(Luận văn) heuristics influencing investment decision making and investment performance, evidence from individual investors in vietnam

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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

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ACKNOWLEDGE

This research would not have been possible without the valuable contribution

of many people We would like to take this chance to express my great gratitude

for their understanding, encouragement, and supports

Firstly, I would like to my deepest appreciation to my thesis supervisor, Dr

Tran Phuong Thao, for numerous valuable comments and suggestions I am very lucky

to have her supervision as her continuous encouragement has motivated me and made me

confident 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

Professor Nguyen Dinh Tho for his patient in listening, discussing, and giving me

precious recommendations I am especially indebted to him for his indispensable

guidelines with regard to statistical analysis techniques

A special appreciation to my friends and colleagues for giving me the sound comments on my questionnaire as well as instant support regardless day or night This

helps me a lot in improving the quality of the research

Furthermore, we want to express our gratefulness to my friends working at the Ho Chi Minh Stock Exchange and securities companies, who help me to arrange interviews

and distribute questionnaires I am also thankful to beneficiary customers who participated

in and the survey

Finally, it would be impossible to say enough about my dear parents, my respectable teachers at University of Economics Ho Chi Minh City and my loved friends

All their understanding, encouragement, and advices help me to overcome the most

difficult time to complete this research in time

Ho Chi Minh City, June 6th, 2015

Nguyen Thi Thanh Thuy

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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

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ABSTRACT

The main objective of this study is to investigate the heuristic factors influencing individual investors’ decisions and investment performance at the Ho Chi Minh Stock

Exchange As there are limited studies about behavioral finance in Vietnam, this

study is expected 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

questionnaires distributed to individual investors at the Ho Chi Minh Stock Exchange

The result shows that there is only overconfidence bias affecting the investment decisions 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

of individual investors and investment performance

Keywords: Heuristic, individual investor, decision making, investment performance, Vietnam

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tools such as fundamental analysis, technical analysis and judgment It is assumed that

information structure and behavioral factors in the market systematically influence

individuals’ 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 are

normally known as behavioral factors focused upon how investors interpret and act on

information to make investment decisions

Much of the economic and financial theories presume that individuals act rationally

in the process of decision making, by taking into account all available information But

there is evidence to show repeated patterns of irrationality in the way humans arrive at

decisions and choices when faced with uncertainty (Bernstein, 1996) Behavioral finance,

a study of market that draws on psychology, throws light on why people buy or sell stock

and why sometimes do not buy or sell at all The most crucial challenge faced by the

investors is in the area of investment decisions The profit made, or losses incurred by an

investor can be attributed mainly to his decision-making abilities The fact that even the

most prominent and 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 traditional models of rational market behavior (Subash, 2012)

In the behavioral finance discipline, heuristics can be defined as the use of experience and practical efforts to answer questions or to improve performance (Fromlet,

2001) Raines & Leathers (2011) argue that when faced with uncertainty, people rely on

heuristics or rules of thumb to subjectively assess risks of alternatives, which reduces the

complex tasks of assessing probabilities and predicting values to simpler judgmental

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operations

It could be seeing that, heuristics are quite useful in investment decision making (Waweru et al., 2008, p.27), however they may lead to biases (Kahneman & Tversky, 1974,

p.1124; Ritter, 2003, p.431) For instance, Kahneman & Tversky (1974, page 1124)

introduce three factors belongs to heuristics namely representativeness, availability bias,

and anchoring, while Waweru et al (2008) also list two factors named Gambler’s fallacy

and Overconfidence of heuristic

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

Kahneman (1974) conduct the research of judgment under uncertainty, heuristics and

biases Hassan et al (2013) study impacts of affect heuristics, fear and anger on the

decision making of individual investor in a conceptual study In addition, another study

examines investment behavior and performance of various investor types in Finland’s

stock market done by Grinblatt & Keloharju, 2000

1.2 Research problem

In the literature, many papers show that investment decision of investors may be affected by behavioral finance Thus, many researchers attempt to investigate

psychological and sociological factors such as heuristics that may influence investment

decisions making process of individual (Subrahmanyam 2007, Le & Doan 2011,

Kengatharan 2014)

