# The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China

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## Abstract

**:**

## 1. Introduction

## 2. Policy Changes in China’s Stock Market

## 3. Data and Methodology

#### 3.1. Data

#### 3.2. Descriptive Statistics

_{B}and R

_{S}are the weekly returns of the biggest and smallest size portfolios, respectively. Before the empirical analysis is presented, necessary descriptive statistics are essential. More specific descriptive statistics are stated in Table 2:

#### 3.3. Vector Auto-regression (VAR)

_{S,t}and R

_{S,t-k}are the equal-weighted weekly returns on the smallest 30% portfolio at period t and period t-k, while R

_{B,t}and R

_{B,t-k}present the equal-weighted weekly return on the largest 30% portfolio at period t and period t-k. (We obtain similar results using value-weighted weekly returns, results are available upon request.) On the other hand, in Equations (3) and (4), R

_{S,i}(t) and R

_{S,i}(t-k) are the equal-weighted weekly returns on the smallest 30% portfolio at period t and period t-k in industry i, while R

_{B,i}(t) and R

_{B,i}(t-k) present the equal-weighted weekly return on the largest 30% portfolio at period t and period t-k in industry i. Moreover, in Equations (1) and (3), ${a}_{k}$ and ${b}_{k}$ are the coefficients of the lagged returns of R

_{S}and R

_{B}, respectively. In Equations (2) and (4), c

_{k}and d

_{k}are the coefficients of lagged returns of R

_{S}and R

_{B}, respectively. ${a}_{0}$ and ${c}_{0}$ (${a}_{i,0}$ and ${\mathrm{c}}_{i,0}$) are the constant terms, correspondingly. Finally, ${u}_{t}$ and ${\mathrm{v}}_{t}$ (${u}_{i,t}$ and ${\mathrm{v}}_{i,t}$) are the error terms, respectively.

## 4. Market Conditions and Intra-Industry Information Diffusion

_{up,t-k}and D

_{down,t-k}, are added into the original VAR model. Particularly, the new conditional VAR model is stated in the following equations:

_{up,t-k}and D

_{down,t-k}are dummy variables, which correspondingly reflect the up and down markets conditions at period t-k. D

_{up,t-k}equals one if the market condition becomes up and zero otherwise. In a similar way, D

_{down,t-k}equals one if the market state is down and zero otherwise. The reason that two dummy variables are set is to achieve the VAR investigations independently of the up and down conditions. For example, we examine whether big firms react faster than smaller firms to acquire common information in the down market state by employing a cross-equation test of null hypothesis: $\sum _{k=1}^{K}{b}_{K,down}}={\displaystyle \sum _{k=1}^{K}{c}_{K,down}$. On the other hand, in an up market condition, we examine whether big firms react faster than smaller firms to acquire common information by using a cross-equation test of null hypothesis: $\sum _{k=1}^{K}{b}_{K,\mathrm{up}}}={\displaystyle \sum _{k=1}^{K}{c}_{K,up}$.

_{k, up}and b

_{k, up}are the coefficients of the lagged returns of R

_{S}and R

_{B}in a up market state, respectively. a

_{k, down}and b

_{k, down}are the coefficients of the lagged returns of R

_{S}and R

_{B}in down market state correspondingly. Similarly, in Equation (6), c

_{k, up}and d

_{k, up}are, respectively, the coefficients of the lagged returns of R

_{S}and R

_{B}in up market state, while c

_{k, down}and d

_{k, down}are the coefficients of the lagged returns of R

_{S}and R

_{B}in a down market state.

#### 4.1. Shorter Horizon of Market Conditions

#### 4.2. Longer Horizon of Market Conditions

## 5. Policy Change and Intra-Industry Information Diffusion

#### 5.1. Examining the Impact of Policy Changes

_{p1}, D

_{p2}, and D

_{p3}, are added into the original VAR model. Hence, the new conditional VAR model is stated in the underlying equations:

_{p1, t-k}, D

_{p2, t-k}, and D

_{p3, t-k}are dummy variables, which respectively reflect the three sub-periods at period t-k. D

_{p1, t-k}equals one if the market is in the first sub-period (January 2002–February 2005) and zero otherwise. In a similar way, D

_{p2, t-k}equals one if the market is in the second sub-period (March 2005–January 2010) and is zero otherwise. D

