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

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 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 Table of Contents Acknowledgement Abstract List of figures List of tables List of abbreviations Introduction 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 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 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 4.5.3 Age 53 4.6 Hypothesis testing results 54 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 List of figures Figure The theory of planned behavior – (Ajzen, 1991) 14 Figure Research model 17 Figure Main steps of research process 28 Figure First Measurement Standardized Modelling 47 Figure Structural Equation Model 48 List of tables Table Measurement scale 30 Table Sample size Criteria (Comfrey & Lee, 1992) 32 Table Criteria for Measurement Model 35 Table Descriptive statistics 37 Table Remiability Test Results 38 Table KMO and Bartlett's Test 40 Table Total Variance Explained 41 Table Pattern Matrix 42 Table 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 List of abbreviations No Abbreviation Meaning AVE Average Variance Extracted ATB Attitude Towards Behavior CFA Confirmatory factor analysis CR Composite Reliability EFA Exploratory factor analysis HOSE Ho Chi Minh City Stock Exchange HNX Hanoi Stock Exchange 12 TRA Theory of Reason Action 13 TBC Perceived Behavioral Control 14 TPB Theory of Planned Behavior SN Subjective Norm SEM Structural Equation Modeling 15 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 HB5 711 EO1 -.302 394 308 858 -.382 -.384 EO2 -.320 824 -.369 -.389 EO4 -.311 805 -.348 -.336 EO3 -.314 791 -.303 -.392 OVC4 -.375 844 -.413 -.392 -.464 -.332 OVC2 -.376 826 -.379 -.477 -.367 -.370 800 -.357 -.369 -.359 -.303 693 -.335 -.348 325 -.323 793 401 408 327 RA2 -.341 744 410 478 308 RA5 -.378 681 410 397 RA1 -.326 665 338 360 OVC1 OVC3 RA4 ATB4 494 -.345 -.410 449 812 507 423 ATB2 449 -.313 -.383 423 802 447 425 ATB1 429 -.366 -.328 445 753 461 446 ATB3 442 -.303 -.443 369 740 439 363 333 -.358 441 502 886 493 345 -.429 505 505 822 434 -.393 494 490 788 404 SN1 326 SN4 SN3 316 313 BI1 482 356 -.395 -.322 360 438 422 846 BI3 421 333 -.383 -.360 336 397 430 802 BI2 431 341 -.319 302 468 445 763 Extraction Method: Principal Axis Factoring Rotation Method: Promax with Kaiser Normalization Factor Correlation Matrix Factor 1.000 172 -.249 -.257 296 240 324 525 172 1.000 -.362 -.415 387 568 383 416 -.249 -.362 1.000 233 -.301 -.414 -.286 -.447 -.257 -.415 233 1.000 -.456 -.489 -.458 -.382 296 387 -.301 -.456 1.000 524 557 398 240 568 -.414 -.489 524 1.000 575 521 324 383 -.286 -.458 557 575 1.000 517 86 8 525 416 -.447 -.382 398 521 517 1.000 Extraction Method: Principal Axis Factoring Rotation Method: Promax with Kaiser Normalization E Confirmatory Factor Analysis Regression Weights: (Group number - Default model) Estimat e S.E C.R P Label PBC2 < PERCEIVED_BEHAVIORAL_CONTR OL 1.000 PBC4 < PERCEIVED_BEHAVIORAL_CONTR OL 1.042 05 18.26 ** * par_1 PBC1 < PERCEIVED_BEHAVIORAL_CONTR OL 1.043 05 18.23 ** * par_2 PBC3 < PERCEIVED_BEHAVIORAL_CONTR OL 898 05 16.67 ** * par_3 HB4 < HERD_BEHAVIOR 1.000 HB1 < HERD_BEHAVIOR 1.057 08 12.90 ** * par_4 HB2 < HERD_BEHAVIOR 1.103 08 13.36 ** * par_5 HB5 < HERD_BEHAVIOR 1.011 08 12.25 ** * par_6 HB3 < HERD_BEHAVIOR 1.063 08 12.93 ** * par_7 EO1 < EXCESSIVE_OPTIMISM 1.000 EO4 < EXCESSIVE_OPTIMISM 896 05 17.04 ** * par_8 EO2 < EXCESSIVE_OPTIMISM 972 05 17.00 ** * par_9 EO3 < EXCESSIVE_OPTIMISM 847 05 16.09 ** * par_1 OVC L R 250 PERCEIVED_BEHAVIORAL_CONTRO < > RISK_AVERSION L 312 PERCEIVED_BEHAVIORAL_CONTRO < > SUBJECTIVE_NORM L 344 BEHAVIORAL_INTENTION_TO_ PERCEIVED_BEHAVIORAL_CONTRO < > L USE 566 HERD_BEHAVIOR < > EXCESSIVE_OPTIMISM -.381 HERD_BEHAVIOR < > OVERCONFIDENCE -.438 HERD_BEHAVIOR < > HERD_BEHAVIOR < > RISK_AVERSION 404 HERD_BEHAVIOR < > SUBJECTIVE_NORM 402 ATTITUDE_TOWARD_BEHAVIO R 89 599 Estimate BEHAVIORAL_INTENTION_TO_ USE HERD_BEHAVIOR < > 432 EXCESSIVE_OPTIMISM < > OVERCONFIDENCE EXCESSIVE_OPTIMISM < > EXCESSIVE_OPTIMISM < > RISK_AVERSION -.323 EXCESSIVE_OPTIMISM < > SUBJECTIVE_NORM -.318 EXCESSIVE_OPTIMISM < > BEHAVIORAL_INTENTION_TO_ USE -.469 OVERCONFIDENCE < > ATTITUDE_TOWARD_BEHAVIO R -.518 OVERCONFIDENCE < > RISK_AVERSION -.484 OVERCONFIDENCE < > SUBJECTIVE_NORM -.489 OVERCONFIDENCE < > ATTITUDE_TOWARD_BEHAVIOR < > RISK_AVERSION 561 ATTITUDE_TOWARD_BEHAVIOR < > SUBJECTIVE_NORM 622 ATTITUDE_TOWARD_BEHAVIOR < > RISK_AVERSION < > SUBJECTIVE_NORM RISK_AVERSION < > BEHAVIORAL_INTENTION_TO_ USE 430 SUBJECTIVE_NORM < > BEHAVIORAL_INTENTION_TO_ USE 554 247 ATTITUDE_TOWARD_BEHAVIO R -.443 BEHAVIORAL_INTENTION_TO_ USE -.416 BEHAVIORAL_INTENTION_TO_ USE 548 586 Variances: (Group number - Default model) Estimate S.E C.R P PERCEIVED_BEHAVIORAL_CONTROL 637 072 8.895 *** par_52 HERD_BEHAVIOR 419 057 7.393 *** par_53 EXCESSIVE_OPTIMISM 605 066 9.093 *** par_54 OVERCONFIDENCE 513 064 8.078 *** par_55 ATTITUDE_TOWARD_BEHAVIOR 331 041 8.098 *** par_56 RISK_AVERSION 319 044 7.318 *** par_57 SUBJECTIVE_NORM 724 079 9.118 *** par_58 BEHAVIORAL_INTENTION_TO_USE 471 054 8.723 *** par_59 e1 275 029 9.556 *** par_60 e2 248 028 8.945 *** par_61 e3 250 028 8.965 *** par_62 e4 284 028 10.208 *** par_63 e5 330 032 10.219 *** par_64 e6 355 035 10.176 *** par_65 90 Label Estimate S.E C.R P e7 324 033 9.690 *** par_66 e8 433 040 10.719 *** par_67 e9 343 034 10.048 *** par_68 e10 224 027 8.438 *** par_69 e11 255 026 9.691 *** par_70 e12 277 030 9.372 *** par_71 e13 263 026 10.040 *** par_72 e14 304 031 9.692 *** par_73 e15 261 030 8.724 *** par_74 e16 202 024 8.296 *** par_75 e17 380 034 11.040 *** par_76 e18 193 020 9.610 *** par_77 e19 178 020 8.915 *** par_78 e20 260 025 10.522 *** par_79 e21 235 023 10.289 *** par_80 e22 234 026 9.106 *** par_81 e23 273 030 9.142 *** par_82 e24 308 029 10.591 *** par_83 e25 334 033 10.151 *** par_84 e26 250 033 7.554 *** par_85 e27 298 035 8.529 *** par_86 e28 321 033 9.620 *** par_87 e29 190 025 7.711 *** par_88 e30 242 026 9.216 *** par_89 e31 277 029 9.621 *** par_90 91 Label Model Fit Summary CMIN Model NPAR CMIN DF P CMIN/DF Default model 90 482.670 406 005 1.189 Saturated model 496 000 Independence model 31 5829.062 465 000 12.536 RMR, GFI Model RMR GFI AGFI PGFI Default model 024 912 892 746 Saturated model 000 1.000 Independence model 233 244 194 229 Baseline Comparisons Model NFI Delta1 RFI rho1 IFI Delta2 TLI rho2 CFI Default model 917 905 986 984 986 Saturated model 1.000 Independence model 000 1.000 000 1.000 000 000 000 Parsimony-Adjusted Measures Model PRATIO PNFI PCFI Default model 873 801 861 Saturated model 000 000 000 Independence model 1.000 000 000 Model NCP LO 90 HI 90 Default model 76.670 25.743 135.829 Saturated model 000 000 000 Independence model 5364.062 5121.198 5613.382 NCP FMIN Model FMIN F0 LO 90 HI 90 Default model 1.527 243 081 430 Saturated model 000 000 000 000 Independence model 18.446 16.975 16.206 17.764 RMSEA Model RMSEA LO 90 HI 90 PCLOSE Default model 024 014 033 1.000 Independence model 191 187 195 000 AIC Model AIC BCC BIC CAIC Default model 662.670 682.952 1000.971 1090.971 Saturated model 992.000 1103.775 2856.415 3352.415 Independence model 5891.062 5898.048 6007.588 6038.588 92 ECVI Model ECVI LO 90 HI 90 MECVI Default model 2.097 1.936 2.284 2.161 Saturated model 3.139 3.139 3.139 3.493 Independence model 18.643 17.874 19.432 18.665 HOELTER Model HOELTER 05 HOELTER 01 298 312 28 30 Default model Independence model F Structural Equation Modeling Regression Weights: (Group number - Default model) Estimat e S.E C.R P ATTITUDE_TOWARD_