Time varying price discovery of the New Third Board market in China: does the market-making system help?

ABSTRACT

This paper examines whether the market-making system helps to improve the price discovery ability of New Third Board (NTB) market in China. We first estimate the time-varying coefficients error correction models, then apply common factor weight method to quantify the time-varying price discovery contributions, and finally explore the impacts of trading volume and volatility to price discovery contributions. Empirical results show that both markets have time-varying characteristic in terms of the magnitudes and directions of the equilibrium price adjustment due to error correction term. The Shanghai Composite Index, SZSE Component Index, and SME Index are found to lead in price discovery, while NTB exhibits the leadership on the GEM Index. Volume and volatility have significant influence on the price discovery contribution. The NTB contribution is positively related to its own trading activity, negatively related to the trading activity of Shanghai and Shenzhen stock markets, while negatively correlated with the volatility of both markets. In comparison, trading activity of SZSE Component Index and volatility of GEM Index have the greatest negative impacts on the contribution of NTB market. As an important part of China’s multi-level capital market, the pricing mechanism of the NTB market needs further to be improved.

KEYWORDS:

• New Third Board
• Shanghai and Shenzhen stock markets
• time-varying coefficients error correction model
• price discovery contribution
• market-making system

JEL CLASSIFICATION:

• G14
• G10

Disclosure statement

No potential conflict of interest was reported by the authors.

Notes

¹ According to the 2017 Market Statistics Express of NEEQ, the total trading volume of NTB was only 8.14, then becomes 130.36, 1910.62, 1912.29, and 2271.80 from year 2014 to 2017, respectively. In comparison, the total trading volume of the Shanghai Main Board was 507214.81, the total trading volume of SZSE Main Board, the GEM Board and the SME Board were 19103.17, 25987.98, and 16552.16, respectively ((measured in billion). The trading value of market-making firms accounts for 71.85% and 44/36% for year 2015 and 2016.

² In Figure 4, we can observe the GEM Index has the lowest volume, even lower than TBMI at most of the time. The volatility of the GEM Index and TBMI are lower during the whole sample period.

³ VECM parameters can be estimated by either maximum likelihood (MLE) or ordinary least squares(OLS), which are equivalent asymptotically (Liu and Qiao 2017).

⁴ Note that VECM model is sensitive to the choice of sample periods and should not be employed blindly (Liu and Qiao 2017).

Additional information

Funding

This work was supported by The MOE (Ministry of Education in China) Project of Humanities and Social Sciences [17YJC790119, 17XJCZH002, 17XJC790008]; Sichuan Social Science Planning Project [SC17C060]; The Fundamental Research Funds for the Central Universities [2682018WXTD05, 2682018ZT25]; The National Natural Science Foundation of China grant [11671328, 71701171, 11501470].