Heuristics influencing investment decision making and investment performance, evidence from individual investors in vietnam

The result shows that there is only overconfidence bias affecting the investmentdecisions of individual investors at the Ho Chi Minh Stock Exchange among three factors of heuristic inclu

Master Thesis

Heuristics influencing decision making and performance: evidence from individual investors in Vietnam

Nguyen Thi Thanh Thuy

Mbus 3.2

Supervisor: Dr Tran Phuong Thao

I hereby declare that this thesis, to the best of my knowledge and belief, is my ownwork and effort and that is has not been submitted, either in part or whole, anywhere forany award

Information and ideas taken from other sources as cited as such This work has notbeen published

This research would not have been possible without the valuable contribution ofmany people We would like to take this chance to express my great gratitude for theirunderstanding, encouragement, and supports

Firstly, I would like to my deepest appreciation to my thesis supervisor, Dr TranPhuong Thao, for numerous valuable comments and suggestions I am very lucky to haveher supervision as her continuous encouragement has motivated me and made meconfident to finish this research

Then, I would like to thank the faculty of International School of Business (ISB) –University of Economic Ho Chi Minh City I would also like to show my gratitude to

guidelines with regard to statistical analysis techniques

A special appreciation to my friends and colleagues for giving me the soundcomments on my questionnaire as well as instant support regardless day or night Thishelps me a lot in improving the quality of the research

Furthermore, we want to express our gratefulness to my friends working at the HoChi Minh Stock Exchange and securities companies, who help me to arrange interviewsand distribute questionnaires I am also thankful to beneficiary customers whoparticipated in and the survey

Finally, it would be impossible to say enough about my dear parents, myrespectable teachers at University of Economics Ho Chi Minh City and my loved friends.All their understanding, encouragement, and advices help me to overcome the mostdifficult time to complete this research in time

Ho Chi Minh City, June 6th, 2015

LIST OF FIGURES

Figure 2.1 Research model 17

Figure 3.1 Research process 20

Figure 3.2 New research model (revised) 27

LIST OF TABLES Table 3.1 Types of measurement for heuristic variables influencing investment decision making 22

Table 3.2 Types of measurement for individual investors’ decision making 23

Table 3.3 Types of measurement for individual investors’ performance 24

Table 4.1 Descriptive statistic of respondent’s characteristics 31

Table 4.2 Reliability analysis for each factor 35

Table 4.3 Key dimensions, items 37

Table 4.4 Correlation among factors 38

Table 4.5 Regression analysis - Model summary 39

Table 4.6 ANOVA 40

Table 4.7 Regression analysis of variables 41

Table 4.8 Correlation 42

Table 4.9 Regression analysis – Model summary 43

Table 4.10 ANOVA 44

Table 4.11 Regression analysis of variables 44

Table 5.1 Conclusion 49

The main objective of this study is to investigate the heuristic factors influencingindividual investors’ decisions and investment performance at the Ho Chi Minh StockExchange As there are limited studies about behavioral finance in Vietnam, this study isexpected to contribute significantly to the development of this field in Vietnam

The study begins with the existing theories in behavioral finance, based on which,hypotheses are proposed Then, these hypotheses are tested through the questionnairesdistributed to individual investors at the Ho Chi Minh Stock Exchange

The result shows that there is only overconfidence bias affecting the investmentdecisions of individual investors at the Ho Chi Minh Stock Exchange among three factors

of heuristic (including available bias, representativeness bias and overconfidence bias).Moreover, this study also found out the relationship between decision making ofindividual investors and investment performance

Keywords: Heuristic, individual investor, decision making, investment

performance, Vietnam.

CHATER 1: INTRODUCTION

1.1 Background

In the financial markets, investment decisions are commonly made by individualinvestors and fund managers Their investment decisions are often supported by decisiontools such as fundamental analysis, technical analysis and judgment It is assumed thatinformation structure and behavioral factors in the market systematically influenceindividuals’ investment decisions as well as market outcomes (Mutswenje, 2014).However, in reality, investor behaviors are often derived from psychological principles

of decision making to explain why people buy or sell stocks These factors that arenormally known as behavioral factors focused upon how investors interpret and act oninformation to make investment decisions

Much of the economic and financial theories presume that individuals act rationally inthe process of decision making, by taking into account all available information But there isevidence to show repeated patterns of irrationality in the way humans arrive at decisionsand choices when faced with uncertainty (Bernstein, 1996) Behavioral finance, a study ofmarket that draws on psychology, throws light on why people buy or sell stock and whysometimes do not buy or sell at all The most crucial challenge faced by the investors is inthe area of investment decisions The profit made, or losses incurred by an investor can beattributed mainly to his decision-making abilities The fact that even the most prominentand well-educated investors were affected by the collapse of the speculative bubble in the

2008 subprime crisis proved that something was fundamentally missing in the traditionalmodels of rational market behavior (Subash, 2012)

In the behavioral finance discipline, heuristics can be defined as the use ofexperience and practical efforts to answer questions or to improve performance (Fromlet,2001) Raines & Leathers (2011) argue that when faced with uncertainty, people rely onheuristics or rules of thumb to subjectively assess risks of alternatives, which reduces thecomplex tasks of assessing probabilities and predicting values to simpler judgmental

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There are several studies in the literature investigating the relationship betweenheuristics and decision making and performance of individual investor as well Tversky

& Kahneman (1974) conduct the research of judgment under uncertainty, heuristics andbiases Hassan et al (2013) study impacts of affect heuristics, fear and anger on thedecision making of individual investor in a conceptual study In addition, another studyexamines investment behavior and performance of various investor types in Finland’sstock market done by Grinblatt & Keloharju, 2000

1.2 Research problem

In the literature, many papers show that investment decision of investors may beaffected by behavioral finance Thus, many researchers attempt to investigatepsychological and sociological factors such as heuristics that may influence investmentdecisions making process of individual (Subrahmanyam 2007, Le & Doan 2011,Kengatharan 2014)

