Capital structure and corporate performance evidence in vietnam

UNIVERSITY OF ECONOMICS INSTITUTE OF SOCIAL STUDIES HO CHI MINH CITY THE HAGUE VIETNAM THE NETHERLANDS VIETNAM – THE NETHERLANDS PROGRAMME FOR M.A IN DEVELOPMENT ECONOMICS CAPITAL STRUCTURE AND CORPORATE PERFORMANCE: EVIDENCE IN VIETNAM A thesis submitted in partial fulfillment of requirements for the degree of MASTER OF ARTS IN DEVELOPMENT ECONOMICS By PHẠM THỊ THÚY DIỄM Academic Supervisor Dr CAO HÀO THI HO CHI MINH CITY, NOVEMBER 2013 i CERTIFICATION “I certify that the substance of this thesis has not already been submitted for any degree and has not been currently submitted for any other degree I certify that to the best of my knowledge and help received in preparing this thesis and all sources used have acknowledged in this thesis” PHAM THI THUY DIEM Date: … November 2013 ii ACKNOWLEGMENTS Foremost, I would like to thank so much to Vietnam – The Netherlands programme for Master of Art in Development Economics (MDE programme), I have been studying useful knowledge Besides, I thank so much for all lecturers because of their valuable contributions as well as all my friends because of their helps during period of studying I would like to thank deeply to my academic supervisor, Doctor Cao Hao Thi for his enthusiastic supports, advices and great encouragements during my completion of the thesis Last but not least, I am deeply grateful to my family, especially my mother who supports in my life as well as lovely thank to my husband Nguyen Phuc Loc and his family One time again, I am grateful to all of you Thank you so much! iii ABSTRACT This article aims to examine the influence of capital structure on corporate performance and the reverse causality from corporate performance to capital structure, using data from 150 Vietnamese listed manufacturing firms from 2008 to 2012 Comparing the results of random effects model (REM) and fixed effects model (FEM), the more appropriate model will be discussed some empirical results The study found that the capital structure has significant and positive relationship with corporate performance in associated with debt to assets (TDTA) and short-term debt to assets (STDTA) In contrast, corporate performance is insignificantly influenced by debt to assets (TDTA) The results also state that there is no existence of optimal capital structure decision The reverse causality from corporate performance to capital structure, corporate performance has a significant and positive influence on capital structure in related with debt to assets (TDTA) and short-term debt to assets (STDTA) but corporate performance has no meaning with long-term debt to assets (LTDTA) Keywords: capital structure, leverage, corporate performance iv TABLE OF CONTENTS CERTIFICATION i ACKNOWLEGMENTS ii ABSTRACT iii LIST OF FIGURES vii LIST OF TABLES viii ABBREVIATIONS ix Chapter 1: INTROCDUCTION 1.1 Problem statement 1.2 Research objective 1.3 Research questions 1.4 Research scope and data 1.5 Thesis structure Chapter 2: LITERATURE REVIEW 2.1 Conceptual issues 2.1.1 Capital structure 2.1.2 Corporate performance 2.2 Theoretical Literature 2.2.1 Theories of capital structure and corporate performance 2.2.2 Theories of reverse causality from corporate performance to capital structure 12 2.3 Empirical