_{p3, t-k}equals one if the market is in the third sub-period (February 2010–December 2013) and zero, or else. The purpose for setting the three dummy variables is to identify the VAR analysis separately for the different sub-periods. For example, the study examines whether big firms react faster than the smaller firms to common information in the first sub-period by employing a cross-equation test for null hypothesis:$\sum _{k=1}^{\mathrm{k}}{\mathrm{b}}_{K,\mathrm{p}1}$ = $\sum _{k=1}^{\mathrm{k}}{\mathrm{c}}_{K,\mathrm{p}1}$. Similarly, a cross-equation test for the null hypothesis is used in the second (third) sub-period: $\sum _{k=1}^{\mathrm{k}}{\mathrm{b}}_{K,\mathrm{p}2}$ = $\sum _{k=1}^{\mathrm{k}}{\mathrm{c}}_{K,\mathrm{p}2}$. ($\sum _{k=1}^{\mathrm{k}}{\mathrm{b}}_{K,\mathrm{p}3}$ = $\sum _{k=1}^{\mathrm{k}}{\mathrm{c}}_{K,\mathrm{p}3}$.).

_{S}and R

_{B}, in the first (second; third) sub-period, correspondingly. Similarly, in Equation (8), ${\mathrm{c}}_{\mathrm{k},p1}$ and ${\mathrm{d}}_{\mathrm{k},p1}$ (${\mathrm{c}}_{\mathrm{k},p2}$ and ${\mathrm{d}}_{\mathrm{k},p2}$; ${\mathrm{c}}_{\mathrm{k},p3}$ and ${\mathrm{d}}_{\mathrm{k},p3}$) are, respectively, the coefficients of the lagged returns of R

_{S}and R

_{B}in the first (second; third) sub-period.

_{3}” is the biggest and “Big-P

_{2}” is in the middle, whereas “Big-P

_{1}” is the smallest. These results suggest that the intra-industry lead-lag effect becomes stronger over time. Therefore, intra-industry information diffusion from big firms to smaller firms becomes slower from the first sub-period to the third sub-period. The policy changes impede intra-industry information diffusion. More delay is brought into intra-industry information diffusion along with the policy changes.

_{1}”, “Big-P

_{2}”, and “Big-P

_{3}” in Table 6 and the sum of coefficients of big firms’ lagged returns in Table 7, it is discovered that the sum of coefficients of big firms’ lagged return decreasingly increase over time, i.e., the growth rate of the sum of coefficients decreases from sub-period 1 to sub-period 2 and from sub-period 2 to sub-period 3. It is found that the first policy change, i.e., the split-share structure reform, has more impact on intra-industry information diffusion than the lifting of short-sale constraints. The empirical results show that the split-share structure reform actually impedes the process of intra-industry information diffusion. However, most people think that the reforms help to improve market efficiency. Do the reforms improve market efficiency? As the most powerful policy reform of China stock market in recent years, the potential impacts of the split share structure reform have been discussed by a few empirical researches. However, effectiveness of the split share structure reform is still in dispute. For example, Chen et al. [13] argue that the split share structure reform improves the liquidity of the market and increases the market efficiency. Yet, Beltratti et al. [25] discover this reform had no impact on the ownership structure of firms in their research. They argue that only some small stocks and historically neglected stocks are partially beneficial from this reform. Additionally, Carpenter et al. [27] suggest that the split share structure reform has little direct immediate impact on the structure of the China stock market in the short term.

#### 5.2. Additional Tests on the Impact of Policy Changes

#### 5.2.1. The Time-series Change of Lead-lag Effects among Three Sub-periods

_{S,i}(t) and R

_{S,i}(t-1) are the weekly returns of the smallest 30% portfolio at period t and period t-1, respectively, in industry i, while R

_{B,i}(t) and R

_{B,i}(t-1) present the weekly returns of the largest 30% portfolio at period t and period t-1 in industry i.

_{1}in Equation (9) and c

_{1}in Equation (10), which examines the size of the lead-lag effect from big firms to smaller firms. Although Mori [17] only investigates the Real Estate Investment Trust market in the U.S., this method is a better reference for our underlying analysis. Furthermore, b

_{1}actually implies the effect that big firms lead small firms, while c

_{1}suggests the effect that small firms lead big firms. Therefore, (b

_{1}–c

_{1}) evaluates the lead-lag effect from big firms to small firms, while control for the reverse lead-lag effect from small firms to big firms. If big firms actually could lead small firms, then b

_{1}should be greater than c

_{1}and (b

_{1}–c

_{1}) should be greater than zero. It presents a distinct lead-lag effect between big and small firms, which reflects that the delayed degree of information diffusion is stronger from big firms to smaller firms. Additionally, if (b

_{1}–c

_{1}) is less than zero, it implies that, instead of the lead-lag effect from big firms to small firms, the reverse lead-lag effect from small firms to big firms appears.