In Vietnam, the first official stock exchange, namely the Ho Chi Minh StockExchange (known as HOSE) has been launched since mid-2000 and five years later, the HaNoi Stock Exchange, (known as HNX) was established Both the markets have recentlysignificantly developed At the time of establishment, the Vietnamese stock market was stillstrange and vague to most of local people due to several its limitations such as insufficientlegal foundation, simple trading system, very few security companies and limited types ofsecurities (HOSE, 2010, p.7) Recently, the Vietnamese stock market has experiencedsignificant development with regards to the market size and market

capitalization As of November 2014, there were 345 companies listed on HOSE, withthe listed value of almost 339,000 billion VND and 367 companies listed on HNX, withthe listed value of almost 92,422 billion VND, reach over VND 145,000 billion of thetotal market value However, in comparison to foreign stock markets, Vietnam stockmarket appears to be much smaller in terms of market size and market capitalization (Le

& Doan, 2011)

Among the two exchanges, the Ho Chi Minh stock exchange has been developingsignificantly in both a number of listed stocks and trading values; however, its aggregatemarket index (VN-Index) movement seems to fluctuate unpredictably over differentperiods As such, several studies shown that investment decision making of investors,particularly individual investors, in the market is influenced by many factors includingbehavioral factors such as herding effect, heuristic and market factors (Waweru, 2008,Hassan et al, 2013)

Several studies in the literature show that individual investors have difficultiesmaking investment decisions due to lack of financial sophistication (Winchester et al.2011) Individual investors often have embraced heuristics or rule of thumb in theirinvestment decision making (Shikuku, 2010) This issue may raise a concern that whetherinvestment decision making of individual investors in the Vietnamese stock market isinfluenced by heuristic? Hence, this research attempts to investigate the influence ofheuristic factors on influencing individual investors’ decision-making and performance inthe context of the Vietnamese stock market

1.3 Research objective and research questions

The objective of the study is to investigate impacts of heuristic factors onindividual investors’ decision-making and their investment performance Morespecifically, two questions are given as follows:

Vietnamese stock market?

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positive influence on the investment performance?

1.4 Scope of the research

There are several heuristic factors influencing behaviors of investors in stockmarkets In this study, three heuristic factors are taken into account, namelyrepresentativeness, availability and overconfidence There are factors that mentioned inseveral studies such as the research on overconfidence bias (Glaser and Weber, 2012),the study on representativeness bias (Taffler, 2012) and analysis regarding to the effects

of behavioral factors on investment decision making by unit companies in Kenya(Shikuku, 2010) and so on

In this thesis, individual investors are concerned because according to the report ofthe Wall Street Securities (2014) this group of investors account for over 60 percentages

of investors trading in the Vietnamese stock market

In addition, due to the time constraint, the research focuses only on the heuristicbehaviors of individual investors studying or working at the Ho Chi Minh City only The

Ho Chi Minh City is chosen because it is often considered as the biggest economiccenter of Vietnam and the biggest Vietnamese stock exchange, namely the Ho Chi MinhStock Exchange, is located there

1.5 Structure of the thesis

This thesis is organizes in five chapters as follow:

Chapter 1 is an introduction chapter This chapter describes an overview ofresearch background, research problem, and objective Besides, the scope of research,implications, and structure of thesis are also present

Chapter 2 is about presenting previous research done on the stream of studiesrelated to theoretical foundation regarding to explain prospect theory and heuristicstheory as well Besides, heuristics of individual investors also is presented in detail in theresearch More importantly, investment decision making and investment performance ofindividual investors is clearly explored as well This chapter is to concentrate on

explaining each variable in the model, and reasons for choosing them to be included inthe research model.

Chapter 3 is research methodology chapter Firstly, research process is presented ingeneral Then, research design and sampling are also mentioned regarding to qualitativemethod and quantitative method as well After that, the measurement scales apply for theresearch factors will be determined clearly and suitably This chapter also defines how tocollect data and analyze the data collected to test the research hypotheses proposed inchapter 2 Finally, research method is explained in detail regarding to Cronbach alpha,Exploratory Factor Analysis and Multiple regression analysis

Chapter 4 is the analysis and discussion chapter In detail, data background isfirstly mentioned and measurement reliability of each factor using Cronbach’s alpha isproperly presented as well Moreover, scale testing by using Exploratory Factor Analysisand multiple regression analysis is explored in detail in the session Furthermore, this partalso discusses the method for collecting data used to test the hypothesis, and it analysesthe data received, its reliability and multiple regression as well

The last chapter, chapter 5 discusses the results and research findings This chaptergives conclusion, implication, and research limitations Finally, this thesis makessuggestions for further research on the topic area

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CHAPTER 2: LITERATURE REVIEW, HYPOTHESIS AND

RESEARCH MODEL

This chapter reviews the related literatures of behavioral finance regarding toheuristic factors that is mentioned as a main content of the study Firstly, somebackgrounds of behavioral finance and the important theories of behavioral finance such

as prospect theory and heuristics theory are included to have an overall picture of this fieldand its impacts on the investment decisions and performance Next, heuristic of individualinvestors is presented in detail like definition of heuristics and the prior studies onheuristics as well as its classification are also explored deeply and its impact to theinvestment decisions and performance More importantly, the content of investmentdecision making and investment performance is properly discussed as major subject.Finally, a research model with hypotheses is proposed to follow during the research

2.1 Theoritical foundation

2.1.1 Prospect theory

Prospect theory is the behavioral theory that has the most remarkable impact oneconomic research (Okur & Gurbuz, 2014) This theory was developed by Professor DanielKahneman and Amos Tversky in 1979 Starting from empirical evidence, it described howindividuals evaluate losses and gains Kahneman & Tversky (1979) developed this theory toremedy the descriptive failures of expected utility theory of decision making Prospect theoryattempted to describe decisions under uncertainty, and has also been applied to the field ofsocial psychology (Okur & Gurbuz, 2014) The authors argued that investors value gains orlosses according to an S-shaped utility function In other words, prospect theory describedsome states of mind affecting an individual’s decision-making processes including Regretaversion, Loss aversion and Mental accounting

In the theory, the reference point was determined by each individual as a point ofcomparison For wealth levels under the reference point investors are risk seekers thatmeans they are prepared to make riskier bets in order to stay above their preferred targetlevel of wealth Whereas for wealth levels above this reference point, the value function

is down ward sloping in line with conventional theories and investors are risks averse.Kahneman & Tversky asserted that people are risk lovers for losses as discussed in thestudy by Finucane et al (2002) As such, the utility function is concave for gains meaningthat people feel good when they gain, but twice the gain does not make them feel twice asgood The utility function is convex for loss meaning that people experience pain whenthey lose, but twice the loss does not mean twice the pain.

