Determinants of behavior intention to use derivative securities a study on individual investors behaviors in stock market of vietnam

2 Abstract This study investigates the determinants of behavior intention to use derivative securities on individual investor ‘s behaviors in stock markets of Vietnam.. The results show

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

-Trang Nguyen Thanh Phuong

DETERMINANTS OF BEHAVIOR INTENTION TO USE DERIVATIVE SECURITIES A STUDY ON

INDIVIDUAL INVESTOR'S

BEHAVIORS IN STOCK MARKET

OF VIETNAM

MASTER OF BUSINESS (honours)

Ho Chi Minh City – Year 2018

UNIVERSITY OF ECONOMICS HO CHI MINH CITY

International School of Business

-Trang Nguyen Thanh Phuong

DETERMINANTS OF BEHAVIOR INTENTION TO USE DERIVATIVE SECURITIES A STUDY ON

INDIVIDUAL INVESTOR'S

BEHAVIORS IN STOCK MARKET

OF VIETNAM

MASTER OF BUSINESS ADMINISTRATION

SUPERVISOR: DR Trần Phương Thảo

Ho Chi Minh City – Year 2018

Acknowledgement

I would like to express my sincere thankfulness to my supervisor, Dr Tran Phuong Thao, who made me believe in myself and gave me the possibility to complete the thesis Her guidance helped me in all the time of research and writing this thesis I am sure that this thesis would not have been possible without her support

I would like to express my gratitude to all staffs in ISB who supported necessary materials and helped submit my papers

My sincere thanks also go to friends and colleagues who participated in the pilot study that led to the development of the final survey questionnaire and their support over the time when I was busy to conduct the research

Especially, I would like to give my special thanks my family for supporting

me spiritually throughout my life

Trang Nguyen Thanh Phuong

2

Abstract

This study investigates the determinants of behavior intention to use derivative securities on individual investor ‘s behaviors in stock markets of Vietnam Those determinants include attitude towards behavior, subjective norm, perceived behavioral control It also examines the effect of overconfidence, excessive optimism, herd behavior, risk aversion toward attitude towards behavior

An empirical test was conducted with a sample of 317 individual investors by means of structural equation modeling The results show that perceived behavior control has the strongest impact on the three main factors affecting behavior intention to use derivative securities with a coefficient of 0.426 The other two factors, including attitude towards behavior, subjective norm, have a direct impact

on behavior intention to use derivative securities with coefficients of 0.356 and 0.216 respectively On the other hand, overconfidence, excessive optimism, herd behavior and risk aversion have direct effect on attitude towards behavior However, herd behavior and aversion effect attitude towards behavior with positive coefficient while overconfidence, excessive optimism affect with negative coefficient Finally, age and education play an important role in behavior intention

to use securities derivatives while there is no difference between men and women who intend to use derivative securities

