The impact of price limit system on the comprehensive quality of the stock market: Research on long-term and short-term effects based on submarkets

Abstract

We construct a difference-in-differences simultaneous equation to study the long-term impact of price limit system on the comprehensive quality of the stock market. Moreover, we use event study method to further test short-term effect. Results show that after the setting of price limit system in China, the quality of total market and the Shenzhen stock market improves to a certain extent. But for the Shanghai stock market, in the long term, the setting of price limit system can reduce liquidity and market efficiency, in the short term, it could cause trading interference effect and price discovery delay effect; nonetheless, it could stabilize volatility and suppress volatility spillover effect.

Keywords:

• price limit system
• comprehensive quality of stock market
• difference-in-differences simultaneous model
• event study method
• long-term and short-term effects

JEL classification:

• G10
• G14
• G18

Acknowledgements

All authors have contributed significantly and are in agreement with the content of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Statement

The content of this submission has not been published or submitted for publication elsewhere except as a brief abstract in the proceedings of a scientific meeting or symposium.

Notes

1. The Chinese mainland stock market, including Shanghai Stock Exchange and Shenzhen Stock Exchange, has relatively strict trading system. But Hong Kong Stock Exchange (in Hong Kong, China) is a stock market in line with international standards of trading systems. The target market of this article is the main-board market of Shanghai and Shenzhen, rather than GEM and New OTC Market, because the main board market is suitable for the majority of investors and there is no minimum capital restriction, but there are some restrictions in other markets, for example, the GEM requires that the average daily assets should not be less than 500,000 YUAN.

2. On 17 October 2014, the “Several Opinions on Reforming, Improving and Strictly Implementing the Delisting System of Listed Companies” was formally issued and achieved obvious results. On 21 December 2015, the “Decision on authorizing the State Council to adjust the application of relevant provisions of the Securities Law in the implementation of the reform of the registration system for stock issuance (draft)” was submitted to the 18th meeting of the Standing Committee of the 12th National People’s Congress for deliberation and approval.

3. The China’s stock market in this article refers to the mainland stock market, such as the total market (including Shanghai and Shenzhen stock markets), Shanghai market, and Shenzhen market.

4. The system stipulates that, except for the first day of listing, the trading price of stocks (including A-shares and B-shares) and fund securities within a trading day shall not rise or fall by more than 10% relative to the closing price of the previous trading day. Orders that exceed the price limit are considered invalid. On 13 June 2019, China’s Science and Technology Innovation Board officially opened. Although the trading system, including the price limit, has been reformed, it has become a 20% price limit. However, due to the short operating time, the overall market fluctuates greatly and does not show a stable system effect.

5. Two methods are DID model and event study. DID model is the main study based on the data of the full sample period, which can reflect of the long-term effects of the three market quality indicators, such as liquidity, volatility, and market efficiency. Event study is a further supplement based on window period data and can reflect the three corresponding short-term effects, such as trading interference effect, volatility spillover effect, and price discovery delay effect.

6. In order to avoid market turmoil interference to the data results, the market data for the first quarter of Shanghai and Shenzhen stock markets were excluded. In addition, on 4 December 2015, Shanghai Stock Exchange, Shenzhen Stock Exchange, and China Financial Futures Exchange officially issued relevant regulations on indicator circuit breakers. The standard circuit breaker index is CSI 300 index, with two thresholds of 5 and 7%. It was officially implemented on 1 January 2016, and suspended on 8 January 2016. The circuit breaker mechanism is a kind of price stabilization mechanism; in order to avoid its impact on the test results, the market data of the month after 1 January 2016 is excluded. Furthermore, to avoid the impact of the COVID-19 from the late of 2019 to 2021 on the data results, the data of the corresponding time period is not considered.

7. Referring to the existing research (Amihud, 2002; Barbara & Agata, 2019; Hasbrouck & Schwartz, 1998; Wang & Yau, 2000; Wu & Qin, 2015) and the measurement methods mentioned in the stock market quality report issued by Shanghai Stock Exchange, we set the index system from the three aspects of stock market quality to measure the stock market quality systematically and comprehensively. These three aspects are liquidity, volatility, and market efficiency.