The theory was used to get an overall review about behaviors of individual investors

in investment decision making that includes heuristic factors According to Okur & Gurbuz(2014) reviewing prospect theory in finance, positing that expected utility theory, with itsrational expectations derivative, was still the dominant paradigm for investor decisions infinance and for economic decisions in general

2.1.2 Heuristics theory

According to Bramson (2007) heuristics is considered as a normative decisiontheory Heuristics are defined as the rules of thumb, which makes decision making easier,especially in complex and uncertain environments by reducing the complexity of assessingprobabilities and predicting values to simpler judgments (Kahneman & Tversky, 1974,p.1124) In general, these heuristics are quite useful, particularly when time is limited(Waweru et al., 2008, p.27), but sometimes they lead to biases (Kahneman & Tversky,

1974, p.1124; Ritter, 2003, p.431)

Balota et al (2004) said that many decisions are based on beliefs concerning thelikelihood of uncertain events such as the outcome of an election, the guilt of a defendant,

or the future value of the dollar These beliefs are usually expressed in statements such as

“I think that …,” “chances are …,” “it is unlikely that …,” and so forth Occasionally,beliefs concerning uncertain events are expressed in numerical form as odds or subjectiveprobabilities What determines such beliefs? How do people assess the probability of anuncertain quantity? People rely on a limited number of heuristic principles which reducethe complex tasks of assessing probabilities and predicting values to simpler judgmentaloperations

Selden (1912) said that psychology of the stock market that the ups and downs of

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prices in stock exchanges depend upon a very substantial degree of psychological approach

of investing and trading community According to Finucane, et al (2000), there is anegative relationship between risk and benefits perception due to affect heuristic

It could be seeing that an investor always wants high returns against his investmentbut his or her decisions are explicitly affected by affect heuristic Hot stocks where aninvestor perceives high risk may be neglected by investor regardless of its return due toinverse relation of affect heuristic and “judgment and decision making” People alreadyhad some images and symbols in their minds to perceive their risk and benefits andultimately they used those images and symbols to make their financial decisions in thestock market It is observed that there are also some investors trading in the stock marketwho are illiterate and have no sufficient financial educational background so that they areunable to do detail financial analysis before taking any decision and use heuristics to get rid

of that critical decisional phase (Hassan, 2013)

In summary, the theory is properly applied to explore possible effects of heuristic onthe individual investor’s judgments and investment decisions

2.2 Heuristic of individual investors

2.2.1 Definition of heuristics

Heuristics, which expresses that individuals have tendency to make judgmentsquickly, are simplifying strategies used to approach complex problems and limit explanatoryinformation (Shikuku, 2010) Individual investors tend to make decisions usually by trial anderror method thus developing rules of thumb To put it simply, investors use rules of thumb inorder to process complex information so as to make investment decisions Sometimes it maylead to a favorable decision, but sometimes, it may result in unfavorable and poor decisionoutcomes (Chandra, 2008) An example of a heuristic is to judge the frequency or probability

of an event by its availability, the ease with which examples of the event come to mind.According to Finucane et al (2000), the affect heuristic refers to the way in which subjectiveimpressions of “goodness” or “badness” can act as a heuristic capable of producing fastperceptual judgments and also systematic biases For

example, as Ganzach (2001) showed, people judge stocks that they perceive as “good” tohave low risks and high returns and judge stocks that they perceive as “bad” to have lowreturns and high risks Specifically, for unfamiliar stocks, perceived risk and perceivedreturn are negatively correlated, as predicted by the affect heuristic In the meantime, forfamiliar stocks, perceived risk and perceived return are positively correlated; riskier stocksare expected to produce higher returns, as predicted by ordinary economic theory.

2.2.2 Classification of heuristics

There were many prior studies on heuristics such as Tversky & Kahneman (1974),Hassan et al (2013), Bramson (2007) Tversky & Kahneman (1982a), for instance, arguefor the prevalence of three general-purpose heuristics: representativeness, availability, andanchoring and adjustment Later et al (2002) bring together the work of many otherresearchers and explicitly include emotional factors as a general-purpose heuristic under

the term the affect heuristic Gilovich and Griffin (2002) listed six general-purpose

heuristics: affect, availability, causality, fluency, similarity, and surprise

Kahneman & Tversky (1974, p.1124-1131) who were known as the first writersstudying the factors belonging to heuristics introduce three factors namelyrepresentativeness, availability bias, and anchoring Waweru et al (2008, p.27) also listedtwo factors named Gambler’s fallacy and Overconfidence into heuristic theory According

to Schwartz (1998) there is considerable evidence on general heuristics—notablyrepresentativeness, availability, anchoring and adjustment, and affect (dealing withemotions) but much less on the specific heuristics used in most decision -makingprocesses

According to Shikuku (2010), due to the fact that the more information was, the faster

it spreaded, life for decision makers in financial markets has become more complicated Thisimplies increased use of heuristics which is often a mostly inevitable approach but not alwaysbeneficial (Fromlet, 2001) The interpretation of new information may require heuristicdecision-making rules (Finucane et al 2002) Chandra (2008) studied behavioral factors andtheir impacts on investors’ attitude towards risk and behavioral

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decision making process The study concluded that individual investors suffer fromheuristics such as representativeness, overconfidence and anchoring, cognitive dissonance,greed and fear, and regret aversion and mental accounting which all influence investor’sperception of risk and subsequently his decision making.

