Introduction
Derivatives are essential financial instruments that derive their value from the price of underlying assets, functioning as contracts for future transactions These instruments serve as effective tools for managing and controlling risk, particularly in mitigating the impact of asset value fluctuations Additionally, derivatives act as hedging instruments against the volatility of commodity prices The derivatives market is divided into two primary segments: the financial derivatives market and the commodity derivatives market This study specifically focuses on the financial derivatives market within the context of the Vietnam stock market.
The Vietnam stock market has experienced significant growth over its 11-year history, with the establishment of two major exchanges: the Ho Chi Minh City Stock Exchange (HOSE) and the Hanoi Stock Exchange (HNX) Currently, there are nearly 89 active securities companies and over 700 listed companies on these exchanges By 2013, the market capitalization at HOSE exceeded $32 billion, accounting for 25% of the country's GDP, while investor participation surged to over 1.3 million trading accounts, including approximately 16,000 foreign accounts This represents a more than 3.5-fold increase in total trading accounts since the end of 2007, with foreign investor accounts nearly doubling, indicating a significant rise in investor demand Daily trading values across both exchanges have surpassed 5 trillion VND, highlighting the robust activity in Vietnam's stock market.
Despite over 13 years of development, Vietnam's stock market lacks a derivatives market to help investors hedge against price fluctuations, leading to diminished trust among small investors due to macroeconomic uncertainty and financial risks Currently, the market offers only basic investment tools like stocks, bonds, and fund certificates However, after extensive preparation, the derivatives market is set to launch soon with its first futures contract, introducing a new trading form for many investors This development is expected to enhance the vibrancy and diversity of the stock market while improving investor knowledge and skills, paving the way for future growth in the derivatives sector.
Numerous studies have explored customer behavioral intentions, including notable works by Jeong & Lambert (2001), Burton, Sheather & Roberts (2003), Liu et al (2004), Amoako & Gyampah (2007), Gu et al (2009), and Han & Kim (2010) In the financial market context, research has also focused on behavioral intentions, with significant contributions from Berry et al (1996), Athanassopoulos (2000), Auh et al (2007), Keh & Xie (2009), and Bolton et al (2010).
This study explores the behavioral intentions of investors in Vietnam's derivatives markets by identifying key factors influencing their decisions Utilizing the Theory of Planned Behavior (TPB), which has been effective in predicting various human behaviors, the research highlights the significance of this theory in understanding investor behavior By adopting a behavioral approach, the study aims to analyze individual investor behavior and their perceptions of different derivatives markets, contributing valuable insights to the field of behavioral finance.
Derivatives play a crucial role for large corporations in managing exchange rate risks, loans, and financial expenses Research indicates that these financial instruments are essential for effective risk management, utilizing options and forward contracts to mitigate market risks As indispensable components of the financial markets, derivatives have experienced rapid global growth and increasingly influence the financial and monetary systems While they offer significant benefits in risk prevention and cater to the diverse needs of market participants, their complexity can lead to economic instability if not managed properly.
In Vietnam, the use of derivative products related to currencies and commodities has a long history, with the establishment of the Buon Me Thuot coffee trading center in 2006 facilitating the trading of Vietnamese coffee through spot and forward contracts Currently, currency derivatives are widely utilized by both domestic and international commercial banks, offering various instruments such as swaps, options, and futures contracts The financial derivatives market is set for establishment and growth in the near future, starting with the introduction of two key futures contracts: those based on the VN30 and HNX30 stock indices, as well as futures on government bonds, with additional derivative contracts planned for future issuance.
In the derivatives market, there are four main contributing factors to the derivative market: infrastructure, legal framework, products and people (Hull,
In recent years, the government has established a legal framework and technical infrastructure for the derivatives market; however, upgrading human factors remains a challenge Human behavior varies significantly across different contexts and financial products, making it difficult to predict actions in the financial environment (Mullainathan & Thaler, 2000; LeBaron, 2001; Shiller, 2002) The effectiveness and growth of the derivatives market depend on the involvement of individual investors Internal factors such as education, experience, gender, culture, and psychological influences also play a crucial role in shaping investor behavior Despite individual investors becoming more professional, empirical studies indicate that market performance, such as the VN-Index, is not random, highlighting the impact of psychological factors on investment decisions (Phan & Chu, 2014) Ultimately, even well-reasoned investment analyses are often swayed by psychological influences (Murgea, 2008; Sehgal & Singh, 2012).
This study is essential for understanding investor attitudes towards derivative financial instruments in Vietnam, particularly as the derivative financial market becomes operational Additionally, it aims to identify the factors influencing investors' intentions to utilize these instruments in the country.
This study aims to investigate the factors influencing Vietnamese investors' decisions to engage in the financial derivative market, which officially launched on August 10, 1977 The Vietnamese State Securities Commission has granted trading eligibility to five key securities firms: Saigon Securities Inc (SSI), Vietnam Prosperity Securities Company (VPBS), Vietnam Securities Corporation (BSC), MB Securities (MBS), and VNDIRECT Securities (VND) Consequently, the research specifically targets investors associated with these companies.
This study aims to analyze investor behavior in Vietnam's derivative securities market, highlighting the impact of various factors on individual investors Understanding these behaviors is crucial for both brokers and the State Security Commission of Vietnam, as it can enhance the adoption of derivative instruments for better risk management in investments Ultimately, these insights can contribute to increased market liquidity and a rise in the number of investors participating in Vietnam's stock market.