8. The market efficiency indicator is very classic and difficult to replace, so we did not perform variable replacement.

9. The efficiency of the stock market includes the efficiency of information transmission, the speed of response to new information, and the efficiency of pricing. Pricing efficiency, also known as information efficiency, refers to the ability of securities prices to reflect information, or the speed and accuracy with which prices reflect all relevant information.

10. The event study method is detailed in the introduction of the further test.

11. The CSI 300 index components and compilation method are used to extrapolate the full sample period, representing the total market data.

12. Some scholars studied the relationship between trading volume, bid–ask spread, and price volatility of four types of futures, and the results show a positive correlation between trading volume and price volatility (Wang and Yau, 2000). Barbara and Agata (2018) found a two-way causal relationship between liquidity and volatility (Barbara & Agata, 2019). The liquidity–volatility causal relationship is common and is often asymmetric.

13. The sample data passes the endogeneity test, and then we use the two-stage least-squares method to regress. After using the difference-in-difference model, the control variables of other factors including financial crisis, securities margin trading, and stamp duty are not significant or significantly decrease, indicating that the interference from other events and factors can be eliminated.

14. In the long-term test part, we use dual indicators to establish liquidity and volatility, thus can verify the robustness of the results.

15. ADF stationarity test results show that the p-value of the variables is 0, which means the data are all stable.

16. The pre inspection results of sub markets are the same, like ADF stationarity test and the Hausman test. ADF stationarity test results show that the p-value of the variables is 0, which means the data are all stable. Limited to space, the pre inspection contents in the following are omitted, and the descriptive statistical results can be seen in Appendix.

17. First of all, the products of the Shanghai and Shenzhen stock market are different. Shanghai stock market only has the main board, while Shenzhen stock market not only has the main board, but also SME board and GEM board; Secondly, the two markets are different in the application of units, Shanghai stock market requires the minimum application of 1,000 shares, while Shenzhen stock market is 500 shares; Finally, there are differences in the types of investors in both markets. So the performance of the Shanghai stock market is different.

18. The study finds that the performance of the total market is similar to that of the Shenzhen stock market. Therefore, the test results based on the event study method only show the analysis of the Shanghai and Shenzhen stock markets; analysis will not be repeated for the total market.

19. The long-term effect results of the difference-in-differences simultaneous model are supplemented and explore the reasons for short-term effects.

20. In the short-term test part, we also replace the index to test robustness, but limited to space, the results are omitted.

21. Take the TR_it indicator as an example. The conclusions of other liquidity indicators are the same, and will not be repeated one by one. The same below.

22. $Stoc{k}_{hit}$ group, $Stoc{k}_{0.9}$ group, and $Stoc{k}_{0.8}$ group refer to stock portfolios whose prices touch the 10%, 9%, and 8% price limit, respectively.

23. Take the Garch_it indicator as an example. The conclusions of other volatility indicators are the same.

Additional information

Funding

We appreciate the financial support of National Social Science Foundation of China (17CJY060), the Fundamental Research Funds for the Central Universities (DUT21RW109), Liaoning Economic and social development research project (2022lslqnwzzkt-002), the key project of National Natural Science Foundation of China (71731003), and the major project of National Social Science Foundation of China (19ZDA094).

Notes on contributors

Zhuwei Li

Zhuwei Li (Corresponding Author), Ph.D of finance engineering, associate professor and master tutor of finance engineering, who is from School of Economics and Management, Dalian University of Technology, Dalian 116024, China.

Xiaoshan Wang, graduate student of finance engineering, who is from School of Economics and Management, Dalian University of Technology, Dalian 116024, China.

Chenyang Kang, graduate student of finance, who is from School of Economics and Management, Dalian University of Technology, Dalian 116024, China; and who is from Shanghai Heyi Financial Information Service Co., Ltd, Shanghai 200003, China.