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Table of Contents

Acknowledgement 1

Abstract 2

List of figures 5

List of tables 6

List of abbreviations 7

1 Introduction 8

2 Theoretical background and hypotheses 13

2.1 Foundational Theory 13

2.2 Research model and hypotheses 16

2.2.1 Attitude towards behavior (ATB) 17

2.2.2 Subjective Norm (SN) 21

2.2.3 Perceived behavioral control (PBC) 23

2.2.4 Demographic factors 24

3 Research methodology 26

3.1 Research approach 26

3.2 Questionnaire design 28

3.3 Data collection 32

3.4 Research Method 33

3.4.1 Pilot test 33

3.4.2 Main survey test 34

4 Data analysis and results 37

4.1 Descriptive statistics 37

4.2 Reliability Analysis 38

4.3 Exploratory Factor Analysis (EFA) 40

4.4 Confirmatory Factor Analysis (CFA) 43

4.4.1 Composite Reliability 43

4.4.2 Convergent Validity of all variables 45

4.4.3 Discriminant Validity of all variables 46

4.3 Structural Equation Modeling (SEM) 48

4.4 Indirect Effects of Behavior intention to use 49

4.5 Independent Sample T-test and Oneway Anova 50

4.5.1 Gender 50

4.5.2 Education 51

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4.5.3 Age 53

4.6 Hypothesis testing results 54

5 Discussion & conclusion 55

5.1 Discussion 55

5.2 Implications for managers 57

5.3 Conclusion 58

5.4 Limitations and directions for future research 59

REFERENCES 60

APPENDICES 63

Questionnaire (English version) 63

Questionnaire (Vietnamese) 67

A Frequencies 71

C Reliability 73

D Factor Analysis 81

E Confirmatory Factor Analysis 87

F Structural Equation Modeling 93

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List of figures

Figure 1 The theory of planned behavior – (Ajzen, 1991) 14

Figure 2 Research model 17

Figure 3 Main steps of research process 28

Figure 4 First Measurement Standardized Modelling 47

Figure 5 Structural Equation Model 48

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List of tables

Table 1 Measurement scale 30

Table 2 Sample size Criteria (Comfrey & Lee, 1992) 32

Table 3 Criteria for Measurement Model 35

Table 4 Descriptive statistics 37

Table 5 Remiability Test Results 38

Table 6 KMO and Bartlett's Test 40

Table 7 Total Variance Explained 41

Table 8 Pattern Matrix 42

Table 9 Value of Composite Reliability 45

Table 10 Value of Average Variance Extracted 45

Table 11 Discriminant Caculating Result 46

Table 12 Square root of AVE results 46

Table 13 Regression Weights of Model 49

Table 14 Indirect effects on Behavior intention to use 50

Table 15 Independent Samples Test 50

Table 16 Test of Homogeneity of Variances 51

Table 17 Anova tesing result 52

Table 18 Descriptives Statistics 52

Table 19 Test of Homogeneity of Variances 53

Table 20 Anova tesing result 53

Table 21 Descriptives Statistics 53

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List of abbreviations

1 AVE Average Variance Extracted

ATB Attitude Towards Behavior

2 CFA Confirmatory factor analysis

3 CR Composite Reliability

5 EFA Exploratory factor analysis

6 HOSE Ho Chi Minh City Stock Exchange

7 HNX Hanoi Stock Exchange

12 TRA Theory of Reason Action

13 TBC Perceived Behavioral Control

14 TPB Theory of Planned Behavior

15 SEM Structural Equation Modeling

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1 Introduction

Derivatives are a valuable financial instrument that depends on the price of the underlying asset Basically, derivatives can be understood as a type of contract for a future but predefined transaction Derivative instruments are conduct as a tool used to manage and control risk Specifically, derivative products are used to prevent risk when asset values fluctuate In addition, derivatives are considered as hedging instruments against volatility of commodity prices In the derivatives market, there are two main markets: the financial derivative market and commodity derivatives market In this study, the author will focus on the financial derivatives market on the stock market, exactly the Vietnam stock market

To date, Vietnam stock market has been established for quite a long time, but just over 11 years the stock market of Vietnam has a significant development; Two stock exchanges have been established, namely the Ho Chi Minh City Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX), a stock exchange depository center has been established, nearly 89 securities companies are operating and more than 700 companies have listed their shares and fund certificates on two Vietnamese stock exchanges By 2013, at the Ho Chi Minh City Stock Exchange, the stock market capitalization has reached over $ 32 billion, equivalent to 25 percent of GDP in 2013, the number of accounts of investors reached over 1.3 million trading accounts, of which foreign investors had about 16,000 accounts, compared to the end of 2007, the total number of securities trading accounts has increased by more than 3.5 times and the number of accounts of foreign investors has nearly doubled, proving that the demand of securities investors has increased

In the body of literature, there are a wide range of studies examined the behavioral intention of customers including Jeong & Lambert (2001), Burton, Sheather & Roberts (2003), Liu, Lu, Marchewka & Yu (2004), Amoako & Gyampah (2007), Gu, Lee & Suh (2009), Han & Kim (2010) In the context of financial market, the behavioral intentions have been examined in a plenty of studies such as Berry, Parasuraman & Zeithaml (1996), Athanassopoulos (2000), Auh, Bell, McLeod & Shih (2007), Keh & Xie (2009), Bolton, Bitner & Mende (2013), Saeidi, Sofian & Saeidi (2015)

Derivatives are used by large corporations and companies to manage exchange rate risk, loans or financial expenses Based on mentioned studies, derivatives are very useful in risk management Through the option call and put option, market risks are prevented using forward and future contracts Derivatives are indispensable products in the deep, broad and diversified development of financial markets To date, derivatives have developed rapidly, are strong on a global scale and play an increasingly important role in the financial and monetary system These tools show prominent features in risk prevention, meeting the needs and interests of many market participants However, it also shows the complexity and if not good management can cause economic instability

In Vietnam, derivative products originating from currencies and commodities have been in use for many years On commodity derivatives, the Buon Me Thuot