Literature 13 2.3.1 The impacts of capital structure on corporate performance 13 v 2.3.2 2.4 The reverse causality from corporate performance to capital structure 18 Conceptual framework 21 Chapter 3: RESEARCH METHODOLOGY 25 3.1 Research process 25 3.2 Measurement of variables 27 3.2.1 Capital structure variable 27 3.1.2 Corporate performance variable 28 3.1.3 Control variables for firm characteristics 29 3.3 Hypothesis development 34 3.4 Model specification 35 3.4.1 Capital structure and corporate performance 35 3.3.2 Reverse causality from corporate performance to capital structure 37 3.4 Estimation strategy 38 3.5 Data collection 39 Chapter 4: EMPIRICAL ANALYSIS RESULTS 41 4.1 Descriptive statistics 41 4.2 Empirical results 44 4.2.1 Corporate performance and capital structure 44 4.2.2 Reverse causality from corporate performance to capital structure 50 Chapter 5: CONCLUSIONS 55 5.1 Conclusions 55 5.2 Limitations and suggestion of further research 56 vi REFERENCE 58 APPENDIX 65 vii LIST OF FIGURES Figure 2.1: The trade-off of capital structure Figure 2.2: Conceptual framework for the impacts of capital structure on corporate performance 22 Figure 2.3: Conceptual framework for the reverse causality from corporate performance to capital structure 23 Figure 3.1: Research process 27 Figure 3.2: Analytical framework for the reverse causality from corporate performance to capital structure 34 viii LIST OF TABLES Table 2.1: A summary of the empirical results analyzing the relationship between capital structure and corporate performance 14 Table 2.2: A summary of the empirical results the reverse causality from corporate performance to capital structure 20 Table 3.1: Control variables used in some previous studies 32 Table 3.2: Analytical framework for the impact of capital structure on corporate performance 33 Table 3.3: Variable description and expected sign for Model 36 Table 3.4: Variable description and expected sign for Model 37 Table 3.5: Variable description and expected sign for Model 38 Table 4.1: Summary statistics of the explanatory variables, 2008-2012 42 Table 4.2: Correlation matrix of the explanatory variables, during 2008-2012 43 Table 4.3: Results of Hausman test 44 Table 4.4: Choice between fixed effects model and random effects model 45 Table 4.5: Corporate performance and capital structure 47 Table 4.6: Corporate performance and optimal capital structure 49 Table 4.7: Reverse causality from corporate performance to capital structure 53 ix ABBREVIATIONS EFF: Effectiveness FEM: Fixed effects regression model Growth: Sales growth HNX: Hanoi Stock Exchange HOSE: Ho Chi Minh Stock Exchange LEV: Leverage LTDTA: Long-term debt to total assets M&M: Modigliani and Miller PROFIT: Profitability ROA: Return on total assets ROE: Return on total equity REM: Random effects regression model Size: Firm size STDTA: Short-term debt to total assets Tang: Tangibility 87 Table 15: Hausman Test regression results for Model using LTDTA Coefficients -| (b) (B) | fixed random (b-B) Difference sqrt(diag(V_b-V_B)) S.E -+ -ltdta | -.3502247 -.4726903 1224656 133661 ltdta2 | 1.100664 9288749 1717893 2697936 profit | 7137402 1.243442 -.5297023 071622 size | 2209938 1703836 0506102 0622189 tang | 2041709 3820959 -.177925 0763129 growth | -.0700093 -.0660608 -.0039485 0058406 d2 | 