Due to the constraint time, this research focuses only on analyzing three of mainlyheuristic factors which including representativeness, availability and overconfidence

Representativeness refers to the way people make subjective probability

judgments based on similarity to stereotypes (Barker & Nofsinger, 2012, p 259).However, recognizing the representativeness heuristic is easier than defining it Gilovich(1991, p.18) described the nature of the heuristic in more detail: “Representativeness is atendency to assess the similarity of outcomes, instances and categories on relativelysalient and even superficial features, and then to use these assessments and similarity as abasis of judgment People assume like goes with like.” Because representativeness is notinfluenced by several factors that should affect probability judgments, the implication isthat errors in judgment sometimes result (Barker & Nofsinger, 2012, p 259).Representativeness may result in some biases such as people put too much weight onrecent experience and ignore the average long-term rate (Ritter, 2003, p.432)

Availability is the heuristic reflecting the weight given to information in place ofprobability or frequency That weighting is attributable to the ease of recall and the content ofwhat is recalled Availability may be due to some recent dramatic news event Warneryd(2001) noted, availability could be experience-based, memory-based, or imagination-based.Unfortunately, there is no agreement as to what constitutes different degrees of availability orthe weight that should be given to those differences in availability One type of recognition ofthe importance of availability can be observed from the behavior of a successful mutual fundmanager, who is supposed to have reflected that he tended to avoid stocks that most analystsand managers were celebrating because he was convinced that such “availability” increasedthe likelihood that the shares of those companies were

overvalued The tendency of investors to focus so overwhelmingly on national rather thaninternational stocks, particularly until the mid-1990s, and to miss profitable opportunitiesabroad, probably reflects reliance on the availability heuristic Perhaps the main bias ofavailability is due to its extreme lack of sensitivity to sample size; by its nature,information that is dramatically available may reflect a small sample

Overconfidence is a pervasive phenomenon and can have severe consequences.Researchers have offered overconfidence as an explanation for wars, strikes, litigations,entrepreneurial failures, and stock market bubbles (Camerer & Lovallo, 1999; Glaser &Weber, 2007; Howard, 1983; Johnson, 2004; Malmendier & Tate, 2005; Neale &Bazerman, 1985; Odean, 1999) According to Plous (1993, p 217), “no problem injudgment and decision making is more prevalent and more potentially catastrophic thanoverconfidence.” On the contrary, when people overestimate the reliability of theirknowledge and skills, it is the manifestation of overconfidence (DeBondt & Thaler, 1995,Hvide, 2002)

According to Barker & Nofsinger (2012), many researchers consider overconfidence

as the most prevalent judgment bias Several studies show that overconfidence can lead tosuboptimal decisions on the part of investors, managers, and politicians For examples,investors and analysts are often overconfident in areas that they have knowledge (Evans,

2006, p.20) However, overconfidence is also believed to improve persistence anddetermination, mental facility, and risk tolerance It can help to promote professionalperformance and enhance other’s perception of one’s abilities, which may help to achievefaster promotion and greater investment duration (Oberlechner & Osler, 2004, p.3)

2.3 Decision making and performance of individual investor

2.3.1 Decision making

Making investment decision is even more critical and difficult in a stock marketand such decisions need better insight and understanding Investment decision may haveeffect due to psychological and behavioral factors (Evans, 2006 and Waweru et al., 2008).Traditional finance expects investor to be rational but behavioral finance believes that

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investors in stock markets act irrational While making decisions in market the investors’process available information Their emotions, psychology, and behavioral biases lead tosystematic errors in the manner in which they process information (Pavabutr, 2002).

According to Macmillan (2000) each investment decision often involvescomplexity and uncertainty Complexity is reflected, in part, by the number of alternativecourses of action from which the decision-maker can choose Uncertainty is inherent in alldecision-making but particularly pertinent to the investment decision-maker where theimplications of their decisions are often very significant for the organization Moreover,investors are usually trying to fulfil multiple objectives in their investment decisions andtherefore have to make trade-offs between expected return and riskiness Dean andSharfman (1996) note that it is unlikely that the influence of such forces eliminates theimpact of choice on decision effectiveness as it is hard to imagine a decision in which allpotential choices will be equally successful or unsuccessful

Barber and Odean (2008) argued that attention greatly influences individualinvestor purchase decisions Investors face a huge search problem when choosing stocks

to buy Rather than searching systematically, many investors may consider only stocksthat first catch their attention (e.g., stocks that are in the news or stocks with large pricemoves) This will lead individual investors to buy attention-grabbing stocks heavily Sincemost individual investors own only a small number of stocks and only sell stocks that theyown, selling poses less of a search problem and is less sensitive to attention effects

2.3.2 Investment performance

Personal rate of return is a person's own investment performance based on his or herown transaction history and resulting cash flows We attempt to shed light on the investmentperformance of common stocks held directly by individual investors Schlarbaum, Lewellen,and Lease (1978a) analyze the aggregate common stock performance of investors at a full-service brokerage firm Odean (1999) and Schlarbaum, Lewellen, and Lease (1978b) analyzethe profitability of common stock trades (as distinct from positions held) by individualinvestors Lin and Swanson (2003, p.208) measure investment performance using threecriteria of returns (raw returns, risk-adjusted returns, and momentum-adjusted

returns) through five-time horizons (daily, weekly, monthly, quarterly, annually) Theyrecognize that investors achieve excellent performance, which exists in the short run and

is partially driven by short-term price momentum rather than by risk-taking

According to Barber and Odean (2001) men tend to be more prone to overconfidencethan women in areas culturally perceived to be in the male domain (Deaux and Farris, 1977),and investors are overconfident tend to predict investors will trade excessively and to theirdetriment When combined, these observations predict that men will trade more than womenand that excessive trading will hurt their performance Consistent with these predictions,Barber and Odean (2001) document that men trade more than women; the annual turnoverrates of men are about 80%, while those of women are 50% The excessive trading of menleads to poor returns While both men and women earn poor returns, men perform worse.Virtually all of the gender-based difference in performance can be traced to the fact that mentend to trade more aggressively than women Neither men nor women appear to have stockselection ability (i.e., the gross returns earned on their trades are similar), so men’s tendency

to trade aggressively and the resulting trading costs drag down men’s returns

There are quite many methods to measure the investment performance Serveralauthors mainly used the secondary data of investors’ results in the security markets tomeasure the stock investment performance (Lin et al., 2003) while others use primary datacollected from interview to evaluate their investment performance, such as Oberlechner &Osler (2004), and Le & Doan (2011)

2.4 Hypothesis Development

2.4.1 Representativeness and investors’ decision making.

A typical example for representativeness bias is that investors often infer a company’shigh long-term growth rate after some quarters of increasing (Waweru et al., 2008, p.27).Representativeness also leads to the so-called “sample size neglect” which occurs whenpeople try to infer from too few samples (Barberis & Thaler, 2003, p.1065) In the stockmarket, when investors seek to buy “hot” stocks instead of poorly performed ones, this meansthat representativeness is applied This behavior is an explanation for investor overreaction(DeBondt and Thaler, 1995, p.390) The good stocks are viewed as being the

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stocks of good companies and good companies as being safe companies is againconsistent with the operation of the representativeness heuristic However, this beliefamong executives is clearly contrary to traditional finance theory, which teaches that riskand return are positively correlated.