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coffee trading center was established in 2006 with the main function of organizing the trading of coffee produced in Vietnam in the form of spot and forward delivery (Forward contract) To date, the use of currency derivatives has been extended to many domestic and foreign commercial banks, with a variety of instruments such as swaps, options, future contract The financial derivatives market is being prepared for establishment and development in the near future In order to help individual investor familiarize with the new investment instrument, in the first phase of the market operation, two basic futures contracts will be introduced including futures

on the stock index (VN30 and HNX30) and futures on government bonds Other derivative contracts on asset types will be issued later

In the derivatives market, there are four main contributing factors to the derivative market: infrastructure, legal framework, products and people (Hull, 2006) In recent years, the government has developed, developed and prepared the legal framework and technical infrastructure to operate the derivatives market However, the government cannot upgrade human factors as they do with infrastructure, legal framework or technical There are many models of human behavior, each of which is applied in different circumstances Human behavior for different problems can be predicted very differently Similarly, in financial environments, for different financial products, human behavior is also very different (Mullainathan & Thaler, 2000; LeBaron, 2001; Shiller, 2002) In other words, if individual investors are not involved in the derivative financial market, this market will be ineffective and unable to grow in the future In addition, the internal factors such as education, experience, gender, culture, individual investors are also

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influenced by a number of psychological factors Although individual investors have gradually become more professional in investment, some empirical studies of market performance show evidence that the VN-Index is not random One of the reasons is the existence of psychological factors affecting the behavioral intentions

of individual investors in the stock market (Phan & Chu, 2014) In accordance, the decision of investors although based on reasonable analysis, is influenced by psychological factors (Murgea, 2008, Sehgal & Singh, 2012)

Hence, this study is necessary to conduct in the current context of Vietnam to understand the level of investors toward derivative financial instruments when the derivative financial market is put into operation The study also is expected to identify factors that affect intention of investors in the use of derivative financial instruments in Vietnam

The purpose of this study is to explore the factors that influence the decision

to participate in the financial derivative market of the investor in Vietnam On August 10, 1977, the Derivative Market was officially opened The Vietnamese State Securities Commission has issued certificates of eligibility for trading of derivative securities to five securities companies including Saigon Securities Inc (SSI), Vietnam Prosperity Securities Company (VPBS), Vietnam Securities Corporation Vietnam Investment and Development (BSC), MB Securities (MBS) and VNDIRECT Securities (VND) Hence, this study focuses on investors who are dealing in these five securities companies

The results of this study will help to learn about the behavior of investors in the derivative securities market in Vietnam Determining the level of impact of

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factors on the behavior of individual investors in the derivatives market is important It will help to capture the behavior of investors in the new derivative market as in Vietnam today This result is very important for both brokers as well as the State Security Commission of Vietnam Knowing the behavior of investors can help them improve the intention to use derivative instruments in securities market to manage and control risk in investing This can help to create market liquidity as well as increase the number of investors in Vietnam's stock market

2 Theoretical background and hypotheses

2.1 Foundational Theory

The theory of planned behavior (TPB) was developed by Ajzen and Fishbein

in 1980 This theory is considered to be pioneering in the field of psychosocial research and is widely applied in scientific research to learn about human behavior The main content is shown in the studies of Ajzen (1985, 1991, 2002) The relationship between intention and behavior has been empirically tested in numerous studies in many areas (Ajzen, 1988; Ajzen & Fishben, 1980; Canary & Seibold, 1984; Sheppard, Hartwick, and Warshaw, 1988) It wasdeveloped from the theory of reasoned action (TRA)by (Ajzen and Fishbein 1980)

Theory of reasoned action (TRA) focuses on understanding the motivational factor of personal behavior consisting of two main components: attitude towards behavior (AT) and subjective norms (SN) Although the TRA is widely accepted in literature, the theory is still limited Inability due to lack of opportunities or resources such as time, capital, skills To overcome these limitations, Ajzen (2002)

Figure 1 The theory of planned behavior – (Ajzen, 1991)

According to the theory of planned behavior (TPB), behavioral control can affect behaviors in two ways: PBC may affect intentions of behavior and PBC can directly influence behavior Both of these controlling effects may be related to the course of action of investors In addition, other factors that affect investors' actions are internal factors and external factors Internal factors include feelings, personal knowledge, experiences and skills External factors include financial resources, time

or partner (Ajzen, 2005) In TPB theory, the three main factors are behavioral attitudes, subjective norms, and perceived behavioral control These factors have been proven and confirmed in numerous researches

