25 1 Vol. 25 No. 1 2016 2 OPERATIONS RESEARCH AND MANAGEMENT SCIENCE Feb. 2016 1 2 2 1. 100083 2. 100084 F272. 3 A 1007-3221 2016 01-0158-08 doi 10. 12005 /orms. 2016. 0021 Equity Financing Regulation and Corporate Capital Structure A Model and the Simulation WANG Zheng-wei 1 ZHU Wu-xiang 2 ZHAO Dong-qing 2 1. PBC School of Finance Tsinghua University Beijing 100083 China 2. School of Economics and Management Tsinghua University Beijing 100084 China Abstract The purpose of this paper is to find whether there is a strict relationship between corporate capital structure adjustment and corporate financing friction. This paper builds a financing model with market regulation and uses a computer simulation method to examine the effects of equity financing regulation friction on company s target capital structure and the adjustment of capital structure. At the same time it also tests the applicability of the capital structure adjustment model. The results show that the change of external equity financing frictions is not only an important influencing factor on the target capital structure but also an important factor on the adjustment speed of company s capital structure which provides a unique view to observe the financing friction of capital market. In addition the test on the existing model of the capital structure adjustment tells us that the model widely used by previous authors ignores a strong constraint between variables which will lead to serious model specification bias. Key words corporate finance equity financing regulation computer simulation method capital structure capital structure adjustment 0 Trade-off Pecking order Market timing Modigliani Miller 1958 1 MM Shyam-Sunder Myers 2013-02-28 71002074 71472100
1 159 2 Fama French 3 Loof 4 Flannery Rangan 5 Byoun 6 Wang Zhu 7 Flannery Rangan 2 30% 1965 ~ 2001 2 1 2 5 Flannery Rangan 100% 5 8 9 10 Dewaelheynsa Hulle 11 Cook Tang 12 13 14 IPO 2006 1 1. 1 1 1 15 16 t 1 I t t + 1 a t + 1 f I t f
160 2016 25 f 0 = 0 f' > 0 f < 0 a t + 1 b t b t 0-1 p 1 1 t a t - p 0 t D t E t δ t 0 r D τ δ t 0 if b t = 0 5 t t δ t δ t δ t δ t < 0 E t + D t δ t > 0 1 d t D t E t D t = D t - 1 + d t 1 I t E t + D t 6 E t = E t - 1 + a t f I t - 1 - I t - 1 - D t - 1 r D 1 - τ - δ t max V = E 1 T t = 1 δ t 1 + r E t -1 3 4 ~ 6 E t > 0 2 2 E t 0 t = 1 18 E r E δ t = 0 D t = 0 I t = 0 if E t < 0 7 r E r D 1. 2 MM 1 2 Baker 17 L D t L 4 D t + E t b t = 0 δ t 0 t b = b t = 1 b t = 0 t b t = 1 t t
1 161 b t =1 b t = 0 δ t 0 L 60% 90% T 1 τ = 25% r E 8% 12% r D 4% 8% p 0 1 p b t p 11 - p 0 p 2 p 3 p 1 0. 3 0. 5 0. 7 T = 30 p 300 T = 30 300 300 1 a 1 = 1 a t + 1 = a t η t + 1 lnη t + 1 N μ t + 1 σ 2 t + 1 μ t 20% σ t 0. 1 p = 0. 3 p = 0. 5 p = 0. 7 a t 3. 1 MATLAB 7 1 0. 557 0. 6272 0. 2689 0. 2228 0. 8999 0. 8999 0 0 0. 5935 0. 6675 8677 8657 0. 5873 0. 6307 0. 248 0. 2201 0. 9 0. 9 0 0 0. 6269 0. 6759 8567 8536 0. 6406 0. 6586 0. 2094 0. 1960 0. 9000 0. 9000 0. 0337 0. 0337 0. 6984 0. 7090 8567 8536 t 18. 71 t 12. 08 t 5. 82 t H0 H1