In Vietnam, the first official stock exchange, namely the Ho Chi Minh Stock Exchange (known as HOSE) has been launched since mid-2000 and five years later, the

Ha Noi Stock Exchange, (known as HNX) was established Both the markets have recently

significantly developed At the time of establishment, the Vietnamese stock market was

still strange and vague to most of local people due to several its limitations such as

insufficient legal foundation, simple trading system, very few security companies and

limited types of securities (HOSE, 2010, p.7) Recently, the Vietnamese stock market has

experienced significant development with regards to the market size and market

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capitalization As of November 2014, there were 345 companies listed on HOSE, with the

listed value of almost 339,000 billion VND and 367 companies listed on HNX, with the

listed value of almost 92,422 billion VND, reach over VND 145,000 billion of the total

market value However, in comparison to foreign stock markets, Vietnam stock market

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 developing significantly in both a number of listed stocks and trading values; however, its aggregate

market index (VN-Index) movement seems to fluctuate unpredictably over different

periods As such, several studies shown that investment decision making of investors,

particularly individual investors, in the market is influenced by many factors including

behavioral 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 difficulties making investment decisions due to lack of financial sophistication (Winchester et al 2011)

Individual investors often have embraced heuristics or rule of thumb in their investment

decision making (Shikuku, 2010) This issue may raise a concern that whether investment

decision making of individual investors in the Vietnamese stock market is influenced by

heuristic? Hence, this research attempts to investigate the influence of heuristic factors on

influencing individual investors’ decision-making and performance in the 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 on individual investors’ decision-making and their investment performance More specifically, two

questions are given as follows:

 Question 1: Do heuristic factors influence individual investors’ decisions in the Vietnamese stock market?

 Question 2: Does a strong tendency of investment decision making have a

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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 stock markets In this study, three heuristic factors are taken into account, namely

representativeness, availability and overconfidence There are factors that mentioned in

several 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 of the 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 heuristic behaviors 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 economic center

of Vietnam and the biggest Vietnamese stock exchange, namely the Ho Chi Minh Stock

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 of research 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 studies related to theoretical foundation regarding to explain prospect theory and heuristics

theory as well Besides, heuristics of individual investors also is presented in detail in the

research More importantly, investment decision making and investment performance of

individual investors is clearly explored as well This chapter is to concentrate on

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

the research model

Chapter 3 is research methodology chapter Firstly, research process is presented

in general Then, research design and sampling are also mentioned regarding to

qualitative method and quantitative method as well After that, the measurement scales

apply for the research factors will be determined clearly and suitably This chapter also

defines how to collect data and analyze the data collected to test the research hypotheses

proposed in chapter 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 is firstly mentioned and measurement reliability of each factor using Cronbach’s alpha is properly

presented as well Moreover, scale testing by using Exploratory Factor Analysis and

multiple regression analysis is explored in detail in the session Furthermore, this part

also discusses the method for collecting data used to test the hypothesis, and it analyses

the data received, its reliability and multiple regression as well

The last chapter, chapter 5 discusses the results and research findings This chapter gives conclusion, implication, and research limitations Finally, this thesis makes

suggestions for further research on the topic area

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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 field and its

impacts on the investment decisions and performance Next, heuristic of individual investors

is presented in detail like definition of heuristics and the prior studies on heuristics as well as

its classification are also explored deeply and its impact to the investment decisions and

performance More importantly, the content of investment decision 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 on economic research (Okur & Gurbuz, 2014) This theory was developed by Professor Daniel

Kahneman and Amos Tversky in 1979 Starting from empirical evidence, it described how

individuals evaluate losses and gains Kahneman & Tversky (1979) developed this theory

to remedy the descriptive failures of expected utility theory of decision making Prospect

theory attempted to describe decisions under uncertainty, and has also been applied to the

field of social psychology (Okur & Gurbuz, 2014) The authors argued that investors value

gains or losses according to an S-shaped utility function In other words, prospect theory

described some states of mind affecting an individual’s decision-making processes

including Regret aversion, Loss aversion and Mental accounting

In the theory, the reference point was determined by each individual as a point of comparison For wealth levels under the reference point investors are risk seekers that

means they are prepared to make riskier bets in order to stay above their preferred target

level of wealth Whereas for wealth levels above this reference point, the value function