_{1}–c

_{1}) in each three sub-periods. First, the mean becomes bigger over time. It suggests that the lead-lag effects develop stronger from the first sub-period to the last sub-period. Second, the F-statistic for mean difference among three sub-periods is significant at the 1% level. Thus, intra-industry information diffusion is also dissimilar in different sub-periods. Based on the above two viewpoints, the delay of intra-industry information diffusion from big stocks to small stocks becomes greater over time, which imply that policy changes impede intra-industry information diffusion. These results support our results in the previous section.

_{1}–c

_{1}) show a downtrend over time. Moreover, the F-statistic for standard deviation difference among the three sub-periods is significant at the 10% level, which suggests that the difference of standard deviation exists among the three sub-periods. The results show that the volatility of lead-lag effects decrease over time, which suggests that the fluctuation amplitude of information diffusion reduces over time. Consequently, these results support the information volatility of China’s stock market declines, along with its policy changes. With the policy changes, the information environment and transparency of market improve over time. Informational efficiency and transparency are brought into the Chinese stock market.

#### 5.2.2. Potential Reasons on the Lead-lag Changes

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

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Industry | Number of Firms |
---|---|

Automobiles parts | 127 |

Construction and materials | 202 |

Food producers | 95 |

Electronic equipment | 126 |

Industrial engineering | 230 |

Industrial metals and mining | 234 |

Pharmaceuticals and biotechnology | 161 |

Industry Portfolio | Mean | Std | Median | Max | Min | Kurtosis | Skewness | ADF Test | PP Test |
---|---|---|---|---|---|---|---|---|---|

Auto-big | 0.0005 | 0.048 | −0.0005 | 0.1580 | −0.1866 | 4.2936 | −0.0812 | −14.969 *** | −25.490 *** |

Auto-small | 0.00056 | 0.051 | 0.0026 | 0.1481 | −0.2524 | 5.0236 | −0.5042 | −23.492 *** | −23.614 *** |

Cons-big | 0.0002 | 0.047 | 0.0006 | 0.1523 | −0.2028 | 4.5305 | −0.2174 | −15.479 *** | −25.159 *** |

Cons-small | 0.0009 | 0.052 | 0.0048 | 0.1601 | −0.3004 | 5.5341 | −0.5967 | −24.146 *** | −24.158 *** |

Elec-big | 0.0003 | 0.047 | 0.0015 | 0.1822 | −0.1893 | 4.2391 | −0.1793 | −25.130 *** | −25.129 *** |

Elec-small | 0.0014 | 0.051 | 0.0056 | 0.1535 | −0.2241 | 4.9526 | −0.4652 | −24.822 *** | −24.823 *** |

Food-big | 0.0006 | 0.046 | 0.0024 | 0.1414 | −0.2388 | 4.5246 | −0.1952 | −24.630 *** | −24.630 *** |

Food-small | 0.0009 | 0.049 | 0.0036 | 0.1931 | 0.2543 | 5.4559 | −0.4885 | −24.121 *** | −24.172 *** |

Engi-big | 0.0001 | 0.049 | 6.82E-05 | 0.1608 | −0.1982 | 4.2426 | −0.0678 | −24.940 *** | −24.947 *** |

Engi-small | 0.0012 | 0.051 | 0.0040 | 0.1498 | −0.2764 | 5.4045 | -0.5923 | −24.221 *** | −24.279 *** |

Meta-big | −0.0006 | 0.049 | −0.0024 | 0.1793 | −0.1796 | 4.4632 | −0.0823 | −15.329 *** | −24.292 *** |

Meta-small | 0.0010 | 0.051 | 0.0034 | 0.1380 | −0.2672 | 4.8873 | −0.4950 | −14.906 *** | −23.702 *** |

Phar-big | 0.0014 | 0.042 | 0.0040 | 0.1656 | −0.1710 | 4.7004 | −0.0728 | −15.413 *** | −25.626 *** |

Phar-small | 0.0017 | 0.049 | 0.0039 | 0.1497 | −0.2486 | 5.2476 | −0.5787 | −15.234 *** | −24.081 *** |

Industry Portfolio | ρ_{0}(j,B) | ρ_{0}(j,S) | ρ_{1}(j,B) | ρ_{1}(j,S) | ρ_{2}(j,B) | ρ_{2}(j,S) | ρ_{3}(j,B) | ρ_{3}(j,S) | ρ_{4}(j,B) | ρ_{4}(j,S) |
---|---|---|---|---|---|---|---|---|---|---|