Theoretical background and hypotheses
Foundational Theory
The Theory of Planned Behavior (TPB), developed by Ajzen and Fishbein in 1980, is a foundational framework in psychosocial research that extensively explores human behavior Key studies by Ajzen (1985, 1991, 2002) have empirically validated the connection between intention and behavior across various domains (Ajzen, 1988; Ajzen & Fishbein, 1980; Canary & Seibold, 1984; Sheppard, Hartwick, and Warshaw, 1988) TPB evolved from the Theory of Reasoned Action (TRA), offering deeper insights into the factors influencing decision-making processes.
The Theory of Reasoned Action (TRA) emphasizes the motivational factors influencing personal behavior, primarily through two components: attitude towards behavior (AT) and subjective norms (SN) Despite its widespread acceptance in academic literature, TRA has limitations, particularly regarding individuals' inability to act due to a lack of opportunities or resources such as time, capital, and skills To address these shortcomings, Ajzen (2002) introduced an additional variable, perceived behavioral control (PBC), which evolved the original TRA into the Theory of Planned Behavior (TPB).
Perceived behavioral control reflects the ease or difficulty of performing the behavior and whether the behavior is controlled or restricted (Ajzen, 1991) The TPB model is shown in figure 1
Figure 1 The theory of planned behavior – (Ajzen, 1991)
The Theory of Planned Behavior (TPB) posits that perceived behavioral control (PBC) influences investor actions in two significant ways: it can shape behavioral intentions and directly impact behaviors Additionally, investors' decisions are influenced by both internal factors, such as emotions, personal knowledge, experiences, and skills, and external factors, including financial resources, time, and partners (Ajzen, 2005) The three core components of TPB—behavioral attitudes, subjective norms, and perceived behavioral control—have been extensively validated through research.
The Theory of Planned Behavior (TPB) has consistently predicted behavioral intentions through attitudes, subjective norms, and perceived behavioral control, demonstrating its reliability and effectiveness across numerous empirical studies Widely applied in various fields, TPB has been instrumental in understanding human behavior in business contexts (Krueger & Carsrud, 1993), addressing unhealthy habits (Chang, 1998), and influencing tobacco control behaviors among adults (Hu & Lanese, 1998) Beyond individual behavior, TPB also aids in predicting community-beneficial actions, such as resource sharing within organizations (Bolloju, 2005) and decision-making in human resource management (Carpenter & Reimers, 2005) Additionally, TPB has been utilized to analyze intentions related to modern practices, including online shopping (Hsieh & Rai, 2008), the adoption of household technology (Pavlou & Fygenson, 2006), and credit card usage (Rutherford & DeVaney, 2009).
The Theory of Planned Behavior (TPB) is extensively utilized in financial and securities markets, as highlighted by Gopi and Ramayah (2007), who examined its application in online home-based businesses and internet banking for securities trading This suggests that TPB is an effective model for predicting behavior East's (1993) notable study further demonstrated TPB's capability to accurately forecast short-term behavior among securities investors Ajzen (2005) emphasizes that individuals are likely to take action when they perceive it positively, feel social pressure, and believe they possess the necessary means and opportunities This perspective on motivation elucidates the key factors influencing individual investment behavior.
The Vietnamese stock market has been evolving for an extended period, yet limited research has utilized the Theory of Planned Behavior (TPB) to analyze stock investment behavior Previous studies predominantly concentrated on behavioral finance theory, financial literacy, and demographic factors influencing investment decisions Additionally, the recent introduction of derivatives as an effective risk management tool in securities trading has piqued the author's interest in employing TPB as a foundational framework for developing a research model to examine the intention to use derivatives in securities investment in Vietnam.
Research model and hypotheses
The Theory of Planned Behavior (TPB) has extensive applications in understanding human behavior and has been validated through numerous global studies This article introduces a research model aimed at examining the factors that affect the intention to use derivatives in securities investments The primary objective is to identify these influencing factors and explore the relationships among them within the model Additionally, the author highlights psychological determinants that indirectly impact the intention to use derivatives, as noted by Phan & Zhou (2014) The forthcoming sections will detail the research model and its associated hypotheses.
According to the Theory of Planned Behavior (TPB), behavioral intentions refer to the willingness to perform a specific action, such as using derivatives in securities trading This concept positions behavioral intentions as a dependent variable in various experimental studies utilizing TPB Empirical research has consistently shown that motivational factors, as outlined in the TPB model, significantly influence these intentions Ultimately, a strong intention to use derivatives suggests that investors are inclined to engage in derivative trading.
Attitude refers to the influence of positive or negative emotions on specific behaviors, as outlined by Fishbein and Ajzen (1980) It is assessed through an individual's beliefs and appreciation for those behaviors, making attitudes crucial for predicting future actions Furthermore, attitudes have evolved to encompass an individual's reactions to various objects, as noted by Ajzen and Fishbein (2000).
Attitudes significantly influence behavioral intentions, with individuals exhibiting positive attitudes more likely to engage in certain behaviors Conversely, those with negative attitudes tend to avoid or criticize such behaviors Research by Gibler and Nelson (1998) underscores the strong correlation between attitude and behavioral intention, highlighting that attitudes are key determinants of personal behavior.