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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 the study

by Finucane et al (2002) As such, the utility function is concave for gains meaning that

people feel good when they gain, but twice the gain does not make them feel twice as good

The utility function is convex for loss meaning that people experience pain when they 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 its

rational expectations derivative, was still the dominant paradigm for investor decisions in

finance and for economic decisions in general

2.1.2 Heuristics theory

According to Bramson (2007) heuristics is considered as a normative decision theory

Heuristics are defined as the rules of thumb, which makes decision making easier, especially

in complex and uncertain environments by reducing the complexity of assessing probabilities

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 the likelihood 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 subjective

probabilities What determines such beliefs? How do people assess the probability of an

uncertain quantity? People rely on a limited number of heuristic principles which reduce the

complex tasks of assessing probabilities and predicting values to simpler judgmental

operations

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 a negative

relationship between risk and benefits perception due to affect heuristic

It could be seeing that an investor always wants high returns against his investment but his or her decisions are explicitly affected by affect heuristic Hot stocks where an

investor perceives high risk may be neglected by investor regardless of its return due to

inverse relation of affect heuristic and “judgment and decision making” People already had

some images and symbols in their minds to perceive their risk and benefits and ultimately

they used those images and symbols to make their financial decisions in the stock market It

is observed that there are also some investors trading in the stock market who are illiterate

and have no sufficient financial educational background so that they are unable 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 on the 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 judgments quickly, are simplifying strategies used to approach complex problems and limit

explanatory information (Shikuku, 2010) Individual investors tend to make decisions

usually by trial and error method thus developing rules of thumb To put it simply, investors

use rules of thumb in order to process complex information so as to make investment

decisions Sometimes it may lead to a favorable decision, but sometimes, it may result in

unfavorable and poor decision outcomes (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 subjective impressions of “goodness” or “badness” can act as a

heuristic capable of producing fast perceptual judgments and also systematic biases For

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example, as Ganzach (2001) showed, people judge stocks that they perceive as “good” to

have low risks and high returns and judge stocks that they perceive as “bad” to have low

returns and high risks Specifically, for unfamiliar stocks, perceived risk and perceived

return are negatively correlated, as predicted by the affect heuristic In the meantime, for

familiar stocks, perceived risk and perceived return are positively correlated; riskier stocks

are 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, argue

for the prevalence of three general-purpose heuristics: representativeness, availability, and

anchoring and adjustment Later et al (2002) bring together the work of many other

researchers 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 writers studying the factors belonging to heuristics introduce three factors namely

representativeness, availability bias, and anchoring Waweru et al (2008, p.27) also listed

two factors named Gambler’s fallacy and Overconfidence into heuristic theory According

to Schwartz (1998) there is considerable evidence on general heuristics—notably

representativeness, availability, anchoring and adjustment, and affect (dealing with

emotions) but much less on the specific heuristics used in most decision-making

processes

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 This implies increased use of heuristics which is often a mostly inevitable

approach but not always beneficial (Fromlet, 2001) The interpretation of new information

may require heuristic decision-making rules (Finucane et al 2002) Chandra (2008) studied

behavioral factors and their impacts on investors’ attitude towards risk and behavioral

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decision making process The study concluded that individual investors suffer from

heuristics such as representativeness, overconfidence and anchoring, cognitive dissonance,

greed and fear, and regret aversion and mental accounting which all influence investor’s

perception of risk and subsequently his decision making

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

 Representativeness bias 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 a

tendency to assess the similarity of outcomes, instances and categories on relatively

salient and even superﬁcial features, and then to use these assessments and similarity as

a basis of judgment People assume like goes with like.” Because representativeness is

not influenced by several factors that should affect probability judgments, the implication

is that errors in judgment sometimes result (Barker & Nofsinger, 2012, p 259)

Representativeness may result in some biases such as people put too much weight on

recent experience and ignore the average long-term rate (Ritter, 2003, p.432)

 Availability bias Availability is the heuristic reﬂecting the weight given to information in place of probability or frequency That weighting is attributable to the ease of recall and the content

of what 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

or the weight that should be given to those differences in availability One type of

recognition of the importance of availability can be observed from the behavior of a

successful mutual fund manager, who is supposed to have reﬂected that he tended to avoid

stocks that most analysts and managers were celebrating because he was convinced that

such “availability” increased the likelihood that the shares of those companies were