In the stock market, for example, investors might classify some stocks as growthstocks based on a history of consistent earnings, growth, ignoring the likelihood that thereare very few companies that will keep growing (Finucane et al 2002) Raines & Leather(2011) argue that the tendency to make numerical predictions of values of stocks that isrepresentative of the descriptions of the companies but ignoring the reliability of thosedescriptions results in overreliance on stereotypes and the underweighting of base rateinformation Kahneman & Tversky (1974) showed that people had a tendency tocategorize events as typical representative of a well-known class and then, in makingprobability estimates to overstress the importance of such categorization disregardingevidence of the underlying probabilities

Representativeness helps to explain why many investors seem to extrapolate pricemovements Many investors appear to believe that if prices have been rising in the pastthen they will continue to rise, and conversely with falling prices The concept ofrepresentativeness suggests that this is because those investors see an investment withrecent price increases as representative of longer-term successful investments, converselywith price falls DeBondt & Thaler (1985) argued that because investors are subject to therepresentativeness bias, they could become too optimistic about past winners and toopessimistic about past losers Trading that is influenced by the representativeness bias canmove share prices away from the levels that accurately reflect all relevant information

It could be seen that the behavioral finance is suggested for some period inVietnam, however, there is no consistent empirical results on the representativeness biastowards decision making of investors As such, one hypothesis is suggested as follows:

Hypothesis 1: Representativeness has positive impact on the individual investors’ decision-making in Vietnam.

2.4.2 Availability bias and investors’ decision-making

According to Shefrin (2002), availability bias occurs because investors reply oninformation to make informed decisions, but not all information is readily available.Investors tend to give more weight to more available information and to discountinformation that is brought to their attention less often The stocks of corporations that getgood press, for example, claim to do better than those of less publicized companies when

in reality these “high-profile” companies may actually have worse earnings and returnpotential

People, generally, easily recall the information that has recently arrived, especially

in the media and corporate releases, because their broker’s or advisor’s recommendationsare fresh in their memory As Barber and Odean (2008) found, stocks with very high level

of press coverage underperform in the subsequent two years following the news

In the availability heuristic, investors estimate probability by the ease with whichthey can bring to mind similar instances or associations Biases occur when “availability”and true frequency diverge For example, Klibanoff, Lamont, & Wizman (1998) showedthat dramatic country-specific news affects the response of closed-end country fund prices

to asset value In a typical week, prices underreact to changes in fundamentals In weeks

where news of the particular country appears on the front page of the Saigon Times, prices

react much more aggressively

In general, the behavioral finance is suggested for some period in Vietnam,however, there is no empirical study on the availability bias towards decision making ofinvestors As such, one hypothesis is suggested as follows

Hypothesis 2: Availability has positive impact on the individual investors’ decision-making in Vietnam

2.4.3 Overconfidence and investors’ decision making

Most of the overconfidence models predict high trading volume in the market whenthere are overconfident traders Moreover, at the individual level, overconfident investorstrade more aggressively: The higher the degree of investor overconfidence, the higher theinvestor’s trading volume Odean (p 1888) called this finding “the most robust effect of

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overconfidence.” DeBondt and Thaler (1995, pp 392–393) noted that the high tradingvolume observed in financial markets “is perhaps the single most embarrassing fact to thestandard finance paradigm” and that “the key behavioral factor needed to understand thetrading puzzle is overconfidence".

In this research, overconfidence factor is used to measure its impact levels on theinvestment decision making as well as the investment performance of individual investors

at the Ho Chi Minh Stock Exchange in the following hypothesis:

Hypothesis 3: Overconfidence factor has positive impact on the individual investors’ decision-making in Vietnam

2.4.4 Investment decision making and investment performance

Theories of investor under- and overreaction to news (based, for example, onoverconfidence and bounded rationality) are being put forth to explain return patterns likelong-horizon reversals The assumptions behind these theories of investor behavior arefounded in psychological research or common sense Clearly, however, this line ofresearch could benefit from a more complete picture of how investors actually behave andhow they differ from one another in the way they react to the same information (Grinblattand Keloharju, 2000)

A number of recent contributions have documented interesting regularities in the return-based behavior of investors Brennan and Cao (1997) presented a theoretical model andempirical evidence that supports the view that foreign investors should pursue momentumstrategies and achieve inferior performance because they are less informed than domesticinvestors Froot et al (2000) and Choe et al (1999) found that foreign investors tend to bemomentum investors, the latter paper focusing on short past-return horizons

past-According to Grinblatt and Keloharju (2000) a simultaneous analysis of theinvestment behavior and performance of all investor categories has been impossible untilnow because of data limitations Different research methods, different data frequencies,different horizons for past returns, and different institutional arrangements unavoidablyblur the comparison of the results and make it difficult to identify general patterns behindthe behavior and performance of isolated investor categories

Anderson, Henker and Owen (2005, p.71) concluded that individual investors whomake higher amount of transactions may result greater returns than individuals with fewertransactions may Kim and Nofsinger (2003, p.2) claimed that stocks experiencing thegreatest increase in individual possession can earn a negative abnormal return during theyear; whereas, stocks that experience the most decrease in individual ownership may earn

a positive abnormal return They also go additionally insight into buying and sellingbehaviors and study the past performance of these bought and sold stocks The authorsfind that stocks that have significant increases in individual ownership (purchased stocks)are the past winning stocks

Such results led Papadakis (1998) to hypotheses that performance is positivelyrelated to comprehensiveness/rationality and formalization in the investment decision-making process

It could be seen that relationship between decision making and performance ofindividual investors is suggested for some period in Vietnam, nevertheless, there are noempirical study whether the individual investors of decision making impacts onperformance of investors or not Hence, one hypothesis is suggested as follows:

Hypothesis 4: The individual investors’ decision-making has positive impact on their investment performance in Vietnam

Availability bias

decision making H3 (+)

Overconfidence

bias

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Accordingly, four following hypotheses are suggested:

Hypothesis H1: Representativeness has positive impact on the individual investors’ decision-making in Vietnam.