Ajzen and Fishbein (1980) suggest that attitude towards behavior reflects an individual's feelings of favorableness or unfavorableness This attitude is ultimately shaped by various influencing factors Phan and Zhou (2004) identify four key psychological factors that directly impact attitude towards behavior: overconfidence, excessive optimism, herd behavior, and risk aversion Consequently, attitude towards behavior is viewed as a dependent variable that is influenced by these four factors.
Overconfidence refers to an inflated sense of self-assurance regarding one's knowledge and decision-making abilities, particularly evident in the stock market (Barberis & Thaler, 2003) Many investors exhibit overconfidence, believing they possess superior insight into stock selection and timing, which often leads to disappointing transaction outcomes Despite the disparity between their confidence and actual performance, these investors remain unaware of their misjudgment, consistently assuming they are making optimal investment choices for maximum profit.
Excessive confidence significantly impacts decision-making, often leading investors to overlook valuable data, which can result in poor investment choices Research indicates that overconfidence not only skews investment decisions but also plays a crucial role in the utilization of derivatives in securities transactions.
Many investors exhibit overconfidence in their trading abilities, often neglecting essential risk control measures, especially when dealing with derivatives This overconfidence typically results in high-frequency trading, which can amplify market volume and volatility, ultimately diminishing expected returns (Gervais, Heaton, 2002) Consequently, an investor's self-assurance significantly impacts their investment behavior, driving them to engage in more frequent transactions.
Overconfident investors often overestimate their investment knowledge and abilities, leading them to overlook the realities of the market and the stocks they own This excessive optimism, which combines overconfidence with unrealistic expectations, becomes particularly problematic during market downturns Such investors tend to believe that negative market conditions are temporary and will have minimal impact on their portfolios, which can result in significant financial losses.
Many investors maintain a strong belief in the potential of their portfolios to rebound quickly, leading them to avoid selling (Wang, 2001; Gervais & Heaton, 2002; Johnson & Lindblom, 2002) This excessive optimism often drives them to expand their portfolios, anticipating market improvements and short-term high returns (Johnsson & Lindblom, 2002) The reliance on derivatives in trading is significantly influenced by the investor's outlook; when optimism is high, derivatives are less likely to be used, whereas a decline in optimism prompts investors to utilize derivatives as a protective measure.
Herd behavior in stock investment refers to the tendency of investors to mimic the actions of others, often influenced by the perceived success of those investments This phenomenon occurs when investors quickly react to the decisions of certain individuals, leading them to replicate transactions in hopes of achieving similar results While such behavior may not significantly impact the market when it occurs on a small scale, it highlights the influence of collective decision-making in investment strategies.
When numerous investors follow the actions of reputable investors, it can significantly impact the market, potentially resulting in overvalued stock prices and heightened investment risk This behavior characterizes what are known as unreasonable investors Over-reliance on specific individuals or organizations can lead to their substantial influence on the market, further exacerbating investment risks (Barber & Odean, 2009).
Herd behavior significantly influences investor actions, with those exhibiting strong herd tendencies often neglecting to utilize derivatives for risk management In contrast, investors with lower herd behavior are more inclined to employ derivatives as a strategic tool to mitigate risks associated with their investment decisions.
Risk in the financial sector refers to the uncertainty surrounding unexpected decisions or incidents According to Tversky and Kahneman's prospect theory (1974), forecasting under uncertainty often deviates from traditional probability rules A key aspect of this theory is risk aversion, which indicates that individuals typically exhibit risk-averse behavior when in the "profitable zone" but may become risk-seeking when faced with losses in the "losing zone" (Tversky & Kahneman, 1992).
Risk-averse investors tend to seek out higher market fluctuations and embrace greater risks, while those with low risk aversion prefer to engage in trading only when they feel secure and confident in their investments.
Research methodology
Research approach
Quantitative and qualitative research methods are fundamental approaches in scientific research (Spencer, Ritchie, and O'Connor, 2003) Quantitative research focuses on experimental surveys of phenomena, utilizing statistics to analyze data This method encompasses various forms of statistics, mathematics, and computer engineering, which are essential for the research, development, and application of theories and models related to the study subject By employing quantitative methods, researchers can verify quantitative relationships, with measurement data typically represented as percentages, means, and standard deviations.
Qualitative research remains a valuable investigative method across various fields, despite its historical usage It enables researchers to synthesize and derive insights from authenticated information and previous studies, offering an objective approach to understanding subjects and populations Key questions addressed in qualitative research include "what, where, when, and how?" Typically, the sample size for qualitative research is small (Sogunro, 2001).
Quantitative methods are formalized techniques used to statistically measure problems, attitudes, views, and behaviors, allowing researchers to generalize findings to larger populations These methods enable the construction of exposure factors and models based on collected measurement data Moreover, quantitative data collection is typically more structured compared to qualitative approaches Neuman (2006) presents a quantitative framework that incorporates various methods, including online surveys, offline surveys, telephone interviews, and organized monitoring activities.
This study employs quantitative research to analyze and test the cause-and-effect relationships of the subject under investigation The research process comprises nine distinct steps, which are outlined below.