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overvalued The tendency of investors to focus so overwhelmingly on national rather than

international stocks, particularly until the mid-1990s, and to miss proﬁtable opportunities

abroad, probably reﬂects reliance on the availability heuristic Perhaps the main bias of

availability 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 bias 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 in

judgment and decision making is more prevalent and more potentially catastrophic than

overconfidence.” On the contrary, when people overestimate the reliability of their

knowledge 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 to

suboptimal 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 and

determination, mental facility, and risk tolerance It can help to promote professional

performance and enhance other’s perception of one’s abilities, which may help to achieve

faster 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 market and such decisions need better insight and understanding Investment decision may have effect

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 to

systematic errors in the manner in which they process information (Pavabutr, 2002)

According to Macmillan (2000) each investment decision often involves complexity and uncertainty Complexity is reflected, in part, by the number of alternative courses of

action from which the maker can choose Uncertainty is inherent in all

decision-making but particularly pertinent to the investment decision-maker where the implications

of their decisions are often very significant for the organization Moreover, investors are

usually trying to fulfil multiple objectives in their investment decisions and therefore have

to make trade-offs between expected return and riskiness Dean and Sharfman (1996) note

that it is unlikely that the influence of such forces eliminates the impact of choice on

decision effectiveness as it is hard to imagine a decision in which all potential choices will

be equally successful or unsuccessful

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

Rather than searching systematically, many investors may consider only stocks that first

catch their attention (e.g., stocks that are in the news or stocks with large price moves) This

will lead individual investors to buy attention-grabbing stocks heavily Since most

individual investors own only a small number of stocks and only sell stocks that they own,

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 her own transaction history and resulting cash flows We attempt to shed light on the investment

performance 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) analyze the profitability of common stock trades (as distinct from positions held)

by individual investors Lin and Swanson (2003, p.208) measure investment performance

using three criteria of returns (raw returns, risk-adjusted returns, and momentum-adjusted

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returns) through five-time horizons (daily, weekly, monthly, quarterly, annually) They

recognize 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 overconfidence than 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 their

detriment When combined, these observations predict that men will trade more than women

and that excessive trading will hurt their performance Consistent with these predictions,

Barber and Odean (2001) document that men trade more than women; the annual turnover

rates of men are about 80%, while those of women are 50% The excessive trading of men

leads 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

men tend to trade more aggressively than women Neither men nor women appear to have

stock selection 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 Serveral authors mainly used the secondary data of investors’ results in the security markets to

measure the stock investment performance (Lin et al., 2003) while others use primary data

collected 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’s high 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 when people try to infer from too few samples (Barberis & Thaler, 2003, p.1065) In

the stock market, when investors seek to buy “hot” stocks instead of poorly performed ones,

this means that 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 again consistent

with the operation of the representativeness heuristic However, this belief among

executives is clearly contrary to traditional finance theory, which teaches that risk and return

are positively correlated

In the stock market, for example, investors might classify some stocks as growth stocks based on a history of consistent earnings, growth, ignoring the likelihood that there

are 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 is

representative of the descriptions of the companies but ignoring the reliability of those

descriptions results in overreliance on stereotypes and the underweighting of base rate

information Kahneman & Tversky (1974) showed that people had a tendency to categorize

events as typical representative of a well-known class and then, in making probability

estimates to overstress the importance of such categorization disregarding evidence of the

underlying probabilities

Representativeness helps to explain why many investors seem to extrapolate price movements Many investors appear to believe that if prices have been rising in the past then

they will continue to rise, and conversely with falling prices The concept of

representativeness suggests that this is because those investors see an investment with recent

price increases as representative of longer-term successful investments, conversely with

price falls DeBondt & Thaler (1985) argued that because investors are subject to the

representativeness bias, they could become too optimistic about past winners and too

pessimistic about past losers Trading that is influenced by the representativeness bias can

move 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 in Vietnam, however, there is no consistent empirical results on the representativeness bias towards

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.