Auto-big | 1 | 0.814 | −0.029 | −0.082 | 0.163 | 0.053 | 0.065 | −0.049 | −0.038 | −0.013 |

Auto-small | 0.913 | 1 | 0.083 | 0.034 | −0.045 | 0.074 | 0.057 | 0.060 | 0.017 | −0.049 |

Cons-big | 1 | 0.831 | −0.022 | −0.062 | 0.134 | 0.045 | 0.071 | −0.034 | −0.083 | 0.010 |

Cons-small | 0.974 | 1 | 0.086 | 0.016 | −0.044 | 0.069 | 0.041 | 0.054 | −0.011 | −0.089 |

Elec-big | 1 | 0.869 | −0.023 | −0.026 | 0.113 | −0.001 | −0.002 | −0.032 | −0.123 | −0.043 |

Elec-small | 0.988 | 1 | 0.032 | −0.015 | −0.002 | 0.093 | 0.040 | 0.034 | 0.045 | −0.051 |

Food-big | 1 | 0.876 | −0.003 | −0.037 | 0.095 | 0.009 | 0.061 | −0.006 | −0.103 | 0.010 |

Food-small | 0.977 | 1 | 0.035 | 0.022 | −0.002 | 0.075 | 0.021 | 0.067 | −0.135 | −0.096 |

Engi-big | 1 | 0.854 | −0.016 | −0.069 | 0.116 | 0.034 | 0.091 | −0.004 | −0.113 | −0.014 |

Engi-small | 0.911 | 1 | 0.047 | 0.003 | −0.025 | 0.090 | 0.008 | 0.050 | 0.023 | −0.063 |

Meta-big | 1 | 0.819 | 0.020 | −0.049 | 0.122 | 0.042 | 0.058 | −0.033 | −0.091 | −0.022 |

Meta-small | 0.863 | 1 | 0.064 | 0.037 | −0.014 | 0.129 | 0.027 | 0.055 | 0.022 | −0.084 |

Phar-big | 1 | 0.753 | −0.039 | −0.076 | 0.148 | 0.035 | 0.053 | −0.003 | −0.097 | −0.016 |

Phar-small | 0.991 | 1 | 0.083 | 0.018 | −0.029 | 0.116 | 0.003 | 0.020 | 0.005 | −0.058 |

_{m}(j,k), m = 0 to 4, j = B or S, and k = B or S, is correlation coefficient. B and S refer to the largest 30% size portfolio and the smallest 30% size portfolio, respectively. ρ

_{m}(j,k) refers to the m

^{th}order correlation coefficient between returns on the largest 30% size portfolio and the smallest 30% size portfolio. For example, ρ1(S, B) denotes the correlation between week t return on the smallest 30% size portfolio and week t−1 return on the largest 30% size portfolio. ρ2 (B, S) represents the correlation between week t return on the largest 30% size portfolio and week t−2 return on the smallest 30% size portfolio. On the other hand, ρ

_{m}(j,k) also displays autocorrelation of portfolios’ return. For instance, ρ1 (B, B) refers to the first-order autocorrelation of the largest 30% size portfolio. ρ4 (S, S) means the fourth-order autocorrelation of the smallest 30% size portfolio.

Industry | Conditional Vector Auto-Regression | |||||
---|---|---|---|---|---|---|

Small-up | Big-up | Small-down | Big-down | Cross-Equation Tests | ||

Panel A: The time-series Conditional VAR | ||||||

Automobiles parts | R_{S} | −0.111 (0.315) | 0.582 *** (7.218) | −0.614 *** (6.164) | 0.654 *** (6.273) | Up: 0.652 |

R_{B} | −0.410 ** (5.473) | 0.735 *** (13.527) | −0.666 *** (8.850) | 0.629 *** (7.048) | Down: 27.446 *** | |

Construction and materials | R_{S} | −0.376 (2.254) | 0.748 *** (6.986) | −0.728 ** (6.175) | 0.750 *** (7.013) | Up: 15.393 *** |

R_{B} | −0.364 (2.474) | 0.628 ** (5.710) | −0.693 ** (6.548) | 0.685 ** (5.822) | Down: 22.096 *** | |

Electronic equipment | R_{S} | 0.065 (0.059) | 0.189 (0.386) | −0.286 (0.651) | 0.305 (0.682) | Up: 1.126 |

R_{B} | −0.134 (0.289) | 0.240 (0.709) | −0.120 (0.131) | 0.043 (0.015) | Down: 1.324 | |

Food producers | R_{S} | −0.413 (2.455) | 0.616 ** (4.233) | −0.610** (5.165) | 0.774 *** (6.821) | Up: 11.302 *** |

