Shenzhen Market Research Articles

Research on the Impact of Economic Policy Uncertainty on China's Stock Market Returns

The development of China's securities market has been relatively short, and the stock market has been developed from scratch, and the relevant market system and theory are not mature enough, gradually showing the characteristics of "policy market". In order to achieve different macroeconomic goals at different stages of economic development, the government constantly adjusts the direction and intensity of the implementation of macroeconomic policy tools in a timely manner. Frequent adjustments in economic policies are bound to affect the development of capital markets. Especially since 2015, the supply-side reform, the Sino-US trade war, the establishment of the science and technology innovation board, the new crown epidemic and other internal and external factors overlapping makes China's economic policy uncertainty index fluctuates sharply, which will certainly have an impact on the stock market. In this paper, monthly data from 2010 to 2022 are used as the study interval, and the SSE and SZSE indices are selected as the samples to study the relationship between them and the Shanghai and Shenzhen market returns using the economic policy uncertainty index developed by Baker et al. It is found that in the short run, economic policy uncertainty causes drastic negative feedback in the stock market; in the long run, the impact of economic policy uncertainty on the stock market gradually tends to level off after investors return to rationality. By market segment, economic policy uncertainty hit the small-cap segment more sharply, and equity market yields were more volatile.

What drive carbon price dynamics in China?

This paper examines the driving factors of China carbon price in a systemic way with quantitative analysis. Specifically, three categories of driving forces as macro economy risk and uncertainty, energy and environment factors are selected to investigate their impacts on carbon price in China, by adopting the dynamic connectedness measurement approach. The empirical results confirm each driving factor has made contributions to the carbon price fluctuations, and the dynamic interactions among them have enhanced connectedness of the whole system. However, the directional dynamic spillovers indicate main driving factors to carbon price are heterogeneous varying over the whole sample and in different carbon markets. Most importantly, market sentiment plays main role in the carbon price dynamics of Guangdong market, whereas the electric power index makes great effects to Hubei carbon price changes, and the carbon price fluctuations in Shenzhen market are largely caused by air quality situation.

Risk spillover from energy market uncertainties to the Chinese carbon market

A thorough understanding of the risk spillover from energy market uncertainties to the Chinese carbon market is significant for risk management and the construction of a unified national carbon market in China. Based on the generalized autoregressive score-driving model, this paper measures China's domestic energy market uncertainty using the conditional volatility of Daqing crude oil returns. Through merging copula approach and CoVaR methods, this paper reveals asymmetric risk spillovers from the international and China's domestic energy market uncertainties to Hubei and Shenzhen carbon pilots, which are the most representative carbon markets in China. Although both the international and China's domestic energy market uncertainties exert significant risk spillover effects on Chinese carbon pilots, the magnitudes of their effects are different. Comparing two carbon pilots, the Shenzhen market is more affected by energy market uncertainties than the Hubei market, thus revealing differences between carbon pilots. The findings in this paper provide meaningful information on investment portfolios and policies for constructing a unified national carbon market.

A Study on the Impact of R&D Investment on Firm’s Performance : Technological Regimes Perspective in Shanghai and Shenzhen Market Listed Companies

Recently the Chinese government has been vigorously implementing an innovation-driven development strategy，relaxing restrictions on financing conditions for enterprise research and development (R&D) innovation. China’s MOST,MOF, and STA jointly issued a document giving tax preferences to enterprises who comply with conditions for R&D expenditures. These policies cause enterprises to continually expand their research fields, add to their R&D investment strength. R&D investment directly impacts enterprises’ current year performance and subsequent years’ performance. This paper is based on technological regime perspective, and from the four dimensions of technological opportunities, innovation appropriability, technical progress accumulation as well as external knowledge acquisition. It used industrial enterprises above designated size listed companies that traded on an A-share on the Shanghai and Shenzhen stock exchanges as research samples to explore the impact of R&D investment on enterprise performance.. Our research found that there was a large difference between enterprises with different categories of technological regimes and the impact of R&D investment on enterprise performance. we provided different recommendations for enterprises of different categories with targeted activities for launching R&D, and also provided beneficial examples and references for theoretical spheres of industrial and commercial education, and for exploring the impact of R&D investment on enterprise performance from the four dimensions of technological regimes.

The Effects of Extreme Weather Conditions on Hong Kong and Shenzhen Stock Market Returns

We investigate the impact of extreme weather conditions on the stock market returns of the Hong Kong Stock Exchange and Shenzhen Exchange. For the weather conditions, we apply dummy variables generated by applying a moving average and moving standard deviation. Our study provides two interesting results. First, extreme