The Impact of Stopping IPO in Shenzhen A Stock Market on Guiding Pattern of Information in China s Stock Markets

2005 9 9 :100026788 (2005) 0920036206,, (, 230009) :,.,, A ;, A A, A A.,2000 10,.,,,. : ; ; ; : F830191 : A The Impact of Stopping IPO in Shenzhen A Stock Market on Guiding Pattern of Information in China s Stock Markets TANG Yun2shu, ZHU J un2hong, LUO Zheng2qing (School of Management,Hefei University of Technology,Hefei 230009,China) Abstract : From the point of view of information, this paper examines the difference of guiding pattern of information in China s stock markets before and after Shenzhen A stock market stopped IPO. We find that before IPO was stopped in Shenzhen A stock market, Shenzhen A stock returns Granger2cause Shanghai A stock market returns and return volatility unilaterally, but after stopping IPO, the relation became no statistical significance. Contrarily, Shanghai A stock market returns and return Volatility became to Granger2cause Shenzhen A stock returns remarkably. The result make clear that stopping IPO in Shenzhen A stock market from October,2000 is crucial for the status between Shenzhen and Shanghai in China s stock markets. The trend that investors abandoned Shenzhen stock market turning for Shanghai market have emerged. In addition,we also find that with the improving of China s stock market, trading volume has had predictive power for stock return and return volatility. Key words : stock returns ; return volatility ;trading volume ; guiding pattern of information 1 2000 10,,,,,.,,,,,,.?,,..,,, :2003210206 : (AHSK01202D044) : (1976 - ),,,, : ; (1963 - ),,,,, :,.

9 37.,,, 1.,2000 10., A,,, A A., A A.,,.,. : ; ; ;. 2,. ( Price2 discovery theory), ( Schreiber Schwartz, 1996).,..,,.,,.,.,,,,. Eun Shim(1989),,. King (1990) 1987 10,,,. Hamao (1990) 1987 10,,,..,.,.,,,,.,.,.,. Epps (1976),. (2000),.., A,,. 3 311 1.

, 1992 2 28 2000 9 31, 2000 10 1 2002 12 31. A A A. (CSMAR). 312 Granger..,, ADF(Augmented Dickey2Fuller) P2P(Phillips2Perron). 1) ADF(Augmented Dickey2Fuller) Y t 2) P2P(Phillips2Perron) m = 1 + Y t - 1 + i Y t - i Y t i = 1 + t. (1) = 0 + Y t - 1 + t. (2) ADF, H0 : = 0 ; P2P, H0 : = 1.,. 1 1, A, ADF t ( ) P2P t ( ) H0 : ADF P2P PanelA : A SHR - 20. 58 3 3-48. 34 3 3 H0 ( SHR 2-21. 72 3 3-50. 34 3 3 H0 SHV - 8. 18 3 3-10. 14 3 3 H0 ). PanelB : A SZR - 20. 35 3 3-50. 13 3 3 H0, Granger. SZR 2-18. 12 3 3-49. 36 3 3 H0 313 SZV - 6. 89 3 3-7. 58 3 3 H0 Granger :1) SHR A ;SHR 2 A ;SHV A ;SZR A ; SZR 2 A ;SZV A ;2) 3 5 % ; 3 3 1 %. Granger (test. of causality), X Y ( Granger), X (precede) Y., Y ( ), X Y, X Y ( Granger). Granger : u 1 t u 2 t. Y t Y t = 0 + = m m i = 1 0 + i = 1 i Y t - i i Y t - i + u 1 t, (3) k + X Y (unidirectional causality), : j = 1 H0 : j = 0, j = 1,2, k ; H1 : j 0, j = 1,2, k. j X t - j + u 2 t, (4), RSS 1 RSS 2 (3) (4) OLS, F : 38 2005 9

9 39 F = ( RSS 1 - RSS 2 )Πm. (5) RSS 2 Π( n - k) F m ( n - k) F., m X, n Y, k (4). F,, X (4), X Y ( Granger). 4 (3) (4), A, (5) F 1., 5. 2, 3. 2 A Null Hypothesis F - P - PanelA1 : A A SZR dose not Granger cause SHR F(5,2018) = 2. 2044 0. 0513 Yes SZR dose not Granger cause SHR 2 F(5,2018) = 2. 4233 0. 0336 Yes SZR dose not Granger cause SHV F(5,2018) = 27. 3466 0. 0000 Yes PanelA2 : A A SZR 2 dose not Granger cause SHR F(5,2018) = 5. 2993 0. 0001 Yes SZR 2 dose not Granger cause SHR 2 F(5,2018) = 2. 8917 0. 0132 Yes SZR 2 dose not Granger cause SHV F(5,2018) = 5. 7246 0. 0000 Yes PanelA3 : A A SZV dose not Granger cause SHR F(5,2018) = 0. 0425 0. 9990 No SZV dose not Granger cause SHR 2 F(5,2018) = 0. 5149 0. 7652 No SZV dose not Granger cause SHV F(5,2018) = 7. 2143 0. 0000 Yes PanelB1 : A A SHR dose not Granger cause SZR F(5,2018) = 1. 1909 0. 3111 No SHR dose not Granger cause SZR 2 F(5,2018) = 0. 9497 0. 4476 No SHR dose not Granger cause SZV F(5,2018) = 13. 7256 0. 0000 Yes PanelB2 : A A SHR 2 dose not Granger cause SZR F(5,2018) = 0. 5951 0. 7038 No SHR 2 dose not Granger cause SZR 2 F(5,2018) = 0. 1080 0. 9906 No SHR 2 dose not Granger cause SZV F(5,2018) = 0. 2071 0. 9596 No PanelB3 : A A SHV dose not Granger cause SZR F(5,2018) = 0. 9419 0. 4526 No SHV dose not Granger cause SZR 2 F(5,2018) = 0. 9312 0. 4596 No SHV dose not Granger cause SZV F(5,2018) = 1. 8524 0. 0996 Yes :1) SHR A ;SHR 2 A ;SHV A ;SZR A ; SZR 2 A ;SZV A. 2) P -. 3), 10 %, Yes, ; No,. 1 Eviews410.