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Behavioral intentions have been predicted by attitudes, subjective norms, perceived behavioral control in the past The theory of plan has been tested for many years and has been shown to be reliable, effective through numerous empirical studies TPB has been widely used in the prediction of human behavior in business (Krueger & Carsrud, 1993), the study of bad habits (Chang, 1998) or tobacco control behaviors for adults (Hu & Lanese, 1998) In addition to predicting and controlling personal behavior, TPB is also used to predict behavior that benefits the community For example, research on resource sharing in the organization (Bolloju, 2005) or decision making in human resource management (Carpenter & Reimers, 2005) TPB is also analyzing the intention to use a variety of new forms, such as the use of internet in shopping (Hsieh & Rai, 2008), the intention to use technology devices in households (Pavlou & Fygenson, 2006), or intention to use credit cards (Rutherford & DeVaney, 2009)

TPB is widely used in the financial and securities markets (Gopi & Ramayah, 2007) Gopi and Ramayah (2007) use TPB to study the intent of online home-based business, or use internet banking for securities trading (Serkan, 2004) All of the above may indicate that TPB is a good model for predicting behavior In a famous study by East (1993), he used TPB to accurately predict the behavior of securities investors in the short term According to Ajzen (2005), in the short term, TPB shows that "people intend to take action when they evaluate it positively, when they feel social pressure to do it, and when they believe they have the means and the opportunity to do it” This view of motivation shows the ability to explain the main factors that affect individual investment behavior

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The stock market in Vietnam has been developing for a long time, however, very few studies have used TPB to study stock investment behavior Most of the previous studies focused on behavior finance theory, financial literacy or demographic factor to investment behavior On the other hand, derivative is an effective risk management tool in securities trading has been recently applied in Vietnam This attracted the author to use TPB as a theoretical background for developing model research and studying the behavior of intention to use derivative

in securities investment in Vietnam

2.2 Research model and hypotheses

As mentioned above, TPB has many applications in analyzing human behavior and has been tested in hundreds of studies worldwide In this section, the author proposes a research model to test the factors that influence the intention to use derivative in securities investments The main purpose is to determine the factors that influence the intention to use as well as the relationship between factors

in the research model In addition, the author identifies psychological determinants that have an indirect effect on intention to use (Phan & Zhou, 2014) The research model and hypotheses will be presented below:

Behavioral intentions according to the TPB theory are intentions to perform some behavior, in this study the intention is to use This makes sense behavior a dependent variable in many experimental studies using TPB as the theory Studies

of behavioral intent have been demonstrated and determined through many empirical studies According to Ajzen (1991), behavioral intentions have been strongly influenced by motivation factors (elements in the TPB model) Behavioral

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intentions imply that people will be willing to act or attempt to do something Therefore, the intention to use derivative indicates that investors are likely to use derivative in securities trading

Figure 2 Research model

2.2.1 Attitude towards behavior (ATB)

Attitude is described as the impact of each positive or negative emotion on a particular behavior (Fishbein & Ajzen, 1980) Attitude of an individual is measured

by the belief and appreciation for that behavior Consequently, attitudes have been widely used to determine predictions of future behavior On the other hand, attitudes have been updated with new definitions as reaction of individual behavior

to different objects (Ajzen & Fishbein, 2000)

In other words, if a person is influenced by attitudes towards a particular behavior, they will be intent on performing that behavior higher than others In contrast, individuals are not attracted by the behavior, they will not intend to do that There are many studies examining the impact of attitudes on behavioral

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intention The results show that there is a strong relationship between attitude and behavioral intention If an individual who has a positive attitude will tend to act In contrast, individuals with negative attitudes will have a tendency not to perform such behavior, even criticize (Gibler & Nelson, 1998) Therefore, attitude is one of the determinants of personal behavior On the contrary, individuals with negative attitude will have a tendency not to perform such behavior, even criticize or obstruct (Gibler & Nelson, 1998) Therefore, attitude is one of the determinants of personal behavior

For the attitude towards behavior, Ajzen and Fishbein (1980) believe that the attitude toward any concepts is one of the feelings about one's favourableness and unfavourableness Thus, attitude towards behavior is only the end result while there are small factors that affect attitude towards behavior Phan and Zhou (2004) have identified four factors that directly affect attitude towards behavior, including overconfidence, excessive optimism, herd behavior, and risk aversion Thus, attitude towards behavior is considered as a dependent variable influenced by four psychological factors as follows:

Overconfidence is the expression of self-confidence behavior of some knowledge or decision Overconfidence has been studied extensively in the stock market (Barberis & Thaler, 2003) In trading, many investors are confident about their knowledge, their ability in reality is not so Transaction results are far from their confidence However, they do not see this problem Mostly, investors are overconfident believe that they choose the best stock and the best time to sell the stock for the highest profit

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Excessive confidence influences their decision Excessive confidence also causes them to ignore other useful data in the investment decision that leads to their erroneous investment decision (Odean, 1998; Wang, 2000; Gervais & Heaton, 2002; Grinblatt & Keloharju, 2009; Montier, 2009) Overconfidence also greatly influences the use of derivative in securities transactions

Most investors are overconfident about their ability, so they will not use risk control measures in their trades, particularly derivative An over-confident investor performs high-frequency transactions and thus increases the volume and volatility

of the market while their expected returns decrease (Gervais, Heaton and 2002) Therefore, the confidence of a person's ability to directly influence the investment attitude, leading to more frequent transactions

For the overconfident investor, they are too confident about their investment information as well as their capabilities This will cause them not to appreciate the reality of the market as well as the stock they are holding Excessive optimism is a combination that holds overconfidence and over optimism This is evident in bad situations, especially when the market is on the decline They always believe that bad situations happen only in the short term, so it will not affect their portfolio much

Or they believe that their portfolios are very good, will rebound in a short time so there is no need to sell (Wang, 2001, Gervais & Heaton, 2002; Johnson & Lindblom, 2002) Excessive optimism also stimulates investors to increase the number of portfolios because they believe that the market will improve and that they will achieve high returns in the short run (Johnsson & Lindblom, 2002) The

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use of derivative in trading depends largely on the attitude of the investor When they feel optimistic, they will not need to use derivative, otherwise, if their optimism diminishes, they will use derivative as a hedge

In stock investment, herd behavior is the behavior of investors acting on the behavior of others, or when heard from a certain investor, they will immediately take action In simple terms, investors copy someone else's transactions based on the level of success of the other investment performance (Banerjee, 1992; Bikhchandani & Sharma, 2000; Hwang & Salmon, 2004) If this happens in small quantities, it will not affect the market

However, if a large number of investors act according to other reputable investors, it will affect the market Stock prices may be overvalued leading to increased investment risk Investors of this type are called unreasonable investors Dependending too much on the individual or organization will lead to that organization having a great influence on the market, thereby increasing investment risk (Barber & Odean, 2009)

Herd behavior can greatly affect the behavior of investors Investors who have high herd behavior may not use derivative to manage risk For investors with low herd behavior, they will use derivative as a tool to control risk from their behavior

Risk in the financial sector is the uncertainty of an unexpected decision or incident Tversky and Kahneman (1974) propose prospect theory and reveal that forecasting and forecasting under uncertainty do not usually follow probability rules Risk averse is a factor in prospect theory that determines that people tend to

H1: Attitude towards behavior positively affects the behavioral intention of

investors in using financial derivative instruments

H1a: Overconfidence is negatively affects the attitude among individual

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friends and family as a determining factor in their behavior People with high SN mean that their decision is influenced by the people who are important to them (Ajzen & Fishbein, 1980) SN plays a very important role in decision making, and is one of the main players in the TPB model Specifically, for financial markets, if an individual investor in the stock market witnessed a more important person than they thought they should perform that action, they will have more motivation to make a decision On the other hand, people often think that when the important people disagree with these activities, they will not intend to behave More specifically, even when a person does not want to do something, they can also be influenced to

do the behavior by the actions of others (Venkatesh & Davis, 2000) Friends, parents, relatives, brokers or reputable financial experts can influence the decision

of the investor (Kalafatis et al., 1999)

Attitudes of others also influence both intentions and decisions In particular, the decision of the investor to choose a derivative financial instrument is affected by the attitude of others People who close relationship to the investor have a lot of impact on the behavior of that investor This means when others think that negative for the tools, investor will be more likely to adjust their intentions of use On the contrary, the intention of an investor would increase if they were interested in the financial instrument (Kotler & Keller, 2006; Rivis & Sheeran; 2003)

However, there are also studies that give different results in predicting the effect of subjective norm on intention The problem is that the causal relationship between subjective norms and behavioral intentions is evidenced in various studies (Teo & Lee, 2010) On the other hand, this relationship was also denied by other