Figure 3 Main steps of research process
Questionnaire design
The questionnaire is distributed through online surveys and hard copies to achieve a sufficient sample size, given the limited knowledge of derivatives among the target audience It has been translated into Vietnamese, ensuring that all questions are concise, clear, and easy to understand, minimizing any potential confusion.
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 consisted of two sections: the first section gathered demographic information from respondents, including age, gender, and education, presented in categorical format The second section comprised the main survey, which utilized a 5-point Likert scale for responses.
Variable Code Measurement Statements Adapted from
OVC1 I am confident in my ability to trade securities
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
EO1 I do not sell stocks when the market is plummeting EO2 I trust the stock will rise
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
RA3 I like to invest in “hot” stock
RA4 I sell stock when prices falling
RA5 I like to use derivative for hedging
ATB1 Derivative helps me better control risk when trading stocks
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 stock trading
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
BEHAVIORAL INTENTION TO USE (BI)
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
Data collection
Prior to implementing a large sample survey, a pilot test was performed to evaluate the effectiveness of the questionnaire (Iarossi, 2006) Research indicates that a sample size of 15 to 25 is ideal for pilot testing (Aaker, Kumar & Day, 2006) Consequently, the author decided to conduct the pilot test with 30 respondents for this study.
According to Gorsuch (1983) and Hair et al (2010), a minimum subject-to-variable ratio of 5:1 is essential, indicating that there should be five respondents for each variable However, the optimal sample size is often calculated using a 10:1 ratio, which suggests ten samples for every variable Consequently, the minimum recommended sample size is 165, while the desired sample size is higher.
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)
0.05
CFI (Comparative Fit Index) > 0.95 great; > 0.9 traditional; > 0.8 sometimes permissible GFI (Goodness-of-Fit Index) > 0.95 great; > 0.9 traditional; > 0.8 sometimes permissible
RMSEA (Root Mean Squared Error of Approximation)
< 0.06: good fit 0.06 – 0.08: acceptable fit 0.08 – 0.1: mediocre fit
CR (Composite Reliability) > 0.7 and > AVE
Source: Joreskog (1969), Bagozzi (1981), Brown and Cudeck (1993), Hair et al (2010)
According to Anderson, Black, Babin, and Hair (2010) in "Multivariate Data Analysis," it is essential to measure Composite Reliability (CR), Average Variance Extracted (AVE), Maximum Shared Variance (MSV), and Average Shared Variance (ASV) to assess the reliability, convergent validity, and discriminant validity of a construct Specifically, the criteria for these measurements include a reliability (CR) greater than 0.7, convergent validity (AVE) exceeding 0.5, and ensuring that the discriminant validity conditions of MSV are less than AVE and ASV are met.
< AVE and square root of AVE greater than inter-construct correlations
Convergent validity issues arise when variables fail to correlate effectively within their parent factor, indicating a lack of strong relationships among them Conversely, discriminant validity issues occur when variables show a higher correlation with those outside their parent factor than with their own, suggesting a misalignment in the expected relationships.
Data analysis and results
Descriptive statistics
Descriptive statistics serve as the initial step in understanding data, providing essential insights through calculations of minimum, maximum, mean, and standard deviation The mean indicates the central tendency of the data distribution, while the standard deviation measures the dispersion within that distribution A low standard deviation signifies that the majority of data points are closely clustered around the mean, highlighting the data's consistency.
N Minimum Maximum Mean Std Deviation
Table 4 reveals significant differences in average scores between overconfidence and excessive optimism items and other categories Specifically, the means for overconfidence and excessive optimism range from 2.03 to 2.19, indicating that respondents generally disagreed with these statements and exhibited low levels of both traits In contrast, the mean scores for the remaining items are higher, ranging from 3.42 to 3.96 Additionally, the standard deviation for all items is below 1, suggesting that most respondents shared similar opinions closely aligned with the mean.
Reliability Analysis
Dimensions Items Corrected Item-Total
Cronbach's Alpha if Item Deleted
Table 5 indicates that items RA3 and SN2 were removed due to their "Corrected Item-Total Correlation" values falling below 0.4 Following this elimination, the reliability testing revealed that the Cronbach’s Alpha for the remaining items exceeded 0.8, with all item-to-total correlations surpassing the standard threshold of 0.4, indicating high internal consistency reliability across the scales Specifically, the initial Cronbach’s Alpha values for overconfidence, excessive optimism, herd behavior, risk aversion, attitude towards behavior, subjective norm, perceived behavioral control, and behavioral intention to use were recorded at 0.867, 0.890, 0.865, 0.810, 0.858, 0.868, 0.904, and 0.844, respectively.
Exploratory Factor Analysis (EFA)
Table 6 KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
The analysis presented in Table 6 indicates a KMO value of 0.907, which exceeds the acceptable threshold of 0.5, and a significant Bartlett test result (p < 0.05) Therefore, it is concluded that exploratory factor analysis (EFA) is appropriate for this data.
Factor Initial Eigenvalues Extraction Sums of Squared Loadings
Total % of Variance Cumulative % Total % of Variance Cumulative %
Table 7 indicates that all eight components have eigenvalues exceeding 1, with a total variance explained of 63.433% This suggests that these eight primary factors account for 63.433% of the total data variance in the variable.
Extraction Method: Principal Axis Factoring
Rotation Method: Promax with Kaiser Normalization a Rotation converged in 6 iterations
Table 8 clearly demonstrates that all items are allocated to their respective groups without overlap, as no items appear in multiple groups Additionally, each item exhibits a factor loading value exceeding 0.5, indicating strong correlations Together, these findings confirm the validity of the measurement scale.
