done so essentially indirectly. That is, the question of the behavioralfollow-through on alleged investment goals has been examined as a by-productof the analysis of demographiccross-sectional nfluences.We soughtto identify the latter for each of the style and strategy variables separatelyand then went on to catalog the observable common threads. We need not,however, rely on so circuitousan approach;we may explorethe relationshipbetween objectives and actions by a direct investigation of the patterns ofstatisticalassociationbetween the two-ignoring the demographicdimensionentirely and grouping nvestors solely by reportedinvestment desires.

Table 15 provides a condensationof the outcome of a series of cross-classification analyses focused on this question. In each instance, the in-vestor's expressed rating of the importance of a basic investment goal istested against a specific measure either of portfolio compositionor tradingactivity. The portrayal is condensedfor reasonsof space limitations-illus-trations of two of the tableaus are included-and is restricted to the short-term capital gains and dividend-incomegoal categories simply because theseare the poles of the spectrum. As discussed previously, the responses toneither the long-term nor intermediate-termcapital appreciation objectiveratings displayed any detectable patterns across the sample. Uniformly,therefore, they were uncorrelatedwith indices of investor personal circum-stances and portfolio strategies.The results shown in the exhibit, however, are persuasive.Differencesin stated aims are systematically reflectedin differences n behavior-in theappropriate directions. Thus, the more important the respondent claims



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Table15Investment Goals and Investment Behavior (Cross-ClassificationAnalysis)A. Associationwith Goal ImportanceRatings

Short-Term CapitalGains Dividend IncomeAspect of Investment Behavior Direction Significance* Direction Significance*

Number of different firms' shares in the in-vestment portfolio .................. Neg. .0012 Pos. .0001Percentage of the portfolio in income secu-rities .............................. Neg. < .0001 Pos. < .0001Number of security transactions executedper year . ................ Pos.


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322 The Journal of Businessshort-termcapital gains to be as an investment goal, the less diversified hisportfolio, the lower the fraction of that portfolio he interprets as providingreturns primarily in the form of dividends, the more often our supportingtransactions file indicates he trades, the more frequently he reports usingrisky-and generallyshort-horizon-investment vehicles, the higher the per-centage of the total transactionsrecorded n the file for him that are in factmarginpurchases,and the lowerthe percentagethereof that are in the moreseasoned securities on the NYSE rather than on the American Exchange,regionalexchanges, or the over-the-countermarket. When profiledagainstthe ratings for dividend income as a goal, the same measures of behaviorshow up with equally strong and exactly opposite relationships.37All arelogical, and all confirmdirectly the consistencies suggested inferentially bythe precedingdemographiccross-sectionalscenario.

VI. OTHER BEHAVIORAL PHENOMENAThe data offer an opportunity to examine a wide range of links between thecomponents of investment strategy, portfolio tactics, and market evalua-tions. Of paramount nterest are those bearingon the attitudes of the sampletoward risk, on the nature of the returns expected from investments, andon the matrix of interrelationshipsamong some key elements of behaviorand opinion. These may be assayed briefly using both the "soft" evidencefrom the questionnaireand the "hard"facts of the underlying transactionshistories.Ourpurest index of sentiments toward risk is embedded in the overtreactions to the statement describedpreviously: "I like to take substantialrisks in orderto realize significantfinancialgains from investments." Eachinvestor was asked to indicate his concurrencewith that statement, on ascale of one to five. Table 16 documents the patterns of associationbetweenthese ratings and certain aspects of trading and investment style. The indi-viduals who expressed the strongest agreement with the characterizationturn out, first of all, to be the ones who reported the greatest incidence ofmargin, option, warrant, and short-sale transactions and the smallest pro-portion of income securities in their portfolios. Looking at the transactionsfile, we find them also to have the highest percentageof margin trades38 ndthe lowest percentageof tradesinvolving NYSE securities.Not surprisingly,their ratings of short-term capital gains as a portfolio goal are at the topof the survey group; those of dividend income at the bottom. Finally, theyare the ones who spend the most time per month on analysis and decision

37. The associated x2 values range as high as 412.1, for bivariate tables typicallyembodying 9 degrees of freedom. As an added sidelight, it turns out-at the .001 level-that high ratings for short-term capital gains go hand in hand with an especially heavy"technical" approach to security analysis.38. Verified by a separate cross-classification of reportedmargin use vs. actual mar-gin trading frequency, from the transaction file. The relationship was positive, and signifi-cant at better than the .0001 level.



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323 InvestmentStrategyand Behaviormaking. A samplingof the full tables is provided. In all instances, the pat-tern is strong and the significanceis high. In our view, each also has con-siderableintuitive appeal as a behavioralmanifestation.Commensuratephenomenaare visible (table 17) in the area of returnexpectations.Those investorswho checkedfigures n the high rangesfor "theaverage annual pretax percentage rate of return attainable from my port-folio"comprise he subgroupwhichexpressedthe greatest willingnessto take

Table16Investor Risk Preferencesand Investment Behavior(Cross-ClassificationAnalysis)A. Associationwith ExpressedRisk-taking Desire

Aspect of Investment Behavior Direction Significance*Expressedncidenceof use of:a) Marginpurchases.............................. Pos. < 0001b) Put or call options............................. Pos. .0003c) Shortsales.................................... Pos. .0001d) Warrants.......... Pos. .0001Percentageof the portfolio n incomesecurities........... Neg.