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studies (Lewis et al., 2003) Moreover, recent studies have also shown that subjectivity is a different predictor of subjective norms for investors' intentions in different regions (Wu et al., 2011)

However, despite different conclusions about the role of subjective norms with behavioral intention, it may be possible to show a significant association between subjective norms and behavioral intention It can be predicted that if an individual investor is affected by subjective norms, they may intend to use more of those who do not suffer the same pressure

H2: Subject norm positively affects the behavioral intention of investors in

using financial derivative instruments

H3: Subject norm positively affects the attitude among individual investors

2.2.3 Perceived behavioral control (PBC)

Perceived behavioral control is a complementary factor to overcome the constraint on the TPB model, which is applicable in the case of individuals affected

by external factors (SN) Thus, individuals will feel more active in their decision by reducing the pressure from SN (Ajzen, 2002) PBC is defined as perceived by individuals as easy or difficult to carry out specific behaviors (Ajzen, 2005) In other words, if the investor's PBC are stronger, they will be motivated more to perform this behavior (Ajzen, 2005) In addition, perceived behavioral control is defined as the degree to which he or she has control of internal and external factors that facilitate or limit behavioral activity

In addition, Ajzen (1991) built the PBC based on research and synthesis of various historical data Specifically, information is collected through personal

The relationship between perceived behavioral control and behavioral intention has been demonstrated in various studies In particular, Ramayah (2007) studied the "Internet taxpayer forecasting by applying the TPB model” Or Alam and Sayuti (2011) have studied behavioral control applications as a tool for predicting investment intentions A series of studies by Iakovleva, et al (2011), Wu

et al (2011) have also found on the same issues In addition, Phan and Chu (2004) also applied TPB to predict the behavior of individual investors when participating

in the Vietnam stock market These studies indicated that the ability of individuals

to intentionally act upon high PBC and vice versa

H4: Perceived behavioral control positively affects the behavioral intention

of investors in using financial derivative instruments

2.2.4 Demographic factors

Many evidences have showed that age, gender, income and education affect the preferences and attitudes towards investment decisions In particular, Jain and Mandot (2012) conclude that different demographic factors such as age, marital status, sex, city, income level, market knowledge, occupation and professional

With some of the above evidence, the author assesses gender differences in the conduct of investment behavior, particularly in the context of the stock market there are the majority of male investors in the market The aspects to investigate are the effects of psychological factors on investment attitudes as well as attitudes, subjective norms and perceptions of behavioral control for behavioral intentions of individual investors

In addition, age is also an important factor affecting behavior in financial markets Korniotis and Kumar (2011) have shown that older people have more mature investment options than young people In addition, older people tend to use financial derivative to manage risk more than young people In contrast, young people prefer adventure with their choice, so they use less defensive tools

Another factor that influences the behavior of the word is education There

is an impact of knowledge and investment orientation, which affects the perception

of risk and investment behaviors of investors Graham et al (2009) noted that investors have a larger portfolio or more education will be more likely to be perceived and competent than investors with smaller or less educated portfolios

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Therefore, educating investors on derivative financial instruments also affects their intention to use derivative for investment

H5a: There is difference between male and female in the behavioral

intention of investors in using financial derivative instruments

H5b: Age positively affects the behavioral intention of investors in using

financial derivative instruments

H5c: Education level positively affects the behavioral intention of investors

in using financial derivative instruments

3 Research methodology

3.1 Research approach

Quantitative and qualitative research methods are two main types of methods used in scientific research (Spencer, Ritchie and O'Connor, 2003) Experimental surveys of phenomena observed organically through statistics are often referred to

as quantitative research Statistics in quantitative research come in many forms, from statistics, mathematics, to computer engineering Quantitative research is applied to the research, development, and application of theories, hypotheses, and models that relate to the object of study By quantitative methods, quantitative research allows quantitative relationships to be verified Measurement data is usually expressed as percentage, mean, standard deviation

Qualitative research is widely applied in many different fields This method has been used quite widely in the past However, this is still a useful method of investigation in modern research in a variety of situations Qualitative research allows us to synthesize and deduce understanding of the subject By synthesizing

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the information that has been authenticated and documenting past research results to provide an objective way of measuring things, people The major questions in qualitative research that the researcher must answer include "what, where, when, how?" The sample size of qualitative research is usually small (Sogunro, 2001)