2153866 2134296 001957 d3 | 0511312 0564502 -.005319 0049225 d4 | -.1849743 -.1728673 -.012107 0098793 d5 | -.1347445 -.1134737 -.0212708 0106139 -b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 64.45 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 88 Table 16: Fixed effects regression results for Model using STDTA Fixed-effects (within) regression Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: = 0.3634 Obs per group: = between = 0.2726 avg = 5.0 overall = 0.3012 max = F(10,149) = 26.33 Prob > F = 0.0000 within corr(u_i, Xb) = 0.0594 (Std Err adjusted for 150 clusters in firms) -| tobinq | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -stdta | 1836106 2539483 0.72 0.471 -.3181946 6854157 stdta2 | 4137253 3951308 1.05 0.297 -.3670585 1.194509 profit | 8308981 2340436 3.55 0.001 3684249 1.293371 size | 1873405 0612688 3.06 0.003 0662726 3084083 tang | 2212659 1247569 1.77 0.078 -.0252553 4677871 growth | -.0665166 0183261 -3.63 0.000 -.1027291 -.0303041 d2 | 2086531 028848 7.23 0.000 151649 2656573 d3 | 045795 0308684 1.48 0.140 -.0152014 1067915 d4 | -.1897574 034382 -5.52 0.000 -.2576966 -.1218182 d5 | -.1407143 0318695 -4.42 0.000 -.2036888 -.0777399 _cons | -.4646552 3249452 -1.43 0.155 -1.106751 1774407 -+ -sigma_u | 30733586 sigma_e | 22642959 rho | 64817213 (fraction of variance due to u_i) 89 Table 17: Random effects regression results for Model using STDTA Random-effects GLS regression Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: = 0.3487 Obs per group: = between = 0.4589 avg = 5.0 overall = 0.4030 max = within Random effects u_i ~ Gaussian Wald chi2(10) = 321.05 corr(u_i, X) Prob > chi2 = 0.0000 = (assumed) (Std Err adjusted for 150 clusters in firms) -| tobinq | Robust Coef Std Err z P>|z| [95% Conf Interval] -+ -stdta | -.2687951 2817783 -0.95 0.340 -.8210704 2834801 stdta2 | 936997 4459997 2.10 0.036 0628536 1.81114 profit | 1.330209 2566551 5.18 0.000 8271741 1.833243 size | 1543503 0491529 3.14 0.002 0580124 2506882 tang | 3988796 1318494 3.03 0.002 1404596 6572996 growth | -.06589 019947 -3.30 0.001 -.1049854 -.0267947 d2 | 2100618 0287303 7.31 0.000 1537515 266372 d3 | 0555706 0295486 1.88 0.060 -.0023436 1134849 d4 | -.1722134 0314531 -5.48 0.000 -.2338603 -.1105665 d5 | -.111461 0294521 -3.78 0.000 -.1691861 -.0537359 _cons | -.35088 2719341 -1.29 0.197 -.8838611 1821012 -+ -sigma_u | 22871007 sigma_e | 22642959 rho | 50501038 (fraction of variance due to u_i) 90 Table 18: Hausman Test regression results for Model using STDTA Coefficients -| (b) (B) | fixed random (b-B) Difference sqrt(diag(V_b-V_B)) S.E -+ -stdta | 1836106 -.2687951 4524057 1486637 stdta2 | 4137253 936997 -.5232717 2246719 profit | 8308981 1.330209 -.4993107 0758935 size | 1873405 1543503 0329902 0625731 tang | 2212659 3988796 -.1776137 0778611 growth | -.0665166 -.06589 -.0006265 0059765 d2 | 2086531 2100618 -.0014086 d3 | 045795 0555706 -.0097756 0048953 d4 | -.1897574 -.1722134 -.017544 0099919 d5 | -.1407143 -.111461 -.0292534 0109329 -b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(10) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 65.43 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 91 Table 19: Fixed effects regression results for Model using TDTA Fixed-effects (within) regression Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: = 0.1121 Obs per group: = between = 0.2447 avg = 5.0 overall = 0.2159 max = F(9,149) = 4.40 Prob > F = 0.0000 within corr(u_i, Xb) = 0.2351 (Std Err adjusted for 150 clusters in firms) -| tdta | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -tobinq | 0581658 0250628 2.32 0.022 0086413 1076903 profit | -.384572 0769716 -5.00 0.000 -.5366689 -.2324752 size | 0581419 0330903 1.76 0.081 -.007245 1235287 tang | 0821295 0621208 1.32 0.188 -.0406221 2048812 growth | -.0072377 0059546 -1.22 0.226 -.0190041 0045287 d2 | 0084173 0108699 0.77 0.440 -.0130618 0298963 d3 | 012401 0118892 1.04 0.299 -.0110922 0358942 d4 | 0213479 013992 1.53 0.129 -.0063004 0489962 d5 | 0105544 0147515 0.72 0.475 -.0185948 0397036 _cons | -.1062856 1786161 -0.60 0.553 -.4592334 2466621 -+ -sigma_u | 1773576 sigma_e | 08315189 rho | 81980078 (fraction of variance due to u_i) 92 Table 20: Random effects regression results for Model using TDTA Random-effects GLS regression Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: = 0.1097 Obs per group: = between = 0.2487 avg = 5.0 overall = 0.2247 max = within Random effects u_i ~ Gaussian Wald chi2(9) = 70.06 corr(u_i, X) Prob > chi2 = 0.0000 = (assumed) (Std Err adjusted for 150 clusters in firms) -| tdta | Robust Coef Std Err z P>|z| [95% Conf Interval] -+ -tobinq | 049598 0238599 2.08 0.038 0028334 0963626 profit | -.4458289 0708319 -6.29 0.000 -.5846568 -.3070009 size | 090193 0199949 4.51 0.000 0510037 1293823 tang | 09531 0536155 1.78 0.075 -.0097744 2003944 growth | -.0105384 0055033 -1.91 0.056 -.0213247 0002478 d2 | 0094228 0107235 0.88 0.380 -.0115949 0304404 d3 | 0096141 0111259 0.86 0.388 -.0121923 0314205 d4 | 0142123 0127494 1.11 0.265 -.0107761 0392007 d5 | 0023363 0137604 0.17 0.865 -.0246336 0293062 _cons | -.272847 111003 -2.46 0.014 -.4904089 -.055285 -+ -sigma_u | 16417783 sigma_e | 08315189 rho | 79585111 (fraction of variance due to u_i) 93 Table 21: Hausman Test regression results for Model using TDTA Coefficients -| (b) (B) | fixed random (b-B) Difference sqrt(diag(V_b-V_B)) S.E -+ -tobinq | 0581658 049598 0085677 0042379 profit | -.384572 -.4458289 0612568 0161329 size | 0581419 090193 -.0320511 0192693 tang | 0821295 09531 -.0131805 0197944 growth | -.0072377 -.0105384 0033007 0019897 d2 | 0084173 0094228 -.0010055 0008812 d3 | 012401 0096141 0027869 0021668 d4 | 0213479 0142123 0071356 0036298 d5 | 0105544 0023363 0082181 0038133 -b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 19.57 Prob>chi2 = 0.0207 (V_b-V_B is not positive definite) 94 Table 22: Fixed effects regression results for Model using LTDTA Fixed-effects (within) regression Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: = 0.0683 Obs per group: = between = 0.2475 avg = 5.0 overall = 0.2132 max = F(9,149) = 2.46 Prob > F = 0.0122 within corr(u_i, Xb) = 0.2730 (Std Err adjusted for 150 clusters in firms) -| ltdta | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -tobinq | 0069243 0126508 0.55 0.585 -.0180738 0319224 profit | -.0804948 048179 -1.67 0.097 -.175697 0147075 size | -.010522 0202205 -0.52 0.604 -.0504781 029434 tang | 135421 0459832 2.95 0.004 0445576 2262844 growth | 0015488 0037341 0.41 0.679 -.0058299 0089275 d2 | 0050557 0073519 0.69 0.493 -.0094718 0195832 d3 | 0037309 0080975 0.46 0.646 -.0122699 