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Borsa _Istanbul Review 17-2 (2017) 97e110

http://www.elsevier.com/journals/borsa-istanbul-review/2214-8450

Full Length Article

Heuristics and stock buying decision: Evidence from Malaysian andPakistani stock markets

Habib Hussain Khan a, Iram Naz b,*, Fiza Qureshi c, Abdul Ghafoor d

a Faculty of Management Sciences, International Islamic University, Islamabad, Pakistanb Faculty of Management Science, Capital University of Science & Technology, Islamabad, Pakistan

c Institute of Business Administration, University of Sindh Jamshoro, Pakistand Department of Finance and Banking, Faculty of Business and Accountancy, University of Malaya, Malaysia

Received 8 June 2016; revised 6 December 2016; accepted 26 December 2016

Available online 6 January 2017

Abstract

Applying both qualitative and quantitative approaches, we examine whether or not investors fall prey to three heuristics; namely, anchoringand adjustment, representativeness, and availability, while investing in stocks. We also compare investors' vulnerability to these heuristics basedon their economic association, their type and demographic factors such as income, education and experience. For the data collection, a self-constructed questionnaire was administered to investors in the Malaysian and Pakistani stock exchanges. Data has been analyzed throughdescription, correlation and regression analysis. The results indicate that all three heuristics are likely to affect the investors' stock buyingdecisions. The effect of heuristics is similar across the sample countries, the type of investors, and the income groups. However, the investorswith a higher level of education and more experience are less likely to be affected by the heuristics.Copyright © 2017, Borsa _Istanbul Anonim Sirketi. Production and hosting by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

JEL classification: G02; G11; G19

Keywords: Heuristics; Anchoring and adjustment; Availability; Representativeness; Stock buying decisions; Malaysia; Pakistan

1. Introduction

The mental shortcuts in the “decision making” process, asopposed to a thorough information gathering and analysis, arereferred to as “heuristics”. Although heuristics can be helpfulin many situations, they often lead to biased decisions(Tversky & Kahneman, 1974). The use of heuristics and theireffects on financial decision making is well recognized inbehavioral economics/finance literature [see for example(Bernard & Thomas, 1989; De Bondt & Thaler, 1985; DeBondt & Thaler, 1990)]. The literature provides many

* Corresponding author.

E-mail addresses: habib.suduzai@gmail.com (H.H. Khan), iram_iiui@

live.com (I. Naz), fqureshi83@gmail.com (F. Qureshi), a.ghafoor44@gmail.

com (A. Ghafoor).

Peer review under responsibility of Borsa _Istanbul Anonim Sirketi.

http://dx.doi.org/10.1016/j.bir.2016.12.002

2214-8450/Copyright © 2017, Borsa _Istanbul Anonim Sirketi. Production and hos

license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

examples of poor decision making, such as selling the winnerstoo early and holding the losers for too long, excessive trading(Odean, 1998), and under-diversification (Goetzmann &Kumar, 2008). These behaviors (anomalies) are consideredto be against the assumptions of the traditional finance theory.However, no satisfactory explanation of why such behaviorsexist in the markets is given by the traditional theories. Thetheory of behavioral finance, on the other hand, attempts toprovide an understanding of the fundamental motivationsbehind such irregular market patterns (Subrahmanyam, 2008).

The studies in the context of the stock market and behav-ioral biases show that investors are greatly influenced by theirbehavioral characteristics. Ariely, Loewenstein, and Prelec(2006), for instance, argue that the judgment of the funda-mental values of assets is a tough task, so investors are likelyto value their assets in relative terms, and mostly they becomeanchored to the previous buying prices. Similarly, Barber,

ting by Elsevier B.V. This is an open access article under the CC BY-NC-ND

98 H.H. Khan et al. / Borsa _Istanbul Review 17-2 (2017) 97e110

Odean, and Zhu (2009) find that the investors are likely to buy“attention grabbing” or “in news” stocks because these stocksare easy to recall. Moreover, the investors tend to buy previ-ously owned stocks because they can easily recall them andalso have some information about them.

Although the literature recognizes the role of heuristics inbuy/sell decisions, the focus of the studies has mostly been onthe developed markets. A direct linkage between heuristicsand stock buying decisions has not been established sodistinctively in the developing countries. The economies in thedeveloping countries differ from those in the developedcountries in many aspects, such as political stability, and lawand order situations, technological developments, the use ofinformation technology, the financial structure, the incomelevel and education. Similarly, stock markets and investors arelikely to differ between the developing and developed coun-tries. Investors' attitudes and behaviors are shaped by envi-ronmental factors and it is likely that such behaviors arereflected in their decision making: for instance, Pompian(2006) suggests that the education is an important tool toovercome heuristics and biases. Thus, the behavioral biasesmay work differently due to differences in education levelsbetween developed and developing countries. Moreover, theearlier studies in behavioral finance have mostly focused on asingle heuristic and considered it to be operating indepen-dently. Yet developments in the behavioral decision theoryspecify that different heuristics often operate collectively andinfluence decisions and predictions (Czaczkes & Ganzach,1996; Ganzach & Krantz, 1990, 1991). In terms of themethodology, most of the studies in behavioral finance use aqualitative approach to examine the influence of heuristics orbiases on financial decisions; however, such findings have notbeen tested using statistical methods.