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324 The Journal of BusinessTable 17Investor Return Expectations and Investment Be-havior (Cross-ClassificationsAnalysis)Association with Level ofExpected Portfolio Returns

Aspect of Investment Behavior Direction Significance*Expressed intensity of risk-taking desires Pos.


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325 InvestmentStrategyand Behaviorwherehistoricalreturnswhichwere at the low end of the aggregatedistribu-tion; and those whofelt that "my security tradingfrequency is substantiallyless than for the averageinvestor" were the ones whom the transactionsfilerevealed to be the infrequent traders. Differential expressions of optimismabout the future, therefore,do not appear to originate in biased appraisalsof the past.

VII. VARIABLE INTERACTIONSWhile the foregoingcross-classificationshave permitted us to focus on keybi- or tri-variate relationshipsamong the important constituents of invest-ment strategy and style, it is clear from the observedpatterns that the de-terminants of behaviorhave ratherbroadinterconnections.The multidimen-sional nature of the process has been manifest in a variety of implicit forms,but it deserves additional explicit attention by way of summarizing ourfindings. The AID analysis alluded to earlier provides an especially con-venient vehicle for both executing and displaying such an overview.The AID algorithm was developed specifically for use in situationswherein a solid a priori model to explain the responseof a given dependentvariable is not readilyavailable,40where there is some reasonto believe that

Table 18Elements of Investor Self-Appraisal (Cross-Classifi-cation Analysis)A. Assessment of Relative Level of Realized PastReturns vs. Reported Absolute Level of Returns

Reported Absolute Level ofAnnual Percentage Return (%)View of Relative Return Level Loss 0-5 6-10 11-15 Over 15

Well above average ......... ....... .02 .06 .23 .34 .35Above average .................... .05 .12 .44 .31 .08Average .......................... .09 .23 .46 .15 .07Below average..................... .19 .27 .34 .13 .07Well below average ......... ....... .31 .23 .20 .12 .14B. Assessment of Relative Trading Frequencyvs.Actual Frequency of Transactions

Actual Transactions per YearView of Relative Trading Frequency 5 or less 6-10 Over 10

Well above average .22 .18 .60Above average .26 .33 .41Average..... .. ................ .42 .28 .30Below average .51 .30 .19Well below average .67 .20 .13NoTE.-Probability of independence< .0001 in both cases.40. Sonquist; Sonquist et al.



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326 The Journal of Businessthe impacts of the possiblecandidateindependentvariablesare not uniformacrossall segments of the populationbeing sampled,wherea numberof thoseproposed variables are neither ordinal nor continuous, and where the as-sumptions of additivity and linearity in the underlyinginfluencesmay wellnot be appropriate.Survey researchefforts of the sort at issue here, whichdeal with individualbehaviorand attributes, often embody all these poten-tial difficulties.In the procedure, the basic principle of least squares is followed, inthat the emphasis is on an ability to reduceerror. The question is, through-out, "What dichotomoussplit on which single candidate independent vari-able will yield maximumimprovement in the capacity to predict values ofthe dependent variable?" It utilizes, in essence, a sequential one-wayanalysis-of-variancetechnique such that the sample is split at each stageinto two nonoverlappingsubgroupsthat provide the greatest reduction inthe unexplainedsum of squaresremainingfrom the previous stage. There isno stipulation that the splits be symmetrical at every stage or that they pro-duce subgroups of equal size; rather, the progressive breakdowns dependentirely on cumulative self-contained contributions to explanatory powerwithin each "branch" of the unfolding array. Data sets of approximately1,000 are needed for meaningfulapplication-a test our sample just meets.The process terminates when the subgroups involved diminish to a pre-specifiedsize or the extra reduction in unexplainedsum of squaresfrom thebest available additional new split falls below a prespecified evel.41The re-sult is a series of partitions of the sample that indicate which independentvariables have the most powerful influences, and in which order. Clearlyvisible in the emergingdisplay, therefore, s a useful rankingof the relevantimpacts as well as a map of their interactions.A variety of AID runs weremade in the investigation as precursors othe cross-classificationanalyses, and those findingsguided in largepart ourselection of particular relationships to explore in detail. For present pur-poses, the profilesof three of those runs should suffice to tie the threads ofthe investment behaviorstory together. They treat, as dependentvariables,(1) the percentage of the individual investor's portfolio he reports as com-mitted to "income"securities; (2) the fraction of his actual total transac-tions over the 7-year study interval which appear in the file as "solicited"trades; and (3) the average annual return he believes is attainable from hisportfolio. The first of these is an effective proxy for basic investment ob-jectives and philosophy, given its documented high degree of associationwith dividend goal ratings (positive), short-term capital gains emphasis(negative), and expressedattitudes toward risk taking (negative). The sec-ond encapsulates the essence of differences n security-analyticand decision-

41. We established these criteria to be (a) a minimum subgroup of 25, and (b) animprovement in the total R2 of .004 from a new split. Both are somewhat arbitrary andcontain trappings of art as much as of science.