40 2005 9 3 A Null Hypothesis F - P - PanelA1 : A A SZR dose not Granger cause SHR F(5,524) = 0. 9107 0. 4737 No SZR dose not Granger cause SHR 2 F(5,524) = 4. 0228 0. 0014 Yes SZR dose not Granger cause SHV F(5,524) = 8. 9085 0. 0000 Yes PanelA2 : A A SZR 2 dose not Granger cause SHR F(5,524) = 2. 6838 0. 0208 Yes SZR 2 dose not Granger cause SHR 2 F(5,524) = 7. 5698 0. 0000 Yes SZR 2 dose not Granger cause SHV F(5,524) = 7. 1083 0. 0000 Yes PanelA3 : A A SZV dose not Granger cause SHR F(5,524) = 2. 6441 0. 0225 Yes SZV dose not Granger cause SHR 2 F(5,524) = 3. 5526 0. 0036 Yes SZV dose not Granger cause SHV F(5,524) = 14. 0497 0. 0000 Yes PanelB1 : A A SHR dose not Granger cause SZR F(5,524) = 0. 8459 0. 5176 No SHR dose not Granger cause SZR 2 F(5,524) = 5. 3792 0. 0001 Yes SHR dose not Granger cause SZV F(5,524) = 7. 3134 0. 0000 Yes PanelB2 : A A SHR 2 dose not Granger cause SZR F(5,524) = 1. 7176 0. 1288 No SHR 2 dose not Granger cause SZR 2 F(5,524) = 9. 1188 0. 0000 Yes SHR 2 dose not Granger cause SZV F(5,524) = 6. 8278 0. 0000 Yes PanelB3 : A A SHV dose not Granger cause SZR F(5,524) = 1. 7668 0. 1179 No SHV dose not Granger cause SZR 2 F(5,524) = 2. 7423 0. 0186 Yes SHV dose not Granger cause SZV F(5,524) = 6. 9239 0. 0000 Yes :1) SHR A ;SHR 2 A ;SHV A ;SZR A ; SZR 2 A ;SZV A. 2) P - ; Yes,, No,. 3), 10 %. 2 3,, 2 3 4. 4,, A A., A,, A ;,, A A.,,,,., 4,,, A 11179 % 1, A, A 10 %.,,,,.,,,. Epps (1976) 1 3.

9 41 (2000). 4 A, SHAR SHAR 2 SHAV SHAR SHAR 2 SHAV R C C R R C C R R C C R R C C R R C C R R C C R SZAR Y N Y N Y N N 3 N Y N Y N SZAR 2 Y N Y N Y N Y Y 3 Y Y 3 Y Y 3 SZAV N Y N N Y Y Y 3 Y Y 3 Y 3 Y Y :1) SHR A ;SHR 2 A ;SHV A ;SZR A ; SZR 2 A ;SZV A. 2) R C ;C R. 3) Y, N. 4) 3. 5.,.,, A, ;, A A, A A,.,2000 10,.,,,,,,.,.,,,,,,.,,,,,.,,,.,. :. : [1 ] Eun C,Shim S. International transmission of stock market movements[j ]. Journal of Financial and Quantitative Analysis,1989, 24 : 241-256. [2 ] King M, Wadhwani S. Transmission of volatility between stock markets[j ]. The Review of Financial Studies, 1990, 3 : 5-33. [3 ] Hamao Y, Masulis R W, Ng V. Correlations in price changes and volatility across international stock markets [J ]. Review of Financial Studies, 1990, 3 : 281-307. [4 ] Epps T W, Epps M L. The stochastic dependence of security price changes and transaction volume :implications for the mixture2of2 distributions hypothesis[j ]. Econometrica,1976, 44 :305-321. [5 ],. [J ].,2000,19 (5) :69-71. Sheng J P, Gao F M. Empirical study on correlation between Trading Volume and Stock Returns[J ]. Forecasting,2000, 19 (5) :69-71.