162 2016 25 1 3. 3 t p = 0. 3 p = 0. 5 p = 0. 7 3 2 ~ 6 L i t - L i t - 1 = λ L * i t - L i t - 1 8 L i t i t L * i t i t 1% 3. 2 λ λ λ = 1 λ = 0 0 < λ < 1 λ > 1 λ < 0 p 2 2 p = 0. 3 / p = 0. 5 p = 0. 7 ANOVA L i t = α + λl * i t + 1 - λ L i t - 1 + ε i t 9 p = 0. 3 p = 0. 5 p = 0. 7 α ε i t 9 t 7. 69 t 22. 74 t - 7. 69 / t 15. 18 F 259. 89 Pr > F < 0. 0001 t - 22. 74 t - 15. 18 1. t t 2. F ANOVA / 8 λ λ Panel data Pooled Fixed-Effect Model Random-Effect Model 4 2 t 1 p F LM Hausman Baltagi 7 19 3 9
1 163 3 9 p = 0. 3 p = 0. 5 p = 0. 7 0. 129 17. 86 *** 0. 088 21. 71 *** 0. 051 16. 37 *** L i t - 1 0. 193 36. 05 *** 0. 129 27. 07 *** 0. 065 18. 94 *** L * i t 0. 693 157. 24 *** 0. 788 187. 51 *** 0. 884 277. 01 *** year Yes no no F F = 1. 23 pr > F = 0. 004 F = 1. 13 Pr > F = 0. 066 F = 1. 09 Pr > F = 0. 146 LM chi 2 = 5. 22 Pr > chi 2 = 0. 022 chi 2 = 1. 48 Pr > chi 2 = 0. 224 chi 2 = 0. 03 Pr > chi 2 = 0. 863 Hausman chi 2 = 330. 76 Pr > chi 2 = 0. 000 chi 2 = 572. 58 Pr > chi 2 = 0. 000 chi 2 = 545. 80 Pr > chi 2 = 0. 000 8357 8236 8236 R 2 76. 91% 82. 05% 90. 37% 1. t 2. t *** 1% 0 3. L i t - 1 L * i t VIF 1 4. year yes F no F year yes year no LM hausman 3 p = 0. 3 3 λ p' = 0. 3 = 0. 693 λ 'p = 0. 5 = 0. 788 λ p' = 0. 7 F = 0. 884 1 - L i t - 1 λ LM λ 5% Hausman 1% 9 L * i t p = 0. 3 λ L i t - 1 1 - λ 1 p = 0. 5 L * i t L i t - 1 p = 0. 7 9 3 L * i t + L i t - 1 R 2 R 2 = 1 F 1% 76. 91% 82. 05% 90. 37% 9 9 L i t - 1 1 - λ 1 - L * 4 ~ i t 6 L * i t L i t - 1 λ p = 0. 3 = 0. 807 5 λ p = 0. 5 = 0. 871 λ p = 0. 7 = 0. 935 ΔReal i t = α + λδtarget i t + ε i t 10 ΔReal i t L i t - L i t - 1 ΔTarget i t L * i t - L i t - 1 α ε i t 9 9 L * i t 4 λ 4 p = 0. 3 0. 5 0. 7
164 2016 25 89. 31% 94. 73% R 2 84. 76% 4 10 p = 0. 3 p = 0. 5 p = 0. 7 0. 055 45. 67 *** 0. 037 36. 02 *** 0. 017 25. 57 *** ΔTarget i t 0. 735 215. 05 *** 0. 821 262. 24 *** 0. 908 384. 55 *** year yes no no F F = 1. 03 pr > F = 0. 368 F = 1. 04 Pr > F = 0. 296 F = 1. 05 Pr > F = 0. 260 LM chi 2 = 0. 00 Pr > chi 2 = 0. 9954 chi 2 = 0. 09 Pr > chi 2 = 0. 765 chi 2 = 0. 08 Pr > chi 2 = 0. 781 Hausman chi 2 = 244. 55 Pr > chi 2 = 0. 000 chi 2 = 225. 62 Pr > chi 2 = 0. 000 chi 2 = 19. 93 Pr > chi 2 = 0. 000 8357 8236 8236 R 2 84. 76% 89. 31% 94. 73% 1. t 2. t *** 1% 0 3. year yes F no F year yes year no LM hausman 4 ΔTarget i t ΔTarget i t 1% 10 ΔTarget i t λ 1 10 2 λ p = 0. 3 = 0. 735 λ p = 0. 5 = 0. 821 λ p = 0. 7 = 0. 908 3 λ λ' 1 λ λ λ' 9 λ 2 λ' 9 10 8 8 5 4
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