Confirmatory Factor Analysis (CFA)
A crucial aspect of Confirmatory Factor Analysis (CFA) is evaluating the suitability of the proposed model and its associated data According to Pire (2007), model fit refers to the degree to which the model accurately represents the data it is intended to explain.
The model demonstrates a strong fit when the sample data aligns closely with the implied model, as indicated by key fit indices Among the various criteria for evaluating model fit, the most significant ones are highlighted in the table The analysis reveals a suitable data fit for the model, with a Chi-square/df ratio of 1.189, GFI of 0.912, TLI of 0.984, CFI of 0.986, RMR of 0.024, and RMSEA of 0.024, confirming that the model fit is indeed good.
In section 4.2, the reliability statistics reveal that all variables exhibit Cronbach's alpha values exceeding 0.6, indicating satisfactory reliability This section will specifically emphasize the composite reliability for each variable.
The same methodology was utilized for other variables, with results presented in the table below Notably, all composite reliability values exceed the threshold of 0.7, indicating strong reliability across the measures.
Table 9 Value of Composite Reliability
Components Value of composite reliability
4.4.2 Convergent Validity of all variables
The Average Variance Extracted (AVE) measures the average variance in indicated variables explained by a construct As shown in Table 23, the AVE values for each factor have been calculated.
Table 10 Value of Average Variance Extracted
The average variance extracted for all constructs, including overconfidence, excessive optimism, herd behavior, risk aversion, attitude towards behavior, subjective norm, perceived behavioral control, and behavioral intention to use, exceeds 0.5, confirming the convergent validity of the measurement.
4.4.3 Discriminant Validity of all variables
Table 12 Square root of AVE results
SN PBC HB EO OVC ATB RA BI
Table 11 indicates that the MSV and ASV values for each scale are lower than the AVE values, while Table 14 shows that all AVE values exceed the r² values For instance, the AVE for Subjective Norm and Perceived Behavioral Control is 0.690 and 0.704, respectively, whereas their r² is 0.830 This demonstrates that the square root of the AVE is greater than the inter-construct correlations, thereby confirming the discriminant validity among the constructs.
The composite reliability values exceeded 0.7, and the average variance extracted was above 0.5, confirming the convergent validity of the measurements related to overconfidence, excessive optimism, herd behavior, risk aversion, attitude towards behavior, subjective norm, perceived behavioral control, and behavioral intention to use.
The MSV and ASV values for each scale are lower than the AVE values, indicating strong discriminant validity among constructs Additionally, all AVE values exceed the r² values presented in Table 12 For instance, the AVE for subjective norm and perceived behavioral control are 0.690 and 0.704, respectively, while their r² is 0.830 This demonstrates that the square root of the AVE is greater than the inter-construct correlations, further confirming the constructs' discriminant validity.
Figure 4 First Measurement Standardized Modelling
Structural Equation Modeling (SEM)
Structural Equation Modeling (SEM) is a comprehensive multivariate analysis technique that encompasses various specialized methods, including path analysis, confirmatory factor analysis, second-order factor analysis, regression models, covariance structure models, and correlation structure models The results of SEM provide valuable insights into complex relationships among variables.
The results of the SEM analysis indicate a strong model fit, with Chi-square/df at 1.296, GFI at 0.903, TLI at 0.974, CFI at 0.977, and RMSEA at 0.031 All hypotheses were supported, as evidenced by the highest p-value of 0.034, which is below the 0.05 threshold The standardized regression weights demonstrate the significant influence of overconfidence, excessive optimism, herd behavior, risk aversion, and subjective norm on attitudes towards behavior, as well as the effects of attitude, subjective norm, and perceived behavioral control on the intention to use derivatives.
Table 13 Regression Weights of Model
ATTITUDE_TOWARD_BEHAVIOR < - SUBJECTIVE_NORM ***
ATTITUDE_TOWARD_BEHAVIOR < - EXCESSIVE_OPTIMISM 002
ATTITUDE_TOWARD_BEHAVIOR < - RISK_AVERSION 029
ATTITUDE_TOWARD_BEHAVIOR < - HERD_BEHAVIOR ***
BEHAVIORAL_INTENTION_TO_USE < - ATTITUDE_TOWARD_BEHAVIOR *** BEHAVIORAL_INTENTION_TO_USE < - PERCEIVED_BEHAVIORAL_CONTROL ***
BEHAVIORAL_INTENTION_TO_USE < - SUBJECTIVE_NORM 003
Indirect Effects of Behavior intention to use
Path analysis, an extension of Structural Equation Modeling (SEM), focuses on identifying the indirect effects of variables on behavioral intentions to use derivatives A significant value of less than 0.05 indicates that a variable contributes uniquely and significantly to predicting the dependent variable, while a value greater than 0.05 suggests no significant contribution This methodology is crucial for understanding complex relationships among variables in behavioral research (Pallant, 2005).
The data indicates that factors such as overconfidence, excessive optimism, herd behavior, risk aversion, and subjective norms significantly influence attitudes toward behavior Furthermore, these attitudes play a crucial role in shaping the intention to engage in specific behaviors.