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327 InvestmentStrategyand Behaviormaking styles within the sample. And the third is a central element in theinvestor's evaluation of his surrounding market environment and oppor-tunities. Figures 1-3 convey the findings. Within each of the arrays, suc-cessive splits on the independent variablesare plotted from left to right.With regardto the income-securitycomponentof the portfolio (fig. 1),we see-in harmonywith our previous discussions-that the most powerfulinfluence on the allocation decision turns out to be the investor's age. Themean for the full sampleis 41%0, ut the best single basis of predictionfor aparticular individual is the information as to whether or not he has reachedhis fifty-fifth birthday.42 nvestors of that age or older have 51%oof theirequity investments devoted to instruments chosen primarily for their divi-dend-generating features, while the below-55 contingent reports a corre-spondingmean of just 28%0.Further, within both groups,the next best pre-dictor of the individual observations is also age-a breakdownaccordingtothat variable providing, on the second round, again the greatest reductionin the unexplainedvariance eft over from the initial division. In the resultingfour-way split, the income-security percentages are seen to rise steadilywith age.Beyond that point, the splits become asymmetric. Successive searchesfor predictor variables begin to bring in additional elements of personalcircumstancesand then of differences in characteristic investment styles.Thus, for individuals who are 65 or older, family size has an effect on strat-egy. Single investors in that bracket seek income moreheavily than do per-sons whose families still contain at least one other member.This is, in somerespects,counterto what we might anticipateat lowerages and is most likelya pension and/or social security phenomenon-that is, if there is a spousewho is either drawingfederal benefits or has an additional private pensionfrom previous employment, the relative current investment-income needof the combinedhousehold is reduced.43Within the group having families,those who areemployed are, reasonably,less concernedwith investment in-come than those who are not."In both the 45-54 and 55-64 subsamplesfemale investors concentratemore on income securities than do their male counterparts. One suspectsthat this also is as much an income-related as a sex-related response,how-ever, given that the majority of the female investors involved are unem-ployed.45For males of both age brackets subsequent discriminations along

42. I.e., there is no other two-way division of the sample along any dimension ofinvestor personal circumstances that would produce groups having as low a total of within-group sum-of-squares of deviations of the dependent variable.43. Or, alternatively, the spouse may be younger than 65 and thereby still activelyemployed.44. The fact that income level does not show up overtly here as a predictor variableis almost certainly accounted for by other personal-situation attributes (e.g., employmentstatus) acting as proxies-and providing cleaner two-way distinctions to which the AIDalgorithm's variance analysis is more receptive.45. And, again, the AID procedure apparently found sex a more congenial variableon which to split the sample.



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329 InvestmentStrategyand Behaviorthe lines of investment approachescontain some additional modest predic-tive content. The "fundamental"analysts, and those who spend very littletime each month on managing their portfolios, appear to gravitate towardincome securities. Evidence that no such distinctions are helpful in connec-tion with females fits the observation that female investors display consid-erableuniformityin modes of decisionand analysis across the sample. Final-ly, in the below-45 age category, well-diversified nvestors of both sexes arethe ones whose portfolios are directed most intensively toward dividendreturns, a joint reflectionno doubt of associated personal-riskpreferences.Further efforts at subdividing the various groups failed to meet imposedminimumstandardsof incrementalpredictive power, and all were thereforeterminated as shown. The resulting R2 of .223 confirms the inherent diffi-culty in attempting to explain a large fraction of individual behavior differ-ences with socioeconomicattributes, but the indicated profile does providea coherent identification of the hierarchy of major factors at work, as wellas the successively finersegmentations which occur within the broadgroup-ings. All appear consistent with the messages from the cross-classificationanalyses.A similarassessmentapplies to the interactionsdepicted in figure 2-where the dependent variable is what we have termed "solicited" transac-tions volume, definedover the 7-year study interval immediately precedingour questionnairemailing. Aggregate explanatory performancein this in-stance is still more modest, as might be anticipated from both our previousexpressionsof concernabout the rigor of the accountexecutives'designationsof the trades and the sense from the cross-classifications hat the details ofthe individual decision-makingprocess have a more substantial stochasticflavor than do either the underlying objectives or the eventual portfoliosassembled. The smaller number of successful partitions recordedattest tothese phenomena.Nonetheless, the distinctions which are made buttress theearlier findings and suggest some additional patterns that have logicalappeal.The most potent influence on behavior is once again age. Deviationsfrom the full-sample mean of 31% (i.e., approximately a third of the totaltrades observed were in apparentresponseto account executive advice) arebest explained by dividing the surv

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Patterns of Investment Strategy anInvestment Strategy and Behavior Among Individual Investorsd Behavior Among Individual Investors