In this study, we examine three heuristics (anchoring andadjustment, representativeness and availability) in the contextof Malaysian and Pakistani stock markets. We use bothqualitative and quantitative approaches to study (i) the influ-ence of these heuristics on investors' stock buying decisions,(ii) the differences between Malaysian and Pakistani investorsin their susceptibility to these biases, (iii), whether the heu-ristics affect investors differently when they make buyingdecisions for themselves and/or for their clients, and (iv) therole of demographic factors such as education, experience andincome level on investors' vulnerability to these heuristics.

Based on a mixed approach (qualitative and quantitative),this study contributes towards the literature on behavioralfinance in terms of its context (developing countries) andmethodological approach. The study has implications forfinancial decision makers in Malaysia and Pakistan (such asprivate investors, financial brokers, fund managers, andfinancial consultants) because the knowledge of relevant bia-ses can prevent decision makers from falling prey to thesebiases. To collect the data, a self-constructed questionnaire isadministered to the investors in the Malaysian and Pakistanistock exchanges. The results show that all three heuristics arelikely to influence the investors' stock buying decisions, andthis influence is similar across the sample countries, the type

of investors, and the income groups. However, investors withmore experience and more education are less affected. In thefollowing section we briefly discuss these heuristics and theireffect on investors' decisions.

1.1. Anchoring and adjustment

Anchoring and adjustment is a cognitive heuristic that arisesout of people's tendency to estimate by starting from an initialguess and then making adjustments to the initial guess in orderto arrive at the final estimate. The initial guess “anchor” maycome from a variety of sources, such as the computation, agiven value, the current value or the historical averages.Regardless of the source of the anchor, the adjustments up ordown to reach the final estimates are insufficient. Use of suchestimates in financial decision making therefore leads the in-vestors to deviate from neo-classically prescribed “rational”norms. Anchoring and adjustment can thus lead the investors tothe following consequences. Firstly, while making generalmarket forecasts, the investors are likely stay “anchored” to thecurrent market values and stay too close to them. Secondly, the“anchor” does not allow the investors or the security analysts toadjust to the new information, and they continue adheringclosely to the original estimates. Thirdly, the current levels ofthe returns are used as “anchors” to forecast the rise or fall inthe percentage values of an asset class. Fourthly, the currenteconomic state of certain countries or companies may serve asthe “anchor” for future prospects. Anchoring is a very commonbias, applying to many areas of finance and business decisionmaking, so investors and wealth management practitionersneed to be keenly aware of this behavior and its effects.

1.2. Representativeness

Representativeness is a cognitive heuristic that refers topeople's tendency to consider a characteristic to be the repre-sentative of the whole of the phenomenon regardless ofwhether the said characteristic relates to the phenomenon ornot. Two primary interpretations of representativeness biasapply especially to individual investors: first, base rate neglectand second, sample size neglect. Base rate neglect refers toinvestors' tendency to contextualize the venture in a way that iseasy to understand, when they are judging the soundness of acompany for investment purposes. However, while making thejudgment they are likely to ignore other related factors whichmay affect the value of the investment. The reason for relyingon such stereotypes is that investors consider it as an alter-native to the required research to evaluate the investment.

Sample size neglect refers to investors' tendency to basetheir judgment on an inadequate sample of data whileanalyzing a particular investment. They incorrectly considerthe small sample size as being representative of the population.This phenomenon is called the law of small numbers. Althoughsuch numbers may reflect the current trend they cannotdescribe the properties of the whole population. Thus both baserate neglect and sample size neglect can lead investors to makeerroneous investment or disinvestment decisions.

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1.3. Availability

Availability is a cognitive heuristic which refers to thetendency to rely on already available information. Accordingto Tversky and Kahneman (1974), people rely on the ease withwhich past experiences or information can be brought to mindto assess the probability of an event. Availability is found to berelevant to the stock market: Chiodo, Guidolin, Owyang, andShimoji (2003) have found that it can cause under-reactionor overreaction in expectations and thus in the asset prices.Availability can be divided into four types: retrievability,categorization, narrow range of experience, and resonance.Retrievability refers to investors' tendency to consider easilyretrievable ideas as the most credible, although such ideas maynot in fact be the most reliable source of information forevaluating the investment. Categorization relates to investors'tendency to match the new information to a certain reference:in other words, they try to fit the new information into theexisting classification of such information. The narrow rangeof experience refers to the restrictive set of information whichis used as a frame of reference to make a judgment about thefuture course of an object/investment. Finally, resonance re-lates to the tendency by individuals to look for the situationsthat match their own personal characteristics; such behaviorscan be very dangerous in the context of investment decisions.

The rest of the study is organized as follows: Section 2discusses the relevant literature on the anchoring and adjust-ment, representativeness and availability heuristics, whileSection 3 is dedicated to the methodology and construction ofthe instrument. In Section 4, we discuss the results from thedescriptive and regression. Finally, Section 5 concludes thestudy with a discussion on the implications and the directionfor future research.