Table 14 Indirect effects on Behavior intention to use
Variable Calculation Indirect effect on
Total effect on Behavior intention to use
Independent Sample T-test and Oneway Anova
This study employs Anova and independent sample t-tests to analyze the relationship between demographic qualitative variables—such as gender, age, and education—and the mean behavior intention to use derivatives.
Levene's Test for Equality of Variances t-test for Equality of Means
95% Confidence Interval of the Difference Lower Upper
The results of Levene's test indicated a p-value greater than 0.05, leading to the acceptance of the hypothesis of equal variances Additionally, the t-test also yielded a p-value exceeding 0.05, suggesting that there is no significant difference in behavioral intentions to use derivatives between males and females.
In the Levene test, a significance level (sig.) of less than or equal to 0.05 indicates that the variance among the options is different, whereas a sig greater than 0.05 suggests that the variance is not different Subsequently, when examining the ANOVA table, a sig greater than 0.05 implies no significant difference between the variables, while a sig of less than or equal to 0.05 indicates a significant difference among the variables.
Table 16 Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig
The results from the "test of homogeneity of variances" in Table 16 indicate that there is no significant difference in variance among the age variable options Consequently, the next step is to examine the ANOVA table.
Sum of Squares df Mean
The ANOVA table indicates a p-value of less than 0.05, suggesting a significant difference in behavioral intentions to use derivatives across different age groups The descriptive statistics will further illustrate the variations in behavioral intentions associated with each age category.
Maxi mum Lower Bound Upper Bound
Respondents with a Primary or Secondary education level typically utilize fewer derivatives in the stock market, averaging around 2.5 In contrast, those with a high school education demonstrate a greater engagement with derivatives, averaging 3.7.
Respondents with a university or college education demonstrated the highest intention to use derivatives, averaging a score of 4.3 This indicates that individuals with higher education levels are more inclined to engage in derivative trading in the stock market.
Table 19 Test of Homogeneity of Variances
Levene Statistic df1 df2 Sig
The result from the table 19 shows that there is no difference in variance between options in age variable Therefore, the anova table will be checked
Sum of Squares df Mean Square F Sig
The analysis presented in Table 9 indicates a p-value of less than 0.05, suggesting a significant difference in behavioral intentions to use derivatives based on age The descriptive data further illustrates the varying levels of behavioral intention across different age groups.
Maxi mum Lower Bound Upper Bound
A study of stock market participants reveals that younger respondents aged 18 to 25 utilize derivatives less frequently, with an average usage score of 2.98 In contrast, those aged 26 to 35 demonstrate a higher average usage of 3.7, while individuals over 35 exhibit the highest derivative usage, averaging 4.6 This data suggests a clear trend: as age increases, the likelihood of using derivatives in stock trading also rises.
Hypothesis testing results
The SEM results and statistical tests, including T-test and ANOVA, indicated strong support for eight out of nine hypotheses, specifically H1, H1a, H1b, H1c, H1d, H2, H3, H4, H5b, and H5c However, the hypothesis H5a, which proposed a difference in behavioral intentions between male and female investors regarding the use of financial derivative instruments, was not supported by the data.
A detailed analysis of the structural paths reveals that, in line with hypothesis H1, investors' attitudes towards behavior have a significant positive impact on their intention to use financial derivative instruments (γ = 0.409, p < 0.05) Additionally, hypothesis H1a, which proposed a negative relationship between overconfidence and individual investors' attitudes, was supported by the data (γ = -0.104, p < 0.05) Similarly, hypothesis H1b, suggesting a negative relationship between excessive optimism and attitudes among individual investors, also received empirical support (γ = -0.121, p < 0.05).
The study found that herd behavior positively influences the attitudes of individual investors, with a significant effect size (γ = 0.272, p < 0.05) Additionally, risk aversion was shown to have a positive impact on investor attitudes (γ = 0.153, p < 0.05) Furthermore, the research indicated a significant relationship between subjective norms and the behavioral intentions of investors regarding the use of financial derivative instruments (γ = 0.170, p < 0.05), as well as a notable connection between subjective norms and attitudes toward behavior (γ = 0.197, p < 0.05).
The study found a significant relationship between perceived behavioral control and investors' intentions to use financial derivative instruments, with a correlation coefficient of γ 0.347 (p < 0.05) While the hypothesis regarding gender (H5a) was not supported, the hypotheses concerning age (H5b) and educational level (H5c) received support.
Discussion & conclusion
Discussion
This study investigates the intentions of investors in Vietnam regarding the use of derivative securities, marking one of the first analyses of its kind in the country It examines how factors such as overconfidence, excessive optimism, herd behavior, and risk aversion shape investor attitudes Findings from a structural equation modeling (SEM) analysis of data from 317 individual investors reveal that attitudes toward behavior and perceived behavioral control are the primary influences on the intention to engage with derivatives in stock trading Additionally, subjective norms also play a significant role in shaping these intentions A deeper dive into attitudes indicates that while overconfidence and excessive optimism negatively impact investor intentions, herd behavior and risk aversion contribute positively.