R_{B} | −0.391 (2.570) | 0.498 * (3.226) | −0.615 ** (6.144) | 0.734 *** (7.168) | Down: 21.958 *** | |

Industrial engineering | R_{S} | 0.052 (0.069) | 0.236 (1.066) | −0.264 (0.876) | 0.269 (1.141) | Up: 2.415 |

R_{B} | −0.119 (0.397) | 0.379 * (2.983) | −0.241 (0.792) | 0.221 (0.690) | Down: 3.766* | |

Industrial metals and mining | R_{S} | 0.148 (0.545) | 0.214 * (2.810) | −0.257 (1.293) | 0.318 * (2.773) | Up: 1.668 |

R_{B} | −0.061 (−0.061) | 0.309 * (2.842) | −0.196 * (3.487) | 0.189 * (2.922) | Down: 4.374 ** | |

Pharmaceuticals and biotechnology | R_{S} | 0.405 * (3.574) | 0.198 * (2.746) | −0.404 (2.380) | 0.521 * (3.006) | Up: 1.661 |

R_{B} | 0.147 (0.661) | 0.072 (0.102) | −0.499 ** (5.108) | 0.500 ** (3.886) | Down: 11.532 *** | |

Panel B: The Panel Conditional VAR | ||||||

All sample industries | R_{S,i} | 0.028 (0.12) | 0.296 *** (10.03) | −0.389 *** (14.61) | 0.420 *** (15.35) | Up: 23.70 *** |

R_{B,i} | −0.159** (4.33) | 0.397 *** (20.66) | −0.359 *** (14.21) | 0.328 *** (10.69) | Down: 52.74 *** |

_{S}is the equal-weighted weekly return on the smallest 30% portfolio, while R

_{B}presents the equal-weighted weekly return on the largest 30% portfolio. R

_{S,i}(t) and R

_{B,i}(t) are the equal-weighted weekly return on the portfolio of the smallest and the largest 30% firms at period t in industry i, correspondingly. Small-up indicates the sum of coefficients of lagged small firms’ returns in up market. Small-down show the sum of coefficients of lagged small firms’ returns in down market. Small-up indicates the sum of coefficients of lagged small firms’ returns in up market. Small-down show the sum of coefficients of lagged small firms’ returns in down market. On the other hand, Big-up indicates the sum of coefficients of lagged big firms’ returns in up market, while Big-down denotes the sum of coefficients of lagged big firms’ returns in down market. F-statistics are reported in parentheses. Furthermore, in cross-equation tests, Up is the F-statistic for the null hypothesis in up market i.e., $\sum _{k=1}^{4}{b}_{K,\mathrm{up}}$ = $\sum _{k=1}^{4}{\mathrm{c}}_{K,\mathrm{up}}$. Down is F-statistic for the null hypothesis in down market i.e., $\sum _{k=1}^{4}{b}_{K,\mathrm{down}}$ = $\sum _{k=1}^{4}{\mathrm{c}}_{K,\mathrm{down}}$. Finally, ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Both AIC and HQIC information criterions support the four-lag to be adaptive order criteria. Thus, four-lag is used in the VAR model. (The result is similar with the view of Hou [4]. He claims that the lag order should be one or four because it is reasonable to assume that small firms will react to information within a month’s time.)

Industry | Conditional Vector Auto-Regression | |||||
---|---|---|---|---|---|---|

Small-up | Big-up | Small-down | Big-down | Cross-Equation Tests | ||

Panel A: The time-series Conditional VAR | ||||||

Automobiles parts | R_{S} | −0.043 (0.050) | 0.386 * (3.400) | −0.637 *** (7.482) | 0.815 *** (11.269) | Up: 11.996 *** |

R_{B} | −0.339 * (3.526) | 0.539 *** (7.498) | −0.698 *** (10.152) | 0.803 *** (12.342) | Down: 38.835 *** | |

Construction and materials | R_{S} | −0.246 (0.943) | 0.521 * (3.667) | −0.770 *** (7.426) | 0.919 *** (8.935) | Up: 7.906 *** |

R_{B} | −0.244 (1.080) | 0.430 * (2.902) | −0.753 *** (8.281) | 0.846 *** (8.826) | Down: 29.572 *** | |

Electronic equipment | R_{S} | 0.068 (0.067) | 0.069 (0.056) | −0.276 (0.577) | 0.398 (1.071) | Up: 0.320 |

R_{B} | −0.096 (0.156) | 0.109 (0.159) | −0.138 (0.165) | 0.140 (0.150) | Down: 1.943 | |