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2.4.2 Availability bias and investors’ decision-making

According to Shefrin (2002), availability bias occurs because investors reply on information to make informed decisions, but not all information is readily available

Investors tend to give more weight to more available information and to discount

information that is brought to their attention less often The stocks of corporations that get

good 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 return

potential

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

in the media and corporate releases, because their broker’s or advisor’s recommendations

are 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 which they can bring to mind similar instances or associations Biases occur when “availability”

and true frequency diverge For example, Klibanoff, Lamont, & Wizman (1998) showed

that 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 of investors

As such, one hypothesis is suggested as follows

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

decision-2.4.3 Overconfidence and investors’ decision making

Most of the overconfidence models predict high trading volume in the market when there are overconfident traders Moreover, at the individual level, overconfident investors

trade more aggressively: The higher the degree of investor overconﬁdence, the higher the

investor’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 trading

volume observed in ﬁnancial markets “is perhaps the single most embarrassing fact to the

standard ﬁnance paradigm” and that “the key behavioral factor needed to understand the

trading puzzle is overconﬁdence"

In this research, overconfidence factor is used to measure its impact levels on the investment 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, on overconfidence and bounded rationality) are being put forth to explain return patterns like

long-horizon reversals The assumptions behind these theories of investor behavior are

founded in psychological research or common sense Clearly, however, this line of research

could benefit from a more complete picture of how investors actually behave and how they

differ from one another in the way they react to the same information (Grinblatt and

Keloharju, 2000)

A number of recent contributions have documented interesting regularities in the past-return-based behavior of investors Brennan and Cao (1997) presented a theoretical

model and empirical evidence that supports the view that foreign investors should pursue

momentum strategies and achieve inferior performance because they are less informed than

domestic investors Froot et al (2000) and Choe et al (1999) found that foreign investors

tend to be momentum investors, the latter paper focusing on short past-return horizons

According to Grinblatt and Keloharju (2000) a simultaneous analysis of the investment behavior and performance of all investor categories has been impossible until

now because of data limitations Different research methods, different data frequencies,

different horizons for past returns, and different institutional arrangements unavoidably blur

the comparison of the results and make it difficult to identify general patterns behind the

behavior and performance of isolated investor categories

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Anderson, Henker and Owen (2005, p.71) concluded that individual investors who make higher amount of transactions may result greater returns than individuals with fewer

transactions may Kim and Nofsinger (2003, p.2) claimed that stocks experiencing the

greatest increase in individual possession can earn a negative abnormal return during the

year; whereas, stocks that experience the most decrease in individual ownership may earn

a positive abnormal return They also go additionally insight into buying and selling

behaviors and study the past performance of these bought and sold stocks The authors find

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 positively related to comprehensiveness/rationality and formalization in the investment decision-

making process

It could be seen that relationship between decision making and performance of individual investors is suggested for some period in Vietnam, nevertheless, there are no

empirical study whether the individual investors of decision making impacts on

performance 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

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Accordingly, four following hypotheses are suggested:

Hypothesis H1: Representativeness has positive impact on the individual investors’

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 that heuristic factors impact the investment decision making and performance of individual

investors in the financial markets, especially in the stock markets The chapter analyzes the

theoretical foundation in detail as prospect theory and heuristics theory Moreover, the

thesis explores heuristics of individual investors, consisted of definition and classification

of heuristics More importantly, it also investigates the relationship between decision

making and performance of individual investors in the Ho Chi Minh stock market as well

as developing hypotheses and building conceptual model

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CHAPTER 3: RESEARCH METHODOLOGY

This chapter presents brief description of the research methodology used for the research 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: induction and deduction When deductive approach is employed, researchers start with the existing

theory and logical relationships among concepts, and then continue to find empirical

evidences In contrast, in inductive research, theory is developed from the observations of

empirical reality and researchers infer the implications of the findings for the theory that

prompted 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 and decision making of investors, which are already “out there”, is the main aim, instead of

inferring and building theory, deduction approach seems to be the most appropriate choice

The study starts with reviewing the behavioral finance theories in general and in stock

market in particular, to get the theoretical and conceptual context as well as empirical

findings of previous researches, from which the research model and hypotheses are

proposed Then, the questions used in interviews and questionnaires are prepared This

process is quite consistent with deductive approach which emphasizes that researchers may