Research indicates that investor attitudes significantly influence the use of derivatives in securities trading Positive attitudes lead investors to learn about and utilize derivatives as effective hedging tools, while negative attitudes deter them from using these instruments This aligns with previous studies, including Adin (1999), which highlight the critical role of attitude in behavioral intention within the financial sector Gobi and Ramayah (2007) emphasized that attitude has the highest impact factor in their research model, supporting the notion that investor attitudes are vital for behavioral intentions Furthermore, studies by Rajdeep and Niladri (2015) and others have shown that these attitudes are particularly influential in the context of the Vietnam stock market This study aims to clarify the factors affecting investor behavior, particularly regarding new derivative financial instruments in Vietnam Notably, herd behavior plays a significant role in enhancing the understanding and utilization of derivatives among investors, as evidenced by similar findings in developing markets like Vietnam and broader Asian stock markets Individual investors often seek advice from others before making decisions, suggesting that the effectiveness of derivatives may be validated through collective testing, thereby encouraging broader adoption.
This study highlights the influence of risk-taker factors in the market, aligning with Odean (1998) and Shiller (2000), who noted that overconfidence leads individual investors to trade more frequently Derivatives serve as effective tools for low-risk tolerance investors, particularly in volatile markets, as they can mitigate risks associated with price fluctuations This dynamic can encourage more investors to enter the market Conversely, riskier investors tend to trade more without utilizing derivatives Previous research on risky investors has faced challenges in predictability due to the complex psychology involved The findings of this study reinforce earlier work emphasizing the significance of perceived behavioral control (Shiller, 1989; Litua, 2009; Mastrangelo, 2011) To enhance the appeal of derivatives and boost individual participation in the stock market, a combination of perceived behavioral control and herd behavior is recommended.
Implications for managers
Evaluating investor behavior is crucial in the marketplace, as confirmed by studies from Barber & Odean (2000), Caparrelli & Arcangelis (2004), and Zumbo (2010) These studies highlight the importance of factors such as herd behavior and perceived behavioral control Consequently, brokers in Vietnam should focus on building strong relationships with investors by providing comprehensive knowledge about stocks and derivatives through both online and offline courses Financial education, particularly regarding derivatives, can be shared through various mediums, including documents and Q&A sessions on forums and social networks, enabling investors to actively acquire knowledge This research will assist broker managers in understanding the behavioral elements that influence investors Furthermore, brand managers should prioritize establishing quality relationships and effective communication channels to build trust with investors, thereby encouraging greater participation in securities investment and the use of derivative financial instruments.
Conclusion
Research on the securities market, particularly derivatives in developing countries, has evolved significantly, highlighting the practical importance of these studies While many investigations emphasize behavioral finance as a predictor of investor sentiment, they often fall short in certain scenarios Additionally, the influence of intrinsic factors on investor behavior remains poorly understood, complicating brokers' efforts to devise effective strategies to attract individual investors To address this gap, it is essential to focus on perceived behavioral control among investors, integrating aspects such as herd behavior to enhance investor engagement This study aims to bridge the divide between investors and brokers, enabling a deeper understanding of individual investor behavior for more effective market strategies.
This research significantly enhances the relationship between individual investors and brokers, benefiting both parties Individual investors gain valuable opportunities to expand their financial and investment knowledge, particularly regarding derivative financial instruments, which can lead to greater success in high-risk market conditions Meanwhile, brokers can attract more clients by fostering connections with investors, allowing them to share knowledge and offer effective investment support tools.
Limitations and directions for future research
This study faced several limitations, primarily due to the widespread familiarity with the Theory of Planned Behavior (TPB) model in Vietnam, which made certain factors easily predictable Consequently, the research concentrated on new components influencing investor behavior, such as herd behavior, risk aversion, overconfidence, and excessive optimism A key strength of the study was its effective integration of internal and external factors to enhance the prediction of investor behavior However, it did not explore how to influence the behavior of risky investors Additionally, the recent introduction of derivatives in Vietnam, limited to just five securities companies, restricted the study's scope Future research is necessary to refine the predictive model for investor behavior as derivatives become more widely adopted in the country.
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I am Trang Nguyen Thanh Phuong, a Master's student in Business at the International School of Business, University of Economics Ho Chi Minh City My research focuses on derivative instruments, which are essential tools for investors to manage risks in securities and financial markets This thesis aims to identify the factors influencing investors' intentions to use derivatives in Vietnam's stock market I invite you to participate in this survey, which will provide insights into investor behavior and current perspectives on risk management The findings will assist in developing effective investment strategies.
1 Have you heard of derivative securities? a Yes b No
Derivative securities are financial instruments whose value is derived from an underlying asset For more information on this topic, you can explore the resources provided in the links below After gaining a better understanding, you may proceed to conduct further research.