2. Literature review

The idea of heuristics was introduced by Tversky andKahneman (1974) who suggest that individuals tend to usemental shortcuts or rule of thumb strategies in “decisionmaking” under situations of uncertainty. These mental short-cuts or rule of thumb strategies are called “heuristics”. Theseheuristics turn complex situations into simple cognitive oper-ations. In many cases such “mental short-cuts” may prove tobe helpful in dealing with complex and uncertain situations;however there are greater chances that they lead to biaseddecisions. Some recent contributions highlight the usefulnessof the heuristics however, researchers are far from reaching aconsensus with respect to the effects of heuristics on decisionmaking [see for instance (Carmines & D'amico, 2015;Gigerenzer & Gaissmaier, 2011; Kurz-Milcke & Gigerenzer,2007)].1 In the following section, we discuss the findings from

1 There is also another related stream of research that examines the innate

characteristics of the managers and their influence on firms' policies and de-

cision making. See for instance (Bertrand & Schoar, 2003) and (Naz, Shah, &Kutan, 2016).

most relevant studies on three heuristics e i.e. anchoring andadjustment, representativeness and availability.

The literature on anchoring and adjustment in context ofcapital market is not matured, however, there are certain areaswhere anchoring is found to have a significant role for thefinancial decisions. For example, Degeorge, Patel, andZeckhauser (1999) find that the executives aim to exceedsalient earning per share (EPS) thresholds, thus, a specificlevel of EPS serves as an anchor for the executives which, inturn influences their decision. Moreover, the investors are notready to bid the stock prices high enough when the stocks areat or near their peak historical prices because they areanchored to the historical highest (George & Hwang, 2004).Similarly, Cen, Hilary, Wei, and Zhang (2010) and Cen,Hilary, and Wei (2013) observe that while estimating thefuture success of firm, the investors are anchored to historicalaverages. They also find that for the firms with high industrymedian-adjusted forecasted earnings per share, stock returnshappen to be higher than for firms with low industry median-adjusted forecasted earnings per share. This phenomenon isreferred to as the cross-sectional anchoring of forecastedearnings per share effect. Johnson, Schnytzer, and Liu (2009)find anchoring and adjustment in financial market; in com-parison to the horserace. They explain that the advantage givenby horse barrier position serves as anchor for horse betters'probability judgment. Kaustia, Alho, and Puttonen (2008) findsignificant anchoring effects in long-term future stock returnestimates in Scandinavian stock market. Williams (2010) findsthat stock prices depend on four things one being anchoringlevel. Campbell and Sharpe (2009) identify anchoring effect ofhistorical values in predictions of macroeconomic data such asthe consumer price index or non-farm payroll employment byprofessionals, resulting in significant forecast errors. Corpo-rate acquisitions are also found to be affected by the anchoringbias (Baker, Pan, & Wurgler, 2009). Anchoring and adjust-ment affects the extremity of earning forecasts (Amir &Ganzach, 1998).

Anchoring has also been reported to influence the varioustypes of decisions in many different contexts. These includejudicial sentencing decisions (Englich, Mussweiler, & Strack,2006), personal injury verdicts (Chapman & Bornstein, 1996),the estimation of the likelihood of diseases (Brewer, Chapman,Schwartz, & Bergus, 2007), job performance evaluation(Latham, Budworth, Yanar, & Whyte, 2008), judges' rankingsin the competitions (Ginsburgh & Van Ours, 2003), and realestate acquisitions (Northcraft & Neale, 1987). In addition,anchoring has been shown to influence intuitive numericalestimations (Wilson, Houston, Etling, & Brekke, 1996),probability estimates (Plous, 1989), estimations of samplemeans and standard deviations (Lovie, 1985) and estimates ofconfidence intervals (Block & Harper, 1991), sales predictions(Hogarth, 1981), Bayesian updating tasks (Lopes, 1991),utility assessments (Schkade & Johnson, 1989), risk assess-ments (Lichtenstein, Slovic, Fischhoff, Layman, & Combs,1978), preferences of gambles (Lichtenstein & Slovic,1971), perception of deception and information leakage(Zuckerman, Koestner, Colella, & Alton, 1984), negotiation

2 At the initial stage of data collection, the sample consisted of five

Southeast Asian countries (Indonesia, India, Malaysia, Pakistan and Thailand).

However, we had physical access to stock markets in Malaysia and Pakistan

only. Investors in rest of the countries i.e. Indonesia, India and Thailand were

requested through email to fill up the questionnaire. However, we did not

receive any response from those investors despite several reminders.

Accordingly, the data used in analysis comes from Malaysia and Pakistan only.3 In case of Malaysia, some questions were slightly changed to incorporate

local currency in numerical examples.

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outcomes (Ritov, 1996), and choices between product cate-gories (Davis, Hoch, & Ragsdale, 1986). Although we did notfind a study that directly linked anchoring bias to investors'decision to invest in stocks but studies on anchoring and stockmarket do suggest that it can influence such decision.

The studies on representativeness bias are very limited ascompared to the anchoring and adjustment bias. However,there is some literature that clearly indicates the role ofrepresentativeness in financial decisions. For example,Johnson (1983) studies the use of representativeness heuristicin judgmental predictions of corporate bankruptcy and findsthat the bankruptcy probability judgments are governed by theassessed similarity of the corporate financial data. Further, theforms of representativeness such as the base rate neglect andthe sample size neglect are tested by Jacobs and Potenza(1991). They find that the judgment biases displayed by theadults are specific to the social domain and that they developover time. However, greater use of the base rates develops atthe same time in the object domain. Similarly, Cox and Mouw(1992) find that the representativeness leads to the misunder-standing of statistical concepts even when participants canwitness their unreasonable approach towards the situation.Even after changing answers to incorporate appropriate in-formation related to situation, the post-test responses revealedthe presence of representativeness. Pham (1998) discovers that“how do I feel about it heuristics” to be present in the decisionmaking. This heuristic arises when a representative of thetarget exists in mind and it generates feelings.