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 data collection

and analysis Comparison between the results of the research and the existing theories

is made to find out the differences Deductive approach is usually associated with

quantitative researches, which involve collecting of quantitative or quantifiable

qualitative data and analyzing statistical methods, which is also compatible with

quantitative research strategies

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Research problem Research objective Research scope

LITERATURE REVIEW

RESEARCH DESIGN

Pilot study

Sampling design

Refine measures &

measurement scale

Refine questionnaire

Main survey

DATA ANALYSIS

Reliability testing (Cronbach’s alpha)

Validity testing (EFA)

Hypothesis testing (Regression)

Conclusion and implication

Figure 3.1 Research process

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21

Firstly, research problem was defined, and then research objective and research questions 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 heuristic

factors influencing decision making and performance of individual investors to find out the

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 some previous

studies Next step is research design with 2 sub-steps:

Pilot study: a study was conducted by interviewing around two managers of securities company face to face about the content, the number and the structure of questions

in preliminary survey to test the survey and measure before launching the main survey

Moreover, we also got a draft survey with 94 investors for testing reliability and run EFA

for the research

Main survey: was conducted survey through mail, social network, or sending the hard copy of survey directly to individual investors thanks to the brokers of securities

company Data collection was done one month later

After that, collected data was cleaned and used to test reliability of scale and validity

of questionnaire through Cronbach’s alpha coefficient and Exploratory Factor Analysis

(EFA) method Multiple regression method was used to evaluate the hypotheses which the

implication and finding were stated and reported

3.2 Research design

The questionnaire is divided into four parts: personal information, heuristic factors influencing investment decisions, decision making and investment performance

In the part of personal information, nominal measurements are used Nominal scales are

used to classify objects

This research is based on the theories of behavioral finance: Heuristic theory, Prospect theory, impacts of behavioral factors on investors’ decision-making, which are

mentioned by Waweru et al (2008, p.24-38) and many other authors cited in the

literature review, 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 their agreement with the impacts of heuristic factors on their investment decision

as well as with the 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 and questions 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 decision making Three of typical heuristics we namely study representativeness, availability and

overconfidence Particularly, representativeness was measured by three observed variables,

developed by DeBondt & Thaler (1995), used a five-point Likert scale, and modified 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

Heuristics factors Representativeness

RE1

Investors buy "hot" stocks and avoid stock that have performed poorly in the recent past?

Kengatharan (2013)

RE3

Investors use trend analysis

of some representative stocks to make investment

Le & Doan (2011)

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decision for all stocks that they invest?

Availability bias

AVA1

Investors rely on their previous experiences in the market for their next investment?

Kengatharan (2013)

AVA2

In investors’ opinion it is safe to invest in local stocks rather than to buy international stocks

to outperform the market?

Kengatharan (2013) Luu (2014)

OVER2

Investors use predictive skills for investment decision making

Qureshi (2012)

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 of

individual investors about their investment Investors’ decision making was measured by

three observed variables, developed by Hassan Et Al (2013) and Qureshi (2012), used a

five-point Likert scale as follows:

Table 3.2 Types of measurement for individual investors’ decision making

Investors’

decision making

Investors’

decision making DEC1

Investors’ investment has a lower risk compared to the market in general

Hassan Et Al (2013)

Qureshi (2012)

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DEC2

Investors’ investment in stocks has high degree of safety

Qureshi (2012)

DEC3

Investors’ investment has the ability to meet interest payment

Qureshi (2012)

3.2.3 Measure of investment performance

The prior authors mainly use the secondary data of investors’ results in the security markets to measure the stock investment performance (Lin and Swanson (2003), Kim and

Nofsinger (2003) and so on) However, this research asks the investors to evaluate their own

investment performance, so that the measurements of investment performance follow the

research of Oberlechner and Osler (2004) for the investment return rate In more details, the

return rate of stock investment is evaluated by objective and subjective viewpoints of

individual investors The subjective assessment of investors is made by asking them to

compare their currently real return rates to their expected return rates while the objective

evaluation is done by the comparison between the real return rates and the average return

rate of the security market Besides, the satisfaction level of investment decisions is

proposed in this research as a criterion to measure the investment performance In reality,

there are investors felling satisfied with their own investment performance even if their

investment profits are not high; in contrast, other investors do not feel satisfied with their

investments even when their profits are relative high Therefore, the satisfaction level of

investment decisions together with investment return rate are proposed as measurements for

the investment performance in this research

Investors’ performance was measured by three observed variables, developed by

Kengatharan (2013), used a five-point Likert scale, and modified by the author as follow:

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Table 3.3 Type of measure for individual investors’ performance

Investment performance

PER1

Investors feel satisfied with their investment decisions in the last year (including selling, buying, choosing stocks and deciding stock volume)

Kengatharan (2013)

PER2

The return rate of the investors’

recent stock investment meets their expectation?