0197317 d4 | -.0001265 0092894 -0.01 0.989 -.0184824 0182294 d5 | -.0091453 0096607 -0.95 0.345 -.0282351 0099444 _cons | 1021879 1137942 0.90 0.371 -.1226708 3270467 -+ -sigma_u | 1168661 sigma_e | 05405292 rho | 82377422 (fraction of variance due to u_i) 95 Table 23: Random effects regression results for Model using LTDTA Fixed-effects (within) regression Group variable: firms Number of obs Number of groups = = 750 150 R-sq: Obs per group: = avg = max = 5.0 within = 0.0683 between = 0.2475 overall = 0.2132 corr(u_i, Xb) = 0.2730 F(9,149) Prob > F = = 2.46 0.0122 (Std Err adjusted for 150 clusters in firms) -| Robust ltdta | Coef Std Err t P>|t| [95% Conf Interval] -+ -tobinq | 0069243 0126508 0.55 0.585 -.0180738 0319224 profit | -.0804948 048179 -1.67 0.097 -.175697 0147075 size | -.010522 0202205 -0.52 0.604 -.0504781 029434 tang | 135421 0459832 2.95 0.004 0445576 2262844 growth | 0015488 0037341 0.41 0.679 -.0058299 0089275 d2 | 0050557 0073519 0.69 0.493 -.0094718 0195832 d3 | 0037309 0080975 0.46 0.646 -.0122699 0197317 d4 | -.0001265 0092894 -0.01 0.989 -.0184824 0182294 d5 | -.0091453 0096607 -0.95 0.345 -.0282351 0099444 _cons | 1021879 1137942 0.90 0.371 -.1226708 3270467 -+ -sigma_u | 1168661 sigma_e | 05405292 rho | 82377422 (fraction of variance due to u_i) 96 Table 24: Hausman Test regression results for Model using LTDTA Coefficients -| (b) (B) (b-B) sqrt(diag(V_b-V_B)) | fixed random Difference S.E -+ -tobinq | 0069243 0024942 0044301 0025483 profit | -.0804948 -.1150664 0345716 0094763 size | -.010522 0215776 -.0320996 0128004 tang | 135421 1889785 -.0535576 0131103 growth | 0015488 -.0018252 003374 0011376 d2 | 0050557 0051198 -.0000641 d3 | 0037309 0010462 0026847 0010062 d4 | -.0001265 -.0060439 0059174 0021727 d5 | -.0091453 -.0160333 006888 0023029 -b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 56.66 Prob>chi2 = 0.0000 (V_b-V_B is not positive definite) 97 Table 25: Fixed effects regression results for Model using STDTA Fixed-effects (within) regression Number of obs = 750 Group variable: firms Number of groups = 150 R-sq: = 0.1292 Obs per group: = between = 0.1579 avg = 5.0 overall = 0.1534 max = F(9,149) = 4.83 Prob > F = 0.0000 within corr(u_i, Xb) = 0.0385 (Std Err adjusted for 150 clusters in firms) -| stdta | Robust Coef Std Err t P>|t| [95% Conf Interval] -+ -tobinq | 0364125 0147661 2.47 0.015 0072344 0655906 profit | -.3151726 0708984 -4.45 0.000 -.4552688 -.1750764 size | 07786 0317468 2.45 0.015 015128 1405921 tang | -.0411467 0495399 -0.83 0.408 -.1390383 0567449 growth | -.0105021 0055821 -1.88 0.062 -.0215325 0005282 d2 | 0069936 0072376 0.97 0.335 -.007308 0212952 d3 | 0102892 008842 1.16 0.246 -.0071827 0277611 d4 | 021626 0108061 2.00 0.047 000273 042979 d5 | 0222641 0115825 1.92 0.056 -.0006232 0451514 _cons | -.2487591 1726867 -1.44 0.152 -.5899903 092472 -+ -sigma_u | 14140798 sigma_e | 06851223 rho | 80988665 (fraction of variance due to u_i) 98 Table 26: Random effects regression results for Model using STDTA Random-effects GLS regression Group variable: firms Number of obs Number of groups = = 750 150 R-sq: Obs per group: = avg = max = 5.0 within = 0.1270 between = 0.1798 overall = 0.1711 Random effects u_i ~ Gaussian corr(u_i, X) = (assumed) Wald chi2(9) Prob > chi2 = = 64.22 0.0000 (Std Err adjusted for 