Kahneman, Slovic, and Tversky (1982) apply the repre-sentativeness bias to the world of sports, these concepts canalso be translated to the financial decision making (Pompian,2006). For instance, the concept of permitting the game “togo longer” in order to increase the probability that the strongerplayer wins can also apply to the investing, where it is calledtime diversification, which refers to the idea that the investorsshould spread their assets across ventures operating accordingto a variety of market cycles, giving their allocations plenty oftime to work properly. Time diversification helps reduce therisk that an investor will be caught entering or abandoning aparticular investment or category at a disadvantageous point inthe economic cycle. It is particularly relevant with regard tothe highly volatile investments, such as stocks and long-termbonds, whose prices can fluctuate in the short term. Holdingonto these assets for longer periods of time can soften theeffects of such fluctuations.

The availability heuristic, a common cognitive strategy inhuman decision making, provides an example of how theprocess of making a judgment influences the evaluation ofrelevant events. People's judgments of the probability and thefrequency of the events are based on the ease with which theexamples of those events come to the mind (Tversky &Kahneman, 1973). The availability heuristic is reported toaffect the decision making in different spheres of the life. Forexample, Folkes (1988) finds that the availability influencesthe judgments about the product performance. Similarly,Barber and Odean (2002) show that the investors tend to investin the attention grabbing stocks because, choosing a suitable

stock or group of stocks among thousands of stocks in themarket needs considerable effort and time. Investors however,tend to limit their search to only attention grabbing stocks.They also find that the purchase of stocks was nearly twice ascompared to normal trading days during the high trading days.Moreover, the investors tend to buy more stocks of companiesthat are in the news. Their most relevant finding was that, theattention grabbing stocks which investors buy due to theavailability bias did not beat the market and they never earnabnormal profits. Similarly, Barber and Odean (2002) illustratea direct practical application of availability bias in individualfinance. They show that the investors tend to deviate fromrationality because they lack time, resources and the skills toprocess huge volume of data that ought to contextualize a truly“rational” stock purchase. Information that is literally avail-able to investors simply isn't always cognitively available.When pertinent information isn't available in this latter,practical sense, decisions are ultimately flawed.

Overall, there is enough evidence to suggest that theanchoring and adjustment, the representativeness and theavailability heuristics likely to affect the investors whilemaking the stock buying decisions. However, the literature onheuristics is limited with respect to its applications to thedeveloping economies. As discussed in the introduction sec-tion, it is possible that the biases my affect the investorsdifferently in the developing economies. Furthermore, most ofthe studies in behavioral finance use qualitative approaches toexamine the influence of heuristics/biases on financial de-cisions, however, such findings have not been tested usingstatistical methods. This study bridges this gap by applyingboth qualitative and quantitative approach to these heuristicsin context of Malaysian and Pakistani stock exchanges.

3. Methodology

3.1. Population, sample, analysis technique

Active investors and brokers in the Malaysian and Pakistanistock markets constitute population of this study.2 Consideringthe typical nature of population, convenient sampling has beenused and a total of 1000 questionnaires are served to investorsin the Malaysian3 and Pakistani Stock Exchanges. We receivedapproximately 300 responses (160 from Pakistan and 140 fromMalaysia), however; we dropped some of the questionnairesfor incompleteness. A total of 240 usable questionnaires havebeen used in the data analysis (144 from Pakistan and 96 fromMalaysia). Items' descriptive analysis has been used as main

Table 1

Percentage responses to questions on anchoring and adjustment.

***SDA=Strongly Disagree, DA= Disagree, N=Neutral, A=Agree, SA=Strongly Agree

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approach to analyze the data whereas, the correlation and theregression analyses have been used as supplementary approachto support the findings from the descriptive analysis.

3.2. Instrument for data collection

To capture the presence or absence of the heuristics, wedevelop a questionnaire with the help of and inspiration fromPompian (2006). In addition to three heuristics (anchoring andadjustment, representativeness, and availability), we alsoconstruct a variable for stock buying decision which measuresthe investors' inclination to invest in the stocks as compared toother investment opportunities i.e. bonds etc. The question-naire was pilot tested in two stages and checked for validityand reliability so that it can be used for statistical analysis. Thegeneralizability of this questionnaire may however be limitedbecause of the specific characteristics of the population.4 Thequestionnaire consists of 27 questions,5 each item captures theresponses on 5 point Likert scale (from strongly disagree tostrongly agree).6

4. Data analysis

Items' descriptive analysis is conducted as a primary anal-ysis technique to explain the most frequent responses of the

4 For example it may only be used to analyze the buying behavior of in-

vestors in stock market.5 These 27 questions are in addition to those asked to capture demographic

aspects such as age, gender, education, income, and experience.6 The questionnaire is appended at the end of this study.

investors to different aspects of the heuristics. As a robustnesscheck, the data has also been analyzed through correlation andregression. The descriptive analysis of the items shows thefrequency of investors' responses to a particular question,which can be interpreted in term of the presence or the absenceof a particular bias. For example, if more frequent responseson a five point Likert scale (from strongly disagree to stronglyagree) are 4 or 5, it can be interpreted as the presence of aparticular aspect of bias. The descriptive analysis of each itemin the questionnaire is presented in the following section.