2 Assets base of derivative securities may be: a commodities such as agricultural products, metals b financial instruments such as stocks, bonds, interest rates c Both
3 Futures contracts are: a the contract in which the holder of the contract, has the right, but not the obligation, to buy or sell a property type basis at a certain time in the future according to predefined prices b standardized futures contracts, listed and traded on the stock exchange c an agreement between the two sides about the swap the cash flows arising from the financial instruments in the future
Please indicate your level of agreement on the following statements by choosing the most appropriate box:
OVC1 I am confident in my ability to trade securities
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
EO1 I do not sell stocks when the market is plummeting EO2 I trust the stock will rise
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
RA3 I like to invest in “hot” stock
RA4 I sell stock when prices falling
RA5 I like to use derivative for hedging
ATB1 Derivative helps me better control risk when trading stocks
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 stock trading
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
BEHAVIORAL INTENTION TO USE (BI)
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
6 Education a Primary/secondary b High school c College/university or above
Kính chào anh/chị, Tôi là Trang Nguyễn Thanh Phương, hiện đang theo học chương trình Cao học của
Viện Đào Tạo Quốc Tế (ISB) thuộc Trường Đại Học Kinh Tế Thành Phố Hồ Chí Minh đang tiến hành nghiên cứu về công cụ chứng khoán phái sinh (Derivative), nhằm giúp các nhà đầu tư kiểm soát rủi ro trong đầu tư chứng khoán và các sản phẩm tài chính khác Nghiên cứu này sẽ xác định các yếu tố ảnh hưởng đến ý định sử dụng Derivative của nhà đầu tư trên thị trường chứng khoán Việt Nam Chúng tôi rất mong nhận được sự tham gia của anh/chị trong khảo sát này, để từ đó có cái nhìn tổng quan về hành vi đầu tư và quan điểm của nhà đầu tư về việc kiểm soát rủi ro, giúp xây dựng chiến lược đầu tư hợp lý.
1 Anh/chị đã từng nghe nói đến chứng khoán phái sinh (Derivative) a Có b Không
Nếu bạn chưa biết về chứng khoán phái sinh, hãy tham khảo thông tin chi tiết tại trang web dưới đây trước khi tiếp tục khảo sát: https://www.vndirect.com.vn/kien-thuc-co-ban-ve-chung-khoan-phai-sinh/.
2 Tài sản cơ sở của chứng khoán phái sinh có thể là: a hàng hóa như nông sản, kim loại b công cụ tài chính như cổ phiếu, trái phiếu, lãi suất c cả hai
3 Hợp đồng tương lai là: a hợp đồng mà trong đó, người nắm giữ hợp đồng có quyền, nhưng không có nghĩa vụ, mua hoặc bán một loại tài sản cơ sở tại một thời điểm nhất định trong tương lai theo mức giá được xác định trước b hợp đồng kỳ hạn được chuẩn hóa, niêm yết và giao dịch trên Sở giao dịch chứng khoán c một thỏa thuận giữa hai bên về việc hoán đổi các dòng tiền phát sinh từ các công cụ tài chính trong tương lai
Anh/chị vui lòng cho biết mức độ đồng ý về các phát biểu dưới đây bằng việc đánh dấu vào ô tương ứng:
Tôi tự tin vào khả năng của mình trong việc đầu tư cổ phiếu, đặc biệt là những cổ phiếu mà tôi đang nắm giữ, vì tôi tin rằng chúng sẽ tăng giá Hơn nữa, tôi cũng tự tin với những thông tin mà tôi đã thu thập từ thị trường.
OVC4 Tôi cho rằng không cần phải sử dụng derivative khi chơi cổ phiếu
EO1 Tôi không bám ra cổ phiếu khi thị trường đang biến động mạnh
EO2 Tôi tin cổ phiếu mình đang nắm giữ sẽ tăng giá
EO3 Tôi tin rằng thị trường sẽ ổn định nhanh trở lại
EO4 Tôi cho rằng không cần sử dụng derivative dù thị trường đang suy giảm
HB1 Tôi đầu tư theo danh mục của chuyên gia HB2 Tôi đầu tư theo danh mục của bạn bè HB3 Tôi đầu tư theo đám đông
HB4 Tôi lập tức bán ra cổ phiếu đang bị bán tháo
HB5 Tôi mua vào cổ phiếu nóng (cổ phiếu được mua nhiều)
AV ERS ION RA1 Tôi chịu được rủi ro thấp
RA2 Tôi thích đầu tư an toàn
RA3 Tôi thích đầu tư vào cổ phiếu nóng
RA4 Tôi bán ra cổ phiếu đang giảm giá
RA5 Tôi muốn sử dụng derivative để kiểm soát rủi ro
ATB1 Derivative giúp tôi kiểm soát rủi ro tốt hơn khi đầu tư cổ phiếu
ATB2 Derivative mang lại nhiều lợi ích đáng giá so với chi phí bỏ ra
ATB3 Tôi cảm thấy derivative mang lại rất nhiều lợi ích
ATB4 Tôi cảm thấy tự tin hơn khi dùng derivative khi đầu tư cổ phiếu
SN1 Bạn bè, đồng nghiệp khuyên tôi sử dụng derivative SN2 Người thân khuyên tôi sử dụng derivative
SN3 The broker khuyên tôi sử dụng derivation
SN4 Những thông tin trên mạng đều khuyến khích sử dụng derivative khi đầu tư cổ phiếu
PBC1 Tôi có thể sử dụng derivative ngay khi tôi cần
PBC2 Tôi có thể tự sử dụng derivative
PBC3 Tôi không gặp vấn đề gì khi sử dụng derivative
PBC4 Tôi có thể sử dụng derivative dễ dàng với sự hướng dẫn của broker
L INT ENT BI1 Tôi có ý định sẽ sử dụng derivative
BI2 Tôi có ý định sẽ giới thiệu bạn bè dùng derivative
BI3 Tôi có ý định sẽ giới thiệu người thân dùng derivative
6 Trình độ học vấn a Tiểu học/trung cấp b Phổ thông c Cao đẳng/đại học hoặc cao hơn