Hypothesis H2: Availability has positive impact on the individual investors’ decision-making in Vietnam.

Hypothesis H3: Overconfidence factor has positive impact on the individual investors’ decision-making in Vietnam.

Hypothesis H4: The individual investors’ decision-making positive impact on their investment performance in Vietnam.

2.6 Chapter summary

In summary, as mentioned in the literature review above, it is undoubtedly thatheuristic factors impact the investment decision making and performance of individualinvestors in the financial markets, especially in the stock markets The chapter analyzesthe theoretical foundation in detail as prospect theory and heuristics theory Moreover, thethesis explores heuristics of individual investors, consisted of definition and classification

of heuristics More importantly, it also investigates the relationship between decisionmaking and performance of individual investors in the Ho Chi Minh stock market as well

as developing hypotheses and building conceptual model

CHAPTER 3: RESEARCH METHODOLOGY

This chapter presents brief description of the research methodology used for theresearch This includes the research process, research design, pilot test and main survey

3.1 Research process

In general, a theory is built and tested based on two different approaches: inductionand deduction When deductive approach is employed, researchers start with the existingtheory and logical relationships among concepts, and then continue to find empiricalevidences In contrast, in inductive research, theory is developed from the observations ofempirical reality and researchers infer the implications of the findings for the theory thatprompted the research (Ghauri & Gronhaug, 2010, p.15-16; Saunder et al., 2009, p.124-126; Blumberg et al., 2005, p.22-24; Bryman & Bell, 2007, p.11)

In this study, exploring the heuristic factors influencing the performance anddecision making of investors, which are already “out there”, is the main aim, instead ofinferring and building theory, deduction approach seems to be the most appropriatechoice The study starts with reviewing the behavioral finance theories in general and instock market in particular, to get the theoretical and conceptual context as well asempirical findings of previous researches, from which the research model and hypothesesare proposed Then, the questions used in interviews and questionnaires are prepared Thisprocess is quite consistent with deductive approach which emphasizes that researchersmay know how the world operates, thus using this approach to examine these ideas against

“hard data” (Neuman & Kreuger, 2003, p.53) The hypotheses are tested through datacollection and analysis Comparison between the results of the research and the existingtheories is made to find out the differences Deductive approach is usually associated withquantitative researches, which involve collecting of quantitative or quantifiable qualitativedata and analyzing statistical methods, which is also compatible with quantitative researchstrategies

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Research problem Research objective Research scope

Conclusion and implication

Figure 3.1 Research process

Firstly, research problem was defined, and then research objective and researchquestions were identified to be the target of solving defined research problem After that,this study conducted a literature review to review some relevant theories about heuristicfactors influencing decision making and performance of individual investors to find outthe suitable one to Vietnamese stock market model and built the hypotheses for this study.From this, a preliminary questionnaire was developed basing on questions used in someprevious studies Next step is research design with 2 sub-steps:

Pilot study: a study was conducted by interviewing around two managers ofsecurities company face to face about the content, the number and the structure ofquestions in preliminary survey to test the survey and measure before launching the mainsurvey Moreover, we also got a draft survey with 94 investors for testing reliability andrun EFA for the research

Main survey: was conducted survey through mail, social network, or sending thehard copy of survey directly to individual investors thanks to the brokers of securitiescompany Data collection was done one month later

After that, collected data was cleaned and used to test reliability of scale andvalidity of questionnaire through Cronbach’s alpha coefficient and Exploratory FactorAnalysis (EFA) method Multiple regression method was used to evaluate the hypotheseswhich the implication and finding were stated and reported

3.2 Research design

The questionnaire is divided into four parts: personal information, heuristic factorsinfluencing investment decisions, decision making and investment performance In thepart of personal information, nominal measurements are used Nominal scales are used toclassify objects

This research is based on the theories of behavioral finance: Heuristic theory,Prospect theory, impacts of behavioral factors on investors’ decision-making, which arementioned by Waweru et al (2008, p.24-38) and many other authors cited in the literaturereview, to synthesize a set of questions related to heuristic factors influencing

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investment decisions and investment performance In these parts, the 5 -point Likert scales,which are rating scales widely used for asking respondents’ opinions and attitudes (Fisher,

2010, p.214), are utilized to ask the individual investors to evaluate the degrees of theiragreement with the impacts of heuristic factors on their investment decision as well as withthe statements of investment performance The 5 points in the scale are respectively from 1

to 5: extremely disagree, disagree, neutral, agree, extremely agree The measurements andquestions for these parts are presented in table 3.1, table 3.2 and table 3.3

3.2.1 Measure of heuristic factors

Based on previous research about behavioral finance impact on investment decisionmaking Three of typical heuristics we namely study representativeness, availability andoverconfidence Particularly, representativeness was measured by three observedvariables, developed by DeBondt & Thaler (1995), used a five-point Likert scale, andmodified by others authors We based on mainly studies of Kengatharan (2013) and Le &Doan (2011) Besides, availability factors was measured by three observed variables,based on research of Hassan et al (2013), Le & Doan (2011) And last but not least, source

of overconfidence bias is mainly based paper of Kengatharan (2013), research of Luu(2014) and Qureshi (2012) with two observed variables

Table 3.1 Types of measurement for heuristic variables influencing investment decision making

Investors buy "hot" stocks DeBondt &and avoid stock that have

1995, p.390recent past?

recent stock prices?

Investors use trend analysis Le & Doan

stocks to make investment

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Variables Items Description Sources

decision for all stocks thatthey invest?

investment?

In investors’ opinion it is

and relatives as the reliable 2011

investment decisions?