4.1. Items' descriptive analysis

The percentage of responses to each question for anchoringand adjustment from both Malaysia and Pakistan are reportedin Table 1. A particular heuristic is said to be “present amonginvestors” if sum of the responses for both strongly agree andagree dominates sum of responses for strongly disagree anddisagree.

The first question is posed to check the tendency of theinvestors to make general market forecasts that are too close tocurrent levels. Agree versus disagree numbers (%) are 56.2and 6.2 respectively for Malaysia whereas for Pakistan theyare 52.8 and 9. The second question measures the investors'tendency to anchor their forecasts on historical minimum ormaximum prices. Responses for this question are 96.9%(agree) versus 3.1% (disagree) for Malaysia and 91% and 9%for Pakistan. Third question gauges the tendency of investorsto make a forecast of the percentage that a particular assetclass might rise or fall based on the current level of returns,and to capture the investors' tendency to anchor their forecasts

102 H.H. Khan et al. / Borsa _Istanbul Review 17-2 (2017) 97e110

for prices on the most recent historical percentage increase ordecrease. For Malaysia, the responses are 76.1 (agree) versus14.5 (disagree), while for Pakistan these are 80.5 versus 13.4.The investors are likely to anchor on the current economicconditions of a particular country; question four is asked tomeasure this tendency. The responses for Malaysia are 51versus 9.3 and 49.3 versus 9.8 for Pakistan. The investors, whoare biased with anchoring and adjustment, tend to anchor onhistorical maximum or minimum stock prices; question fivemeasures this phenomenon. The responses to this question are13.5 versus 46.8 for Malaysia and 47.3 versus 13.2 forPakistan. In question six, five point measurement scale in-cludes two options (4 and 5) very close to the given value of900,000 representing high level of anchoring, while otheroptions were fairly away from this value with option 1 exactly10% less of this value. In case of Malaysia the responses foroption “4” and “5” are 73.9 versus 21.9 for option 1 and 2; forPakistan, these responses are 75.7 versus 20.2 respectively.Question seven measures the phenomena that the investors,who are exposed to anchoring and adjustment, tend to anchorto current economic conditions of a particular company. Theresponses for this question are 88.5 versus 7.3 in case ofMalaysia and 89.6 versus 7 in case of Pakistan.

Table 2 shows investors' responses to each question forrepresentativeness from Malaysia and Pakistan. Interpretationof responses is same as in case of anchoring and adjustment.Question 8 measures the phenomena that while judging thelikelihood of a particular investment outcome, investors often

Table 2

Percentage responses to questions on representativeness.

***SDA=Strongly Disagree, DA= Disagree, N=Neutral, A=Agree, SA=Strongly Agree

fail to accurately consider the sample size of the data onwhich they base their judgments. The responses are 84.4versus 9.4 and 84 versus 8.3 for Malaysia and Pakistanrespectively. Similarly the investors, who are biased withrepresentativeness, tend to neglect the sample size whileanalyzing the performance of stocks, questions 9 measuresthis phenomenon. Responses are 83.4 versus 5.2 and 87.5versus 6.9 for Malaysia and Pakistan respectively. Question10 measures the fact that representativeness heuristic canlead investors to ignore the base reality and consider a givencharacteristic as representativeness of whole the scenario.Responses to this question are 87.5 versus 7.3 for Malaysiaand 84 versus 6.3 for Pakistan. Question 11 gauges the in-vestors' tendency to determine the potential success of aninvestment in a company by contextualizing the venture in afamiliar, easy-to-understand classification scheme is knownas base rate neglect. For Malaysia, the responses are 57.2versus 6.3 whereas for Pakistan these are 61.9 versus 3.5.Question 12 measures the investors' tendency to ignore thebase reality. Responses for Malaysia are 86.5 versus 7.3while for Pakistan, responses are 86.1 and 9.6. Investors'tendency to show base rate neglect was further confirmed byasking the question 13. Responses are 81.3 versus 13.5 and80.6 versus 14.6 for Malaysia and Pakistan respectively. Thesample size neglect can lead the investors to wrong conclu-sion about the performance of analysts or money managersby taking too narrow sample period of their performancerecord; question 14 verifies this aspect. For Malaysia and

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Pakistan 22.9 versus 7.3 and 26.4 versus 6.3 responses arereceived respectively.

Investors' responses to each question for availability fromMalaysia and Pakistan are shown in Table 3. Investors' ten-dency to rely on readily available knowledge rather thanexamine other alternatives or procedures is known as avail-ability bias. Four types of availability biases apply most toinvestors: retrievability, narrow range of experience, catego-rization and resonance. Retrievability refers to the ease withwhich the information can be recalled; question 15 measuresretrievability. Responses are 91.7 versus 4.1 and 90.3 versus6.3 for Malaysia and Pakistan respectively. Investors' tendencyto categorize or call for information that matches a certainreference is known as categorization and this is measuredthrough question 16. Responses for this question are 89.6versus 9.3 for Malaysia and 82.4 versus 4 for Pakistan.Question 17 measures the extent to which certain, given sit-uations matches with individuals' own, personal situations canalso influence judgment and this is known as resonance. ForMalaysia, the responses are 84.4 versus 6.2 while for Pakistanthese are 81.3 versus 12.5. When a person possesses a toorestrictive frame of reference from which to formulate anobjective estimate, then narrow range of experience bias oftenresults; question 18 gauges the narrow range of experience.81.3 versus 11.5 are the responses for Malaysia and 84.1versus 7.6 are ones for Pakistan. Presence of narrow range ofexperience among investors is further confirmed by askingquestion 19. Responses from Malaysia and Pakistan are 11.4versus 19.8 and 17.4 versus 13.2 respectively. Finally, question20 confirms the presence of narrow range of experience among

Table 3

Percentage responses to questions on availability.