Food producers | R_{S} | −0.246 (0.943) | 0.521 * (3.667) | −0.767 *** (7.426) | 0.919 *** (8.935) | Up: 7.907 *** |

R_{B} | −0.244 (1.079) | 0.430 * (2.902) | −0.753 *** (8.281) | 0.846 *** (8.826) | Down: 29.901 *** | |

Industrial engineering | R_{S} | 0.225 (1.368) | 0.018 (0.008) | −0.581 ** (4.203) | 0.701 ** (5.839) | Up: 0.110 |

R_{B} | 0.049 (0.069) | 0.093 (0.229) | −0.529 * (3.739) | 0.613 ** (4.798) | Down: 12.902 *** | |

Industrial metals and mining | R_{S} | 0.264 (1.643) | 0.029 * (3.020) | −0.341 * (2.515) | 0.490 ** (4.300) | Up: 0.272 |

R_{B} | −0.078 (0.152) | 0.120 (0.360) | −0.299 (2.003) | 0.358 * (2.798) | Down: 11.146 *** | |

Pharmaceuticals and biotechnology | R_{S} | 0.400 * (3.565) | 0.217 ** (4.689) | −0.318 * (1.978) | 0.486 ** (4.393) | Up: 1.074 |

R_{B} | 0.054 (0.090) | 0.136 (0.380) | −0.297 (1.928) | 0.362 * (2.868) | Down: 7.246 *** | |

Panel B: The Panel Conditional VAR | ||||||

All sample industries | R_{S,i}(t) | 0.143 (0.145) | 0.097 (1.20) | −0.493 *** (24.63) | 0.626 *** (34.62) | Up: 2.52 |

R_{B,i}(t) | −0.044 ** (4.33) | 0.201 *** (20.66) | −0.465 *** (25.00) | 0.528 *** (28.07) | Down: 105.11 *** |

Industry | Three Sub-Pperiods | Cross-Equation Tests | ||||||||
---|---|---|---|---|---|---|---|---|---|---|

Small-P_{1} | Big-P_{1} | Small-P_{2} | Big-P_{2} | Small-P_{3} | Big-P_{3} | Test-P_{1} | Test-P_{2} | Test-P_{3} | ||

Panel A: The time-series Conditional VAR | ||||||||||

Automobiles parts | R_{S} | −0.468 (1.664) | 0.373 (0.767) | −0.241 (1.598) | 0.523** (6.565) | −0.377 (1.345) | 0.636* (3.582) | 1.710 | 29.071 *** | 10.754 *** |

R_{B} | −0.184 (0.293) | 0.250 (0.390) | −0.578 *** (10.473) | 0.755 *** (15.450) | −0.466 (2.344) | 0.519 * (2.712) | ||||

Construction and materials | R_{S} | −0.693 (1.653) | 0.575 (0.663) | −0.345 (1.771) | 0.563 ** (4.126) | −0.644 * (2.953) | 0.830 ** (5.142) | 2.035 | 12.580 *** | 16.689 *** |

R_{B} | −0.433 (0.753) | 0.295 (0.204) | −0.420 * (3.078) | 0.592** (5.335) | −0.665 * (3.673) | 0.673 ** (3.951) | ||||

Electronic equipment | R_{S} | 0.425 (0.817) | 0.717 (2.190) | −0.153 (0.344) | 0.371 (1.723) | −0.368 (0.373) | 0.477 (0.544) | 4.191 ** | 4.920 ** | 0.907 |

R_{B} | 0.275 (0.392) | −0.662 (2.131) | −0.256 (1.111) | 0.336 (1.616) | −0.139 (0.060) | 0.152 (0.063) | ||||

Food roducers | R_{S} | −0.480 (0.797) | 0.215 (0.180) | 0.030 (0.011) | 0.148 (0.237) | −0.004 (0.068) | 0.027 (0.003) | 2.535 | 0.051 | 0.007 |

R_{B} | −0.592 (1.364) | 0.383 (0.642) | 0.079 (0.090) | 0.030 (0.011) | 0.066 (0.026) | −0.017 (0.001) | ||||

Industrial engineering | R_{S} | −0.489 (0.820) | 0.261 (0.187) | 0.110 (0.355) | 0.062 (0.108) | −0.502 (1.594) | 0.690 * (2.897) | 2.028 | 0.193 | 8.073 * |

R_{B} | −0.597 (1.334) | 0.366 (0.403) | −0.021 (0.014) | 0.175 (0.927) | −0.462 (1.467) | 0.507 (1.699) | ||||

Industrial metals and mining | R_{S} | −0.397 (1.682) | 0.146 (0.203) | 0.144 (0.541) | 0.082 (0.159) | −0.358 (1.171) | 0.580 * (2.880) | 2.199 | 0.066 | 8.731 *** |