Quantitative methods are more formalized and used to measure the number

of problems by statistical data Furthermore, it is also used to measure attitudes, views, behaviors and other determinants, and then infer the results to a larger population sample The factors and models of exposure in the study may be constructed using the measurement data used in the quantitative study In addition, quantitative data collection methods are organized more tightly than the qualitative data collection methods In addition, Neuman (2006) offers a quantitative approach that combines a variety of methods Such methods can be online surveys, offline, direct to telephone interviews, organized monitoring activities

Synthesized from the above information, with the purpose of understanding the level as well as testing the relationship to establish the cause and effect relationship for the object studied, quantitative research should be applied in this study The research process includes 9 steps are shown below:

There are two forms of measurement scales in this questionnaire design:

• Nominal scale: present data into categories (Crossman, 2009)

• 5-point Likert scale: level of agreement or disagreement with each of a series of statement (Naresh, 2009) The range from 1 to 5 corresponds to strongly disagree and strongly agree

The questionnaire included two parts The first part was respondents’ demographics included age, gender and education displayed in categories questions The last part was main survey displayed in 5-point Likert scale questions

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Table 1 Measurement scale

OVC2 I am confident in the holding stock will rise

OVC3 I am confident in market information

OVC4 There is no need to use derivative to reduce risk

EO3 I believe that the market will stabilize after

several sessions of declines

EO4 There is no need to use derivative when the

market shows signs of deterioration

HB1 I invest by following the specialist ‘s portfolio

HB2 I invest by following friend’s portfolio

HB3 I invest in stocks according to the crowd

HB4 I sold out when I saw a large number of sellers

HB5 I bought into stock being bought a lot

RA1 I have low risk tolerance

Klos & Weber (2005) RA2 I like safe investing

RA3 I like to invest in “hot” stock

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RA4 I sell stock when prices falling

RA5 I like to use derivative for hedging

ATB2 Derivative is more beneficial than the cost that I

have to spend ATB3 I feel derivative brings a lot of benefits

ATB4 I am more confident when using derivative in

SN1 Friends, colleagues advised me to use derivative

SN2 Relatives advised me to use derivative in stock

trading

SN3 The broker recommends me to use the derivation

in stock trading

SN4 The information available is advisable to use

derivative in stock trading

PBC1 I can use derivative as soon as I need it

PBC2 I can manually use derivative

PBC3 I have no problem using derivative

PBC4 I can easily use derivative with the help of broker

BI1 I intend to use derivative in stock trading

BI2 I intend to introduce my friends to use derivative

in stock trading

BI3 I will introduce family members to use derivative

in stock trading

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3.3 Data collection

Before conducting a large sample survey, a pilot test was conducted to assess the competency of the questionnaire (Iarossi, 2006) The number of samples required in the pilot test is from 15 to 25 (Aaker, Kumar & Day, 2006) Therefore, for this research, the author conducted pilot test for 30 respondents

According to Gorsuch (1983) and Hair et al (2010), the ratio between subject and variable must be at least 5:1 It means five respondents per variable However, the most acceptable sample size calculation is 10:1 ratio (10 samples for one variable) Therefore, the minimum sample size is 165 and desired sample size is

330 On the other hand, according to Comfrey & Lee (1992), the number of

samples ranked from very poor to very good as follows:

Table 2 Sample size Criteria (Comfrey & Lee, 1992)

Sample size Level

Hence, the sample size is 300 seems to meet all requirements

Convenient method is the most common method of data collection This method appreciates the convenience as well as the accessibility of the object as easily as possible This will make it much easier to gather the required number of objects to assess significance of research problems in order to save time and costs

In addition, the number of people with derivative knowledge is limited so this is the most feasible method Questionnaire will be delivered both online and offline

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In both online & offline, surveys will be collected from individual investors from 5 securities companies operating in the derivative stock market including Saigon Securities Inc (SSI), Vietnam Prosperity Securities Company (VPBS), Vietnam Securities Corporation Vietnam Investment and Development (BSC), MB Securities (MBS) and VNDIRECT Securities (VND)

3.4 Research Method

3.4.1 Pilot test

One of the most noteworthy steps in conducting a survey is to conduct a survey and test the data obtained from a small group of subjects in the pilot test The purpose of the pilot test is to ensure that all persons in the survey sample not only ask questions in the questionnaire, but also in the same way This makes the data collected true and unmistakable, avoiding affecting statistical results, analyzing or running the model Through the pilot test, questionable issues will be identified and corrected before conducting a broad survey In addition, the inspection and processing of the survey data collected to find out how to overcome the possible data errors