***SDA=Strongly Disagree, DA= Disagree, N=Neutral, A=Agree, SA=Strongly Agree

investors for which responses are 89.7 versus 8.3 and 86.8versus 6.9 for Malaysia and Pakistan.

Overall, the sum of “agree” and “strongly agree” responsesdominates the sum of “disagree” and “strongly disagree”options in nearly all of the questions for each of the heuristics.The respondents' agreement in general to all questions sup-ports the presence of the heuristics. The questions are posedto gauge whether or not the investors behave in predictedways under anchoring and adjustment, representativeness andavailability heuristics. Their agreement to such statementsthus suggests that they are likely to make decisions which areunder the influence of these heuristics. Moreover, the re-sponses from the investors in Malaysia and Pakistan are onaverage similar, therefore we conclude that investors'vulnerability to these three heuristics is similar across thesetwo countries.

4.2. Correlation and regression analysis

In order to support the results of descriptive analysis, wealso perform the correlation and regression analyses. For thispurpose, we take the median of responses from each respon-dent for each variable. For instance, there are 7 questions(items) for anchoring and adjustment, each respondent an-swers to all the 7 questions on a scale of 1e5. The median ofthese 7 responses from a single respondent constitutes theanchoring and adjustment variable. Thus, we have as manyobservations of anchoring and adjustment as the number ofrespondents. A similar procedure has been applied to eachvariable i.e. representativeness, availability and stock buying

Table 4

Correlation analysis.

Variables (1) (2) (3) (4)

(1) Anchoring and adjustment 1 e e e

(2) Representativeness 0.734** 1 e e

(3)Availability 0.676** 0.653** 1 e(4) Stock buying decision 0.319** 0.472** 0.526** 1

Note: The table shows the correlation among (1) Anchoring and adjustment,

(2) Representativeness, (3) Availability, and (4) Stock buying decision. Sub-

scripts “**” and “*” show the significance of relationships at 5% and 10%

levels respectively.

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decision to compute the data for correlation and regressionanalysis.7 Results for the correlation analysis are reported inTable 4. The last row shows the relationship of stock buyingdecision with anchoring and adjustment, representativenessand the availability heuristics. These relationships are signif-icant implying that heuristics are related to the stock buyingdecision.

In the next step, we perform the regression analysis basedon Equation (1) which represents the stock buying decision asa function of three heuristics. The abbreviations SBD, ANC,REP and AVA respectively represent the stock buying decision,anchoring and adjustment, representativeness and the avail-ability; l1, l2 and l3 are the regression coefficients for threeheuristics, and ε is the error term.

SBD¼ l0 þ l1ANCþ l2REPþ l3AVAþ ε ð1ÞThe estimation results from regression analysis are reported

in Table 5. Model 1 represents the main estimation modelwhere the stock buying decision is regressed on three heuris-tics without considering the interactions of investors' type andthe demographic factors such as country, level of education,level of income, and experience. The coefficients on all threeheuristics are significant implying that the investors areinfluenced by the heuristics while investing the in the stocks.Although, it is difficult to interpret the magnitude and thedirection of coefficients for the heuristics, the significance ofthese coefficients can be interpreted in term of the likelihoodof the investors to make less than optimal decision due to thepresence of the heuristics.

4.3. The interactive role of demographic characteristics

In addition to main regression, we estimate five moremodels to observe if the impact of heuristics on stock buyingdecision differs across, a) the sample countries b) the types ofinvestors c) the education of the investors, d) the income leveland, e) the level of experience. The details of these modelsalong with the discussion of the results are given next.

7 We are thankful to the anonymous referees for the suggestion. Questions

12 to 27 are intended to capture the investors' stock buying behavior. Median

value of these questions gives the variable “stock buying decision” which has

been used as dependent variable in regression analysis.

SBD¼ l0 þ l1ANCþ l2REPþ l3AVAþ g1ANC*DðCÞþ g2REP*DðCÞ þ g3AVA*DðCÞ þ ε ð2Þ

Equation (2) is structured to capture the differences amonginvestors with respect to their vulnerability to the heuristicsbased on their economic association. Where, DðCÞ is a dummyvariable which equals 1 if the respondent is from Pakistan and0 otherwise; g1, g2 and g3 are coefficients on the interactionterm between the dummy variable and heuristics. The esti-mation results from Equation (2) are reported under model 2 inTable 5. The coefficients on all interaction terms are insig-nificant, indicating that the impact of heuristics on investors'buying behavior is similar for investors across both thecountries. Subsequently, the Equation (3) is constructed toexamine the impact of heuristics on stock buying decision fordifferent types of investors.