Kengatharan (2013)

PER3

Investors’ investment in stocks has demonstrated increased revenue growth in last year

Hassan Et Al (2013)

Qureshi (2012)

3.3 Pilot test

Based on the literature, to prepare a draft of questionnaire for pilot test, the author interviews with two managers of the HOSE are conducted to have deeper understandings

about 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 managers

are invited to separate interviews Since these managers are responsible for trading

surveillance and market information for the HOSE, which has to supervise the securities

companies as well as trading activities at Ho Chi Minh stock market, they are expected

to have deep understanding about the stock market and investors’ behaviors Hence, it is

believed that they are qualified interviewees for this study, who can provide significant

analysis 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 sure

that the measurements are reliable However, many statisticians believe that it c an be

acceptable if the Cronbach’s alpha is over 0.6 (Shelby, 2011, p.143) Besides, statisticians

recommend that it is necessary to consider the corrected item-total correlations when using

the 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 chooses

the acceptable Cronbach’s alpha is 0.6 or more, with the corrected item-total correlation

index is 0.3 or more because the measurements of financial behavior are new to the

stockholders 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 alpha

test is finished by SPSS software

Then, EFA is used to explore the factors that the heuristic variables, investment decision making and investment performance of the questionnaire (question 13 to question

26) belong to EFA is used to reduce the number of items in the questionnaire that do not

meet the criteria of the analysis (O’brien, 2007, p.142) In this case, EFA is utilized to test

the hypotheses shown in the research model of Chapter 2

In this research, the following criteria of the exploratory factor analysis are applied: Factor

loadings, KMO, Total variance explained, and Eigen value

Based on 94 samples collected from a pilot studies, the results show that, all most the measurement 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 Total

Correlation values were over 0.3 Therefore, this variable is also accepted for Exploratory

factor analysis later It could be seen that the scale designed in this research is meaningful in

statistic and has the necessary reliability

Besides, after processing factor analysis for the independent variables and the dependent variable by the Varimax method has four factors are formed They are

representativeness bias, overconfidence bias, decision making and performance of

individual investors (value of availability variable (AVA2 is removed because it is loaded

double, AVA1 and AVA3 is loaded in the same component of representativeness variable

and overconfidence, details could be seen in Appendix 1, so that we renamed AVA1 to RE4,

and AVA3 to OVER3 From now on, the new model is suggested that representativeness

and overconfidence bias influencing decision making and performance of individual

investors as follows:

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thus, the more reliable result is (Saunders et al., 2009, p.219) Nevertheless, the sample size

depends on researchers’ available resources including time, finance and human (Saunders

et al., 2009, p.212) Hair, Black, Babin, Andersion and Tatham (1998, p.111) suggest that

with quantitative research, at least 100 respondents should be studied in order 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 response when 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

cannot provide 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 the whole population of individual investors In contrast, stratified random

sampling allows us to stratifying the population by a criterion (in this case, the brokerage

market share), then choose random sample or systematic sample from each strata (Bryman

& Bell, 2007, p.187) Stratified sampling ensures that the sample is distributed in the same

way as the population (Bryman & Bell, 2007, p.187) The number of questionnaires were

sent 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

been chosen 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, the

factor loading was tested by Exploratory Factor Analysis and the hypotheses were tested by

Multiple Regression A data set satisfied requirement of Exploratory Factor Analysis was

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 sample required

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 sample required 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 be satisfied 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 of

variables 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 is 0.3 or more

- Exploratory factor analysis: EFA

As mentioned in pilot test, after surveying, EFA method with Varimax rotation was used to analyze observed variables of heuristic factors influencing decision making and

performance of individual factor KMO (Kaiser-Meyer-Olkin) test and Bartlett test were

also used to measure the compatibility of sample

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- Multiple regression analysis