The pilot test was conducted by collecting 50 respondents The respondents were individual investors who currently customer in five securities companies mentioned above The data collected from pilot study was testing by Cronbach’s alpha reliability and exploratory factor analysis (EFA) to refine the measurement scale The main survey was sent to the investors through an online survey created

by the google document platform

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3.4.2 Main survey test

SPSS (Statistical Package for Social Sciences) is a computer program for statistics It will be used to analyze collected data in this research The analysis sequence consists of descriptive statistics, reliability, exploratory factor analysis In addition, confirmatory factor analysis (CFA) and structural equation modeling (SEM) will be conducted (Anderson and Gerbing, 1988) by AMOS (adds-on SPSS)

In particular, Cronbach's alpha will be used to test reliability Validity of the data will be tested by EFA Then, CFA will perform to validate the measurement scales Finally, SEM will be used to test the research model Moreover, this research also conducts the statistics with demographic and descriptive statistics with measurement scale

“Reliability test is performed to measure the internal consistency of the construct In other words, testing for reliability is to discover and remove failures before conducting factor analysis Internal consistency is measured by Cronbach’s alpha, which is the most widely – used method According to George and Mallery (2003),” Cronbach’s alpha is acceptable when equals to 0.6 Besides, the rules have stated that if Cronbach’s alpha if Item deleted is greater than the overall Cronbach’s alpha and corrected item - total correlation is less than 0.4, the variable should be deleted from the list The table below shows the results after conducting reliability test.”

After finding the reliability of each item in one dimension, the exploratory factor analysis was conducted to identify the reliability among independent and

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dependent variables in order to access the highest validity for the scale In this step, the main aim is to find out factors among observed variables by putting variables with similar characteristics into one group.”

• Based on Pallant (2005), the KMO (Kaiser-Meyer-Olkin) must be from 0.6

and above to have a good factor analysis.””

• In the Bartlett’s Test of Sphericity, the Sig would be less than 0.05 for factor

analysis to be considered appropriate (Tabachnick & Fidell, 2007).”

• Number of factors extracted must have Eigenvalue greater than 1.0”

• The Total Variance Explained value of each item needs to be higher than

50%.”

Confirmatory Factor Analysis is a method which is used to verify whether the measured variables represent the number of constructs It provides the researcher important information about relationships between measured variables and their underlying factor structure “

The table below shows the criteria to evaluate the measurement model fit

Table 3 Criteria for Measurement Model

Chi-square/DF (CMIN/DF) < 3 good; < 5 sometimes permissible

p-value for the model > 0.05

CFI (Comparative Fit Index) > 0.95 great; > 0.9 traditional; > 0.8 sometimes

TLI (Tucker Lewis Index) ≥ 0.9

CR (Composite Reliability) > 0.7 and > AVE

AVE (Average Variance Extracted) > 0.5

Standardized Regression Weight > 0.5

Source: Joreskog (1969), Bagozzi (1981), Brown and Cudeck (1993), Hair et al (2010)

According to Anderson, Black, Babin and Hair (2010) in the “Multivariate data analysis”, the Composite Reliability (CR), Average Variance Extracted (AVE), Maximum Shared Variance (MSV) and Average Shared Variance (ASV) of scales must be measured in order to check the reliability, convergent validity and discriminant validity of the construct Those requirements are reliability (CR) > 0.7, convergent validity (AVE) >0.5 and discriminant validity with MSV < AVE, ASV

< AVE and square root of AVE greater than inter-construct correlations

If convergent validity issues happen, then the variables do not correlate well with each other within their parent factor If discriminant validity issues happen, then variables correlate more highly with variables outside their parent factor than with the variables within their parent factor.”

Table 4 Descriptive statistics

N Minimum Maximum Mean Std Deviation

The table 4 shows that the average means all items The average means are

very different when comparing overconfidence, excessive optimism items with the other items Especially, the means of overconfidence and excessive optimism items are 2.03 to 2.19 The low mean indicates that respondent disagreed with the statements In other words, respondents had low levels of overconfidence and excessive optimism In contrast, the mean of the remaining items ranged from 3.42

to 3.96 The standard deviation of all items was less than 1, which means that most respondents had the same opinion in each statement and very close to the mean

4.2 Reliability Analysis

Table 5 Remiability Test Results

Dimensions Items Corrected Item-Total

Correlation

Cronbach's Alpha if Item Deleted OVERCONFIDENCE