Luu (2014)

decision making

3.2.2 Measure of investors’ decision making

There are many factors influencing on decision making of individual investors.Nevertheless, in this study, the problem is mainly stated regarding to the viewpoint ofindividual investors about their investment Investors’ decision making was measured bythree observed variables, developed by Hassan Et Al (2013) and Qureshi (2012), used afive-point Likert scale as follows:

Table 3.2 Types of measurement for individual investors’ decision making

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Variables Items Description Sources

safety

payment

3.2.3 Measure of investment performance

The prior authors mainly use the secondary data of investors’ results in the securitymarkets to measure the stock investment performance (Lin and Swanson (2003), Kim andNofsinger (2003) and so on) However, this research asks the investors to evaluate theirown investment performance, so that the measurements of investment performance followthe research of Oberlechner and Osler (2004) for the investment return rate In moredetails, the return rate of stock investment is evaluated by objective and subjectiveviewpoints of individual investors The subjective assessment of investors is made byasking them to compare their currently real return rates to their expected return rates whilethe objective evaluation is done by the comparison between the real return rates and theaverage return rate of the security market Besides, the satisfaction level of investmentdecisions is proposed in this research as a criterion to measure the investmentperformance In reality, there are investors felling satisfied with their own investmentperformance even if their investment profits are not high; in contrast, other investors donot feel satisfied with their investments even when their profits are relative high.Therefore, the satisfaction level of investment decisions together with investment returnrate are proposed as measurements for the investment performance in this research

Investors’ performance was measured by three observed variables, developed byKengatharan (2013), used a five-point Likert scale, and modified by the author as follow:

Table 3.3 Type of measure for individual investors’ performance

The return rate of the investors’

(2013)their expectation?

Investors’ investment in stocks Hassan Et Al

(2013)

Qureshirevenue growth in last year

(2012)

3.3 Pilot test

Based on the literature, to prepare a draft of questionnaire for pilot test, the authorinterviews with two managers of the HOSE are conducted to have deeper understandingsabout financial behaviors of Vietnamese individual investors The interview list is picked

up based on convenience sample due to the limited free time of managers Two managersare invited to separate interviews Since these managers are responsible for tradingsurveillance and market information for the HOSE, which has to supervise the securitiescompanies as well as trading activities at Ho Chi Minh stock market, they are expected tohave deep understanding about the stock market and investors’ behaviors Hence, it isbelieved that they are qualified interviewees for this study, who can provide significantanalysis and discussion on this topic

Moreover, the draft survey would be collected before doing main survey First of all,Cronbach’s Alpha Test is used to test the internal consistency reliability of measurements Nunnally (1978, p.245) suggests that Cronbach’s alpha should be at least 0.7 to make surethat the measurements are reliable However, many statisticians believe that it can beacceptable if the Cronbach’s alpha is over 0.6 (Shelby, 2011, p.143) Besides, statisticiansrecommend that it is necessary to consider the corrected item-total correlations when usingthe Cronbach’s alpha index The corrected item-total correlations, which reflect the

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correlation of variables or items designated with the total score for all other items, should

be at the acceptable score of 0.3 or higher (Shelby, 2011, p.143) This research choosesthe acceptable Cronbach’s alpha is 0.6 or more, with the corrected item-total correlationindex is 0.3 or more because the measurements of financial behavior are new to thestockholders of the Ho Chi Minh Stock Exchange Besides, the accepted significant level

of the F-test in Cronbach’s alpha technique is not more than 0.05 The Cronbach’s alphatest is finished by SPSS software

Then, EFA is used to explore the factors that the heuristic variables, investmentdecision making and investment performance of the questionnaire (question 13 to question26) belong to EFA is used to reduce the number of items in the questionnaire that do notmeet the criteria of the analysis (O’brien, 2007, p.142) In this case, EFA is utilized to testthe hypotheses shown in the research model of Chapter 2

In this research, the following criteria of the exploratory factor analysis are applied: Factorloadings, KMO, Total variance explained, and Eigen value

Based on 94 samples collected from a pilot studies, the results show that, all most themeasurement scales have the Cronbach’s alpha coefficient larger than 0.6 Cronbach’s alpha

of availability bias is 0.566 that less than 0.6, but also greater 0.5 and Corrected Item TotalCorrelation values were over 0.3 Therefore, this variable is also accepted for Exploratoryfactor analysis later It could be seen that the scale designed in this research is meaningful instatistic and has the necessary reliability

Besides, after processing factor analysis for the independent variables and thedependent variable by the Varimax method has four factors are formed They arerepresentativeness bias, overconfidence bias, decision making and performance ofindividual investors (value of availability variable (AVA2 is removed because it is loadeddouble, AVA1 and AVA3 is loaded in the same component of representativeness variableand overconfidence, details could be seen in Appendix 1, so that we renamed AVA1 toRE4, and AVA3 to OVER3 From now on, the new model is suggested thatrepresentativeness and overconfidence bias influencing decision making and performance

of individual investors as follows:

Figure 3.3 New Research Model (revised)

Representativeness

Overconfidence

H1 (+)

H4 (+) Investment performance Investors’

to have fit the statistical methods of data analysis

Questionnaires were sent to respondents using stratified random sampling Initially,convenience sampling is chosen as it is the best technique to get the highest rate of responsewhen sending to friends and relatives In addition, it will help to save time and money.Nevertheless, convenience sampling is one type of non-probability sampling, which cannotprovide representative sample, thus the result cannot be generalized for the whole population(Bryman & Bell, 2007, p.198) while the target is to find out the financial behaviors of thewhole population of individual investors In contrast, stratified random sampling allows us tostratifying the population by a criterion (in this case, the brokerage market share), then chooserandom sample or systematic sample from each strata (Bryman

& Bell, 2007, p.187) Stratified sampling ensures that the sample is distributed in the sameway as the population (Bryman & Bell, 2007, p.187) The number of questionnaires weresent to brokers of these companies, who support to their investors randomly Due to time

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constraint, only individual investors from ten leading securities companies have beenchosen See appendix 2 for the list of securities firms selected.