SBD¼ l0 þ l1ANCþ l2REPþ l3AVAþ g1ANC*DðInvÞþ g2REP*DðInvÞ þ g3AVA*DðInvÞ þ ε ð3Þ

where, DðInvÞ is a dummy variable which equals 1 if therespondent is an individual investor (uses his own money) and0 otherwise; g1, g2 and g3 are coefficients on the interactionterm between the dummy variable and heuristics. The resultsfrom Equation (3) are reported under model 3 in Table 5. Thecoefficients on the interaction terms of investors' type withthree heuristics are insignificant suggesting that the heuristicsinfluence the stock buying decisions across of all types ofinvestors. The investors' vulnerability to heuristics acrossdifferent income group is determined through Equation (4).

SBD¼ l0 þ l1ANCþ l2REPþ l3AVAþ g1ANC*DðEduÞþ g2REP*DðEduÞ þ g3AVA*DðEduÞ þ ε

ð4Þ

where, DðEduÞ is a dummy variable which equals 1 if therespondent's education is higher than the median educationlevel and 0 otherwise; g1, g2 and g3 are coefficients on theinteraction term between the dummy variable and heuristics.The results from estimation of Equation (4) are reported undermodel 4. The coefficients on interaction terms are significantwith signs opposite to the ones for the heuristics. Thesefindings imply that the investors with higher level of educationare less influenced by heuristics. The influence of heuristics onstock purchase decision across different income groups hasbeen analyzed through Equation (5).

SBD¼ l0 þ l1ANCþ l2REPþ l3AVAþ g1ANC*DðIncÞþ g2REP*DðIncÞ þ g3AVA*DðIncÞ þ ε ð5Þ

where, DðIncÞ is a dummy variable which equals 1 if the re-spondent's income is higher than the median income and0 otherwise; g1, g2 and g3 are coefficients on the interactionterm between the dummy variable and heuristics. The esti-mation results from Equation (5) are shown under model 5.The coefficients on interactions terms are not significant

Table 5

Regression analysis.

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

Anchoring �0.274*** �0.268** �0.131** �0.235** �0.211** �0.197**

(0.088) (0.125) (0.057) (0.075) (0.089) (0.034)

Representativeness 0.405*** 0.359*** 0.349** 0.316** 0.377** 0.298**

(0.096) (0.149) (0.147) (0.152) (0.109) (0.139)

Availability 0.548*** 0.588*** 0.459*** 0.556*** 0.561*** 0.595***

(0.088) (0.159) (0.140) (0.187) (0.192) (0.174)

Anchoring � Country e �0.017 e e e ee (0.185) e e e e

Representativeness � Country e 0.071 e e e e

e (0.193) e e e eAvailability � Country e �0.050 e e e e

e (0.184) e e e e

Anchoring � Investor Type e e �0.197 e e e

e e (0.193) e e eRepresentativeness � Investor Type e e 0.090 e e e

e e (0.183) e e e

Availability � Investor Type e e 0.112 e e e

e e (0.178) e e eAnchoring � Education e e e 0.109** e e

e e e (0.051) e e

Representativeness � Education e e e �0.210** e e

e e e (0.101) e eAvailability � Education e e e �0.307** e e

e e e (0.148) e e

Anchoring � Income e e e e 0.117 ee e e e (0.133) e

Representativeness � Income e e e e 0.103 e

e e e e (0.137) e

Availability � Income e e e e 0.152 ee e e e (0.108) e

Anchoring � Experience e e e e e 0.128**

e e e e e (0.057)

Representativeness � Experience e e e e e �0.190**

e e e e e (0.088)**

Availability � Experience e e e e e �0.243**

e e e e e (0.119)

Constant 1.432*** 1.413*** 1.441*** 1.378*** 1.339*** 1.355***

(0.263) (0.272) (0.267) (0.475) (0.331) (0.387)

Adjusted R-Squared 0.53 0.71 0.73 0.69 0.75 0.68

No. of Observations 240 240 240 240 240 240

ShapiroeWilk W Test (Probability) 0.073 0.109 0.089 0.136 0.191 0.117

BreuschePagan (Chi Square) 12.05** 11.48** 7.48** 9.13** 9.98** 11.01**

Multicollinearity (VIF) 2.29 18.8 13.5 16.1 21.5 19.2

Note: The table reports the results of regression analysis. Model 1 through 6 show the estimation results corresponding to Equation (1) through (6). The corrected

standard errors are reported in the parenthesis, subscripts ***, ** and * show the significance of relationships at 1%, 5% and 10% levels respectively.

BreuschePagan test rejects the null hypothesis of no heteroscedasticity therefore we use robust standard errors for calculation of z value. Variance inflation factor

(VIF) is high for model 2 to 6, however these models include dummy variables and their products in which case we can ignore the higher values of VIF.

Insignificant values of ShapiroeWilk W Test suggest that the dependent variable is normally distributed.

105H.H. Khan et al. / Borsa _Istanbul Review 17-2 (2017) 97e110

implying that the influence of heuristics is similar across in-vestors with different income levels. Lastly, we constructEquation (6) to examine if the investors with more/lessexperience are affected differently.