The Multiple Regression Analysis was used to test the research hypotheses of this study In accordance with Leech et al (2005), there are many assumptions to consider but

we will focus on the major ones that are easily tested with SPSS The assumptions for

multiple regression include the following: that the relationship between each of the predictor

variables and the dependent variable is linear and that the error, or residual, is normally

distributed and uncorrelated with the predictors A condition that can be extremely

problematic as well is multicollinearity, which can lead to misleading and/or inaccurate

results Multicollinearity (or collinearity) occurs when there are high intercorrelations

among some set of the predictor variables In other words, multicollinearity happens when

two or more predictors contain much of the same information

Although a correlation matrix indicating the intercorrelations among all pairs of predictors is helpful in determining whether multicollinearity is a problem, it will not always

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 The multiple

regression analysis would be run by SPSS 16.0

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

 The first multiple regression model: Y’ = β1X1 + … + βnXn + ε Where in:

- Y: Investment decision making variable (dependent variable)

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

- ε: Radom error

 The second multiple regression model: Y’’ = β1Y’1 + ε Where in:

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

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

- ε: Radom error

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3.5 Chapter summary

Collected data was analyzed and interpreted in a series of stage First, the demographic profile of respondents was summarized and analyze Second, the reliability of

the items used in measuring the constructs was validated using Cronbach’s alpha Third, the

correlation of the independent variables and the dependent variable was ascertained through

Promax method At last, standard multiple regression analysis was used to establish the

statistical significance of the model and the predictive power of each independent variables

in explaining the two dependent variables (investment decision making and investment

performance)

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CHAPTER 4: FINDINGS AND DISCUSSIONS

The purpose of this chapter is to present findings which were collected from the actual 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 heuristic

variables, investment decision making and investment performance of the questionnaire

belong 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 Minh Stock Exchange, 186 respondents are reported, so that the respondent rate is 47%, a moderate high

rate for a postal questionnaire survey The data was gathered from securities company in

Hochiminh city The 186-respondent sample with the characteristics of gender, age, marital

status, education, years of working, income, years of attendant in the stock market,

securities company, course of stock, time of course, total amount of money invested in the

stock market and total amount of money invested in the stock market last year are

summarized as Table 4.1 that gives a detailed description of the demographic statistics for

the respondents:

Table 4.1 Descriptive statistic of respondent’s characteristics

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MayBank Kim End Securities

Course of stock market

Total amount of money

invested in the stock

Total amount of money

invested in the stock

market last year (USD)

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34

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 predominantly

between 18 and 35 years, which account for 34% Regarding marital status, the single

respondents account for more than 55%, while married respondents are 36%, the rest of

divorced is 9% With the education factor, the highest percentage is the bachelor degree

group that accounts for 73% Majority investors with years of working is less than five

years (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 (it

account for 28%) Almost investors are holding account in Hochiminh City Securities

Corporation (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% for from 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 which the items that make up the scale “hang together”, Cronbach’s alpha coefficient is calculated

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 underlying construct (Pallant,

2001) For scale to be reliable, its Cronbach’s alpha value should be above 0.6 (George &

Mallery, 2003)

The above guideline indicates that the higher the Cronbach’s alpha value is, the more reliable are the items measuring a give construct Cronbach’s alpha closer to 1.0 is preferred

A Cronbach’s alpha value of 0.9 and above was regarded as the most reliable of scales,

while a scale that has a Cronbach’s alpha value that is below 0.5 is regarded as unreliable

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 the factors, which are identified in the factor analysis This test is done to make sure that

the measurements are reliable for further uses

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Cronbach's Alpha if Item Deleted

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

Tiêu đề	Heuristics Influencing Investment Decision Making and Investment Performance, Evidence from Individual Investors in Vietnam
Tác giả	Nguyen Thi Thanh Thuy
Người hướng dẫn	Dr. Tran Phuong Thao
Trường học	University of Economics Ho Chi Minh City
Chuyên ngành	Finance / Investment
Thể loại	master thesis
Năm xuất bản	2015
Thành phố	Ho Chi Minh City

Định dạng
Số trang	80
Dung lượng	1,82 MB