3.4.2 The research method

The sampling method was the non-probability sampling with convenience method.The method applied to test the reliability of measurement scale was Cronbach’s Alpha, thefactor loading was tested by Exploratory Factor Analysis and the hypotheses were tested

by Multiple Regression A data set satisfied requirement of Exploratory Factor Analysiswas five times the number of variables and at least more than 100 (Hair et al., 1998) n =5k (k: number of variables) and n > 100 Therefore, in this study, the minimum samplerequired by EFA was n > 100 (there are five variables in this study)

The minimum sample for multiple regression analysis must ensure the formula of n

> 50 + 8m (m: number of independent variables) (Tabachnick and Fidell, 1996)

As there were three independent variables in this study, the minimum samplerequired to run multiple regression in this research was n > 74

As a result, the minimum sample size in this study was over 100 which would besatisfied both EFA and multiple regression analysis

- Cronbach’s Alpha Test

As mentioned in pilot test, this research use Cronbach’s alpha to examine the reliability ofvariables in the questionnaire through following coefficients:

Cronbach’s alpha coefficient: the scale is reliable when this coefficient is 0.6

Corrected Item – Total correlation: variables are acceptable when this coefficient is0.3 or more

As mentioned in pilot test, after surveying, EFA method with Varimax rotation wasused to analyze observed variables of heuristic factors influencing decision making andperformance of individual factor KMO (Kaiser-Meyer-Olkin) test and Bartlett test werealso used to measure the compatibility of sample

- Multiple regression analysis

The Multiple Regression Analysis was used to test the research hypotheses of thisstudy In accordance with Leech et al (2005), there are many assumptions to consider but wewill focus on the major ones that are easily tested with SPSS The assumptions for multipleregression include the following: that the relationship between each of the predictor variablesand the dependent variable is linear and that the error, or residual, is normally distributed anduncorrelated with the predictors A condition that can be extremely problematic as well ismulticollinearity, which can lead to misleading and/or inaccurate results Multicollinearity (orcollinearity) occurs when there are high intercorrelations among some set of the predictorvariables In other words, multicollinearity happens when two or more predictors containmuch of the same information

Although a correlation matrix indicating the intercorrelations among all pairs ofpredictors is helpful in determining whether multicollinearity is a problem, it will notalways indicate that the condition exists Multicollinearity may occur because several

predictors, taken together, are related to some other predictors or set of predictors For this

reason, it is important to test for multicollinearity when doing multiple regression Themultiple regression analysis would be run by SPSS 16.0

In this study, there are two models of multiple regression as follows:

Where in:

- Y: Investment decision making variable (dependent variable)

- X: Heuristic factors (independent variables included representativeness, availability and overconfidence variables)

- ε: Radom error

Where in:

- Y’’: Investment performance variable (dependent variable)

- Y’: Investment decision making variable (independent variable)

- ε: Radom error

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CHAPTER 4: FINDINGS AND DISCUSSIONS

The purpose of this chapter is to present findings which were collected from theactual questionnaire survey Firstly, data of the research is presented in detail Secondly,measurement reliability using Cronbach’s alpha to test the internal consistency reliability

of measurement Next, exploratory Factor Analysis is used to test factors that heuristicvariables, investment decision making and investment performance of the questionnairebelong to More importantly, Linear regression is properly applied to test hypotheses

4.1 Data background

From 400 questionnaires delivered to individual investors at the Ho Chi MinhStock Exchange, 186 respondents are reported, so that the respondent rate is 47%, amoderate high rate for a postal questionnaire survey The data was gathered fromsecurities company in Hochiminh city The 186-respondent sample with the characteristics

of gender, age, marital status, education, years of working, income, years of attendant inthe stock market, securities company, course of stock, time of course, total amount ofmoney invested in the stock market and total amount of money invested in the stockmarket last year are summarized as Table 4.1 that gives a detailed description of thedemographic statistics for the respondents:

Table 4.1 Descriptive statistic of respondent’s characteristics

Measure Value Frequency Percentage

Measure Value Frequency Percentage

JSC

market (USD)

market last year (USD)

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The sample size used for data analysis is 186 samples in which 50% is male and 50%

is female The results also indicate that the research samples has age predominantlybetween 18 and 35 years, which account for 34% Regarding marital status, the singlerespondents account for more than 55%, while married respondents are 36%, the rest ofdivorced is 9% With the education factor, the highest percentage is the bachelor degreegroup that accounts for 73% Majority investors with years of working is less than fiveyears (52%) Involving with investors’ income, between 300 USD to 600 USD is 34%.Majority of individual investors with time attending in stock market from 1 to 3 year (itaccount for 28%) Almost investors are holding account in Hochiminh City SecuritiesCorporation (36%) There are 67% of investors taken a course of the stock market.Superiority of investors attends a three-month course, accounts for 65% Besides,majority of correspondents invest less than 10,000 USD into the stock market (46% forfrom date of participating in the stock market and 50% for last year)

4.2 Measurement Reliability using Cronbach’s Alpha

To ascertain the reliability of the measurement scales and to check the degree to whichthe items that make up the scale “hang together”, Cronbach’s alpha coefficient iscalculated Cronbach’s alpha checks the internal consistency reliability of scales It checks

if whether the items that make up the scale actually measure the same underlyingconstruct (Pallant, 2001) For scale to be reliable, its Cronbach’s alpha value should beabove 0.6 (George & Mallery, 2003)

The above guideline indicates that the higher the Cronbach’s alpha value is, the morereliable are the items measuring a give construct Cronbach’s alpha closer to 1.0 ispreferred A Cronbach’s alpha value of 0.9 and above was regarded as the most reliable ofscales, while a scale that has a Cronbach’s alpha value that is below 0.5 is regarded asunreliable and cannot be used to measure a given construct

In this section, Cronbach’s alpha is used to test the reliability of items included in thefactors, which are identified in the factor analysis This test is done to make sure that themeasurements are reliable for further uses

Table 4.2 Reliability analysis for each factor

The Cronbach’s alpha of representativeness (RE) is 0.759, which is larger than 0.6,and the corrected item-total correlation of representativeness 1 (RE1) = 0.703,representativeness 2 (RE2) = 0.655, representativeness 3 (RE3) = 0.711,representativeness 4 (RE4) = 0.735 all of those are larger than 0.3 Therefore, this variable

is accepted for Exploratory Factor Analysis later

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