SBD¼ l0 þ l1ANCþ l2REPþ l3AVAþ g1ANC*DðExpÞþ g2REP*DðExpÞ þ g3AVA*DðExpÞ þ ε

ð6Þ

where, DðExpÞ is a dummy variable which equals 1 if therespondent's experience is higher than the median years ofexperience and 0 otherwise; g1, g2 and g3 are coefficients onthe interaction term between the dummy variable and heuris-tics. The estimation results for Equation (6) are shown undermodel 6. The coefficients on the interactions terms are sig-nificant and have sings opposite to those on heuristics. Thisimplies that the investors with more experience are less likelyto fall prey to the heuristics.

106 H.H. Khan et al. / Borsa _Istanbul Review 17-2 (2017) 97e110

Overall evidence from correlation and regression analysissupports the results from items' descriptive analysis and weconclude that three heuristics namely anchoring and adjust-ment, representativeness and availability, influence the in-vestors while buying the stocks in the stock market. However,this effect is lesser for the investors with higher level of ed-ucation and higher experience.

5. Conclusion

The influence of heuristics on investors' decisions is wellrecognized in the behavioral finance literature. However, thefocus has mostly been on the developed markets. A directlinkage between heuristics and stock buying decisions has notbeen established so distinctively in the developing countries,as their economies are different from the developed ones inmany aspects, such as political stability, law and order situa-tions, technological developments, the use of informationtechnology, the financial structure, income level and the edu-cation etc. In the same vein, the stock markets and investorsacross developing and developed economies are likely todiffer. Investors' attitudes and behaviors are shaped by envi-ronmental factors and it is likely that such behaviors are re-flected in their decision making. For example, Pompian (2006)considers education is an important tool to overcome heuris-tics and biases. Therefore, the behavioral biases may workdifferently due to differences in education levels betweendeveloped and developing countries. Moreover, early researchin behavioral finance has mostly focused on a single heuristicand considered it to be operating independently. Nevertheless,the developments in behavioral decision theory specify thatdifferent heuristics often operate collectively and influencedecisions and estimations (Czaczkes & Ganzach, 1996;Ganzach & Krantz, 1990, 1991).

In this study, we apply quantitative and qualitative ap-proaches to study the influence of three heuristics (anchoringand adjustment, representativeness and availability) on in-vestors' stock buying decisions in the context of Malaysia andPakistan. In doing so, we examine the differences in in-vestors' vulnerability to these heuristics across the samplecountries and the investor types. We also examine the influ-ence of demographic factors such as education, income andexperience. The data has been collected through a self-constructed questionnaire from investors in the Malaysianand Pakistani stock exchanges. An items descriptive analysishas been used as the main approach to analyze the data, whilecorrelation and regression analyses have been used as asupplementary approach to support the findings from thedescriptive analysis.

The results from the descriptive analysis support the pres-ence of all three heuristics, and are also supported by thecorrelation and regression analyses where all three heuristicsare significantly correlated to and have a strong influence oninvestors' stock buying decisions. The influence of heuristicson stock buying decisions is similar across the sample

countries, the type of investors, and the income groups.However, the investors with a higher level of education andmore experience are less affected by the heuristics.

The implications of these heuristics can be far reaching forinvestors. First, investors who are biased in relation toanchoring and adjustment are more likely to purchase a stockthat may behave against their expectations. For example ifinvestors are anchored to historical high stock prices andexpect the stock to recover, they may continue holding theirlosing stocks for too long (Odean, 1998). Similarly they maybecome anchored to historical percentage increase in pricesand expect a similar trend in the future may lead them tobuying the overvalued stock. Investors can also becomeanchored to historical performance of companies which mayactually deviate from their trends of past performancebecause of certain uncontrollable economic factors. Second,the investors who are exposed to representativeness bias aremore likely to buy a wrong stock for their portfolio. Forexample, they may base their buying decision on insufficientpast data, this may lead them to buy a stock that may not havethe potential to meet their expectation in future (sample sizeneglect). Investor may also fall prey to another type ofrepresentativeness bias (base rate neglect), for example,looking for a long-term investment stock they may end upwith stock that is not in fact a long-term investment (i.e. NewIPO companies). Third, most investors, if asked to identifythe “best” mutual fund company, are likely to select a firmthat engages in heavy advertising. In addition to maintaininga high public relations profile, these firms also “cherry pick”the funds with the best results in their fund lineups, whichmakes this belief more “available” to be recalled. In reality,the companies that manage some of today's highest-performing mutual funds undertake little to no advertising.Investors who overlook these funds in favor of more widelypublicized alternatives may exemplify retrievability/avail-ability bias. Investors will choose investments based on in-formation that is available to them (advertising, suggestionsfrom advisors, friends, etc.) and will not engage in disciplinedresearch or due diligence to verify that the investmentselected is a good one.

Heuristics are also likely to influence other areas of finan-cial decisions making such as investing, financing, assetmanagement, and dividend policy decisions. The study can beextended to cover these areas of corporate finance as well.Moreover, given the limitations of a primary data gatheringtechnique such as questionnaire, it would be worthwhile toconsider an alternative measure such as investors' trading data,to capture the heuristics.

Acknowledgement

We are thankful to the editor Borsa Istanbul Review, theanonymous referees and the participants of PostgraduateConference on Economics, Public Administration and Busi-ness (PCEPAB) for the valuable suggestions and comments.

107H.H. Khan et al. / Borsa _Istanbul Review 17-2 (2017) 97e110

Appendix

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