R_{B} | −0.335 (1.258) | 0.161 (0.258) | 0.029 (0.023) | 0.120 (0.354) | −0.430 (1.172) | 0.481 (2.077) | ||||

Pharmaceuticals and biotechnology | R_{S} | −0.500 (0.708) | 0.262 (0.141) | −0.023 (0.014) | 0.307 (1.605) | 0.404 (1.526) | 0.406 (1.188) | 0.911 | 5.175 ** | 2.577 |

R_{B} | −0.404 (0.640) | 0.252 (0.181) | −0.244 (2.158) | 0.455 ** (4.905) | 0.192 (0.478) | 0.245 (0.600) | ||||

Panel B: The Panel Conditional VAR | ||||||||||

All sample industries | R_{S,i}(t) | −0.353 ** (5.07) | 0.112 (0.41) | −0.048 (0.38) | 0.267 *** (9.64) | −0.262 * (3.62) | 0.421 *** (8.47) | 4.84 ** | 30.25 *** | 35.05 *** |

R_{B,i}(t) | −0.272 * (3.43) | 0.077 (0.23) | −0.206 *** (7.72) | 0.352 *** (19.12) | −0.303 ** (5.52) | 0.343 ** (6.45) |

_{S}and R

_{B}are the equal-weighted weekly return on the smallest and the largest 30% firms, correspondingly. R

_{S,i}(t) and R

_{B,i}(t) are the equal-weighted weekly return on the portfolio of the smallest and the largest 30% firms at period t in industry i, correspondingly. Small-P

_{1}and Big-P

_{1}respectively indicate the sum of coefficients of lagged small firms’ returns and the sum of coefficients of lagged big firms’ returns in the first sub-period. Similarly, Small-P

_{2}and Big-P

_{2}, respectively, refer to the sum of coefficients of lagged small firms’ returns and lagged big firms’ returns in the second sub-period. Small-P

_{3}and Big-P

_{3}respectively refer to the sum of coefficients of lagged small firms’ returns and lagged big firms’ returns in the third sub-period. F-statistics are reported in parentheses. Test-P

_{1}is F-statistics for cross-equation tests for the null hypothesis in the first sub-period i.e., $\sum _{\mathrm{k}=1}^{4}\mathrm{bk}$,

_{P1}= $\sum _{\mathrm{k}=1}^{4}\mathrm{ck}$,

_{P1}.

_{,}Test-P

_{2}and Test-P

_{3}are also corresponding F-statistics in the second sub-period and the third sub-period. Finally, ***, **, and * denote significance at the 1, 5, and 10 % levels, respectively. Both AIC and HQIC information criterions support the four-lag to be adaptive order criteria. Thus, four-lag is used in the VAR model.

Sub-period1: Jan 2002–Feb 2005 | |||

Cross-equation tests | |||

${\sum}_{\mathrm{K}=1}^{4}$R _{S,i}(t-k) | ${\sum}_{\mathrm{K}=1}^{4}$R _{B,i}(t-k) | $\sum _{\mathrm{k}=1}^{4}$${b}_{k}=\sum _{\mathrm{k}=1}^{4}{c}_{k}$ | |

R_{S,i}(t) | −0.454 *** (16.99) | 0.069 (0.33) | 11.86 *** |

R_{B,i}(t) | −0.347 *** (12.46) | 0.058 (0.29) | |

Sub-period2: Mar 2005–Jan 2010 | |||

Cross-equation tests | |||

${\sum}_{\mathrm{K}=1}^{4}$R _{S,i}(t-k) | ${\sum}_{\mathrm{K}=1}^{4}$R _{B,i}(t-k) | $\sum _{\mathrm{k}=1}^{4}$${b}_{k}=\sum _{\mathrm{k}=1}^{4}{c}_{k}$ | |

R_{S,i}(t) | −0.070 (0.47) | 0.277 ** (6.27) | 20.75 *** |

R_{B,i}(t) | −0.227 ** (5.71) | 0.359 *** (12.16) | |

Sub-period3: Feb 2010–Dec 2013 | |||

Cross-equation tests | |||

${\sum}_{\mathrm{K}=1}^{4}$R _{S,i}(t-k) | ${\sum}_{\mathrm{K}=1}^{4}$R _{B,i}(t-k) | $\sum _{\mathrm{k}=1}^{4}$${b}_{k}=\sum _{\mathrm{k}=1}^{4}{c}_{k}$ | |

