Long-term analysis

CHAPTER 5 Regression analysis

The Chapter 5 selects the price, the daily number and value of the stocks traded in as the outcome variables. Through the DID model, we got the regressions in the long-term and short-term analysis.

5.1 Long-term analysis

Tables 5.1.1 and 5.1.2 explain the coefficient of regression in the long-term group. The following tables analyze the price, volume and value of the stocks.

Table 5.1.1 Long-term regression result of stock price

Note. The values in parentheses are t-value.

*** and ** indicate significant level under one percent and five percent, respectively.

Variables Clsprc

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

The regression results of the stock price indicators in long-term trading are listed in Table 5.1.1. There are four models in the long-term analysis. In the table, the treatment coefficient in table 5.1.1 can be found to have significant regression results and the coefficients are all positive, meaning that the stock price in the treatment group is higher than the control group after considering the impacts from other variables. Then, notice that the time-dummy coefficient is negative in the long-term group, meaning that the price index in the post-period will be lower than that in the pre-period, including the close price, open price, lowest price, and highest price.

The interaction results of the stock price index in the long-term group all have significant regression results. When the inter value is negative it means that, after the circuit breaker mechanism, the treatment group will decrease in price index compared to the control group. There has been a significant effect on the treatment group. After the circuit breaker mechanism is implemented, while controlling other factors, the policy indeed reduces the day trading stock price of the A-share constituents, including the opening price, the closing price, the highest price, and the low price. In addition, the log (total assets), leverage, profit, and earnings ratio are used as control variables.

In the long-term, a higher total assets and leverage will make the stock price fall; a higher rate of return and earnings ratio will make the stock price rise.

In conclusion, we can clearly find that the circuit breaker mechanism for CSI300 has indeed led to the day trading stock price index decline relative to the other A shares that have not implemented the policy. From the regression results, this mechanism may exacerbate the instability of the stock market. Referring to the research literature of Han Ao Shuang (2018), considering the empirical results that reveal the sell order was withdrawn, the implementation of the circuit breaker mechanism has aggravated the

‧

degree of unbalance of the place orders. Shuang also believes that the policy has increased the instability of the stock market.

Table 5.1.2 Long-term regression result of the daily stock market

Note. The values in parentheses are t- value.

*** and ** indicate significant level under one percent and five percent, respectively.

Table 5.1.2, illustrates the circulation status of the daily stock market, and we have four models in the long-term analysis. We also use the DID statistical regression method to perform the analysis and research. The treatment coefficient in Table 5.1.2 may be positive, which means that the coefficient of the daily stocks traded and their daily trade value in the treatment group is higher than the control group after considering impacts from other variables.

Variables Dnshrtrd

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

Moreover, one may notice that the time-dummy coefficient has a negative number in the long-term group. The posting period will decrease the daily number and value of stocks traded from the pre-period.

It is important to note that the different inter-coefficients of the explanatory variables have had different effects. In the long-term group, only the daily number and value of stocks traded have a significant effect when the circuit breaker mechanism is implemented. First, based on the data of model (Ltv1), when the inter-coefficient is negative it means that, after the policy is implemented, the treatment group will decrease the daily number of stocks traded compared to the control group. This significantly affects the treatment group. Second, in the data of model (Ltv2), the inter-coefficient is the same as the negative value. It also means that the treatment group will decrease the daily value of stocks traded after the policy when compared to the control group.

Furthermore, the log (total assets), leverage, profit, and earnings ratio are used as control variables. In the long-term, the higher leverage and earnings ratio will make the number of daily stocks traded and the daily trade value fall; the higher total assets and rate of return will make the number of daily stocks traded and the daily trade value rise.

In the long run, it has indeed reduced the daily number and value of stocks traded in CSI300 constituents by controlling other factors after implementing the policy. This means that the circuit breaker mechanism weakens the coefficient of trading shares and market liquidity during the day. The results of the long-term group showed that the liquidity index has indeed decreased and did not exacerbate the investors’ behavior of disregarding the cost while selling. Subsequent selection will include short-term data

‧ 國

立 政 治 大 學

‧

N a tio na

l C h engchi U ni ve rs it y

groups to further analyze the regression study of the daily circulation status under the circuit breaker mechanism.

The reference literature, Han Ao Shuang (2018), shows that "the fuse mechanism has aggravated the imbalance of the sell order, but at the same time, the rate of withdrawal is also significantly improved. This reflects the possibility that orders remain in market manipulation." Considering the fact that the order remains in control, Shuang continues to analyze the impact of the fuse mechanism on the sell order withdrawal ratio and to find that the circuit breaker mechanism does increase the withdrawal rate.

Then, according to the regression model in (Ltv1) and (Ltv2) in table 5.1.2, the daily number and value of stocks traded all fell after the implementation of the fuse mechanism. Compared to the withdrawal mentioned in the reference, we can reach the same conclusion. As the rate of withdrawal is increased, the number of shares traded in the market will be reduced. Furthermore, Shuang also indicated that the liquidity indicators improved through the regression analysis and believed that the behavior of the investor to disregard the cost was not aggravated.

‧

Tables 5.2.1 and 5.2.2 explain the coefficient of regression in the short-term group. The following tables analyze the price, volume and value of the stocks.

Table 5.2.1 Short-term regression result of stock price

Variables Clsprc

Note. The values in parentheses are t- value.

*** and ** indicate significant level under one percent and five percent, respectively.

Table 5.2.1 shows the regression results of the stock price indicators under the circuit breaker mechanism in short-term trading, and we obtain four models in the short-term analysis. The biggest difference is that the treatment coefficient in model (Stp1), model (Stp2), model (Stp3), and model (Stp4) all had no significance, indicating that the