150 clusters in firms) -| Robust stdta | Coef Std Err z P>|z| [95% Conf Interval] -+ -tobinq | 0319247 0141527 2.26 0.024 0041858 0596635 profit | -.3407963 0653682 -5.21 0.000 -.4689156 -.212677 size | 0779003 0165655 4.70 0.000 0454326 110368 tang | -.0767093 0400126 -1.92 0.055 -.1551326 001714 growth | -.0104628 0055507 -1.88 0.059 -.021342 0004164 d2 | 0080509 0072958 1.10 0.270 -.0062485 0223503 d3 | 0102731 0083161 1.24 0.217 -.0060262 0265724 d4 | 0204109 0099614 2.05 0.040 0008869 039935 d5 | 0209372 0110505 1.89 0.058 -.0007213 0425957 _cons | -.2314657 0897023 -2.58 0.010 -.407279 -.0556524 -+ -sigma_u | 13548674 sigma_e | 06851223 rho | 79636411 (fraction of variance due to u_i) 99 Table 27: Hausman Test regression results for Model using STDTA Coefficients -| (b) | fixed (B) random (b-B) Difference sqrt(diag(V_b-V_B)) S.E -+ -tobinq | 0364125 0319247 0044878 0036976 profit | -.3151726 -.3407963 0256237 0141135 size | 07786 0779003 -.0000403 0159312 tang | -.0411467 -.0767093 0355626 0165657 growth | -.0105021 -.0104628 -.0000393 0017378 d2 | 0069936 0080509 -.0010573 0011436 d3 | 0102892 0102731 0000161 0019785 d4 | 021626 0204109 0012151 0031217 d5 | 0222641 0209372 0013269 0032699 -b = consistent under Ho and Ha; obtained from xtreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 9.15 Prob>chi2 = 0.4233 (V_b-V_B is not positive definite) 100 Table 28: Testing for multicollinearity in Model using TDTA vif Variable | VIF 1/VIF -+ -tobinq | 1.52 0.659167 profit | 1.38 0.724435 size | 1.10 0.906883 tang | 1.07 0.933661 growth | 1.04 0.959992 -+ -Mean VIF | 1.22 Table 29: Testing for multicollinearity in Model using LTDTA vif Variable | VIF 1/VIF -+ -tobinq | 1.52 0.659167 profit | 1.38 0.724435 size | 1.10 0.906883 tang | 1.07 0.933661 growth | 1.04 0.959992 -+ -Mean VIF | 1.22 Table 30: Testing for multicollinearity in Model using STDTA vif Variable | VIF 1/VIF -+ -tobinq | 1.52 0.659167 profit | 1.38 0.724435 size | 1.10 0.906883 tang | 1.07 0.933661 growth | 1.04 0.959992 -+ -Mean VIF | 1.22 101 Table 31: Testing for multicollinearity in Model using TDTA vif Variable | VIF 1/VIF -+ -tdta | 1.31 0.760756 size | 1.22 0.822286 profit | 1.15 0.871605 tang | 1.04 0.962063 growth | 1.03 0.968062 -+ -Mean VIF | 1.15 Table 35: Testing for multicollinearity in Model using LTDTA vif Variable | VIF 1/VIF -+ -ltdta | 1.45 0.692028 tang | 1.35 0.740657 size | 1.09 0.917131 profit | 1.08 0.929888 growth | 1.03 0.968131 -+ -Mean VIF | 1.20 Table 36: Testing for multicollinearity in Model using STDTA vif Variable | VIF 1/VIF -+ -stdta | 1.23 0.814546 size | 1.16 0.861729 profit | 1.09 0.918742 tang | 1.05 0.955206 growth | 1.03 0.969294 -+ -Mean VIF | 1.11 ... optimal capital structure decision The reverse causality from corporate performance to capital structure, corporate performance has a significant and positive influence on capital structure in related... between firm’s capital structure and its performance Particularly, this study investigates: - The influence of firm’s capital structure on its performance - Finding out the optimal capital structure. .. negative link exists between capital structure and corporate performance Majumdar and Chhibber (1999) examine the relationship between firm’s capital structure and its performance for Indian corporations