R_{S,i}(t) | −0.282 *** (6.84) | 0.438 *** (15.01) | 43.02 *** |

R_{B,i}(t) | −0.304 *** (8.38) | 0.340 *** (9.55) |

_{S,i}(t) and R

_{B,i}(t) are the equal-weighted weekly return on the smallest and the largest 30% firms at period t in industry i, correspondingly. R

_{S,i}(t-k) and R

_{B,i}(t-k), respectively, are the equal-weighted weekly return on the smallest and the largest 30% firms at period t-k in industry i. Cross-equation test denotes F-statistic for the cross-equation null hypothesis: $\sum _{\mathrm{k}=1}^{4}\mathrm{bk}$ = $\sum _{\mathrm{k}=1}^{4}\mathrm{ck}$. Finally, ***, **, and * denote significance at the 1, 5, and 10 % levels, respectively. Both AIC and HQIC information criterions support the four-lag to be adaptive order criteria.

Sub-Period | Mean | Std | Comparison Sub-Period | Mean Difference |
---|---|---|---|---|

Sub-period 1 | −0.313 | 1.669 | Sub-period 2 | −0.711** |

Sub-period 3 | −1.009** | |||

Sub-period 2 | 0.398 | 1.562 | Sub-period 1 | 0.711** |

Sub-period 3 | −0.298 | |||

Sub-period 3 | 0.696 | 1.517 | Sub-period 1 | 1.009** |

Sub-period 2 | 0.298 | |||

F-test | 12.459 *** | 2.658* |

_{1}–c

_{1}) in the sub-period. Std refers to standard deviation of (b

_{1}–c

_{1}) in the sub-period. F-test refers to F-statistics for mean compare and variance compare among three sub-periods. ***, **, and * denote significance at the 1, 5, and 10% levels, respectively. Mean Difference denotes the difference of mean between sub-periods.

Variables | Mean | Std | Max | Min | F-Test | |
---|---|---|---|---|---|---|

Sub-period 1: Jan 2002–Feb 2005 | Mean | Variance | ||||

Market-return | −0.029 | 0.106 | 0.152 | −0.218 | 1.529 | 12.206 *** |

Market-capitalization | 41032 | 4867 | 50417 | 31590 | 41.792 *** | 72.619 *** |

Market-trading volume | 414 | 179 | 682 | 137 | 31.087 *** | 34.727 *** |

Proportion-institutional | 0.0056 | 8.64E-05 | 0.0057 | 0.0055 | 48.305 *** | 62.510 *** |

Proportion-individual | 0.9943 | 8.64E-05 | 0.9945 | 0.9943 | 48.305 *** | 62.510 *** |

Sub-period 2: Mar 2005–Jan 2010 | ||||||

Market-return | 0.056 | 0.221 | 0.425 | -0.416 | ||

Market-capitalization | 151824 | 87563 | 327140 | 32430 | ||

Market-trading volume | 2552 | 1334 | 4454 | 506 | ||

Proportion-institutional | 0.0052 | 0.0059 | 0.0059 | 0.0046 | ||

Proportion-individual | 0.9947 | 0.00042 | 0.9954 | 0.9941 | ||

Sub-period 3: Feb 2010–Dec 2013 | ||||||

Market-return | −0.026 | 0.101 | 0.101 | −0.260 | ||

Market-capitalization | 233433 | 22013 | 277662 | 195138 | ||

Market- trading volume | 3226 | 956 | 5015 | 1961 | ||

Proportion- institutional | 0.0046 | 4.58E-05 | 0.0047 | 0.0045 | ||

Proportion-individual | 0.9953 | 4.58E-05 | 0.9954 | 0.9953 |

_{1}–c

_{1}). F-test refers to F-statistics for mean compare and variance compare among three sub-periods. Finally, ***, **, and * refer to significance at the 1, 5, and 10% levels, respectively.

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Dong, C.; Lean, H.H.; Ahmad, Z.; Wong, W.-K.
The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China. *Sustainability* **2019**, *11*, 1037.
https://doi.org/10.3390/su11041037

**AMA Style**

Dong C, Lean HH, Ahmad Z, Wong W-K.
The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China. *Sustainability*. 2019; 11(4):1037.
https://doi.org/10.3390/su11041037

**Chicago/Turabian Style**

Dong, Chi, Hooi Hooi Lean, Zamri Ahmad, and Wing-Keung Wong.
2019. "The Impact of Market Condition and Policy Change on the Sustainability of Intra-Industry Information Diffusion in China" *Sustainability* 11, no. 4: 1037.
https://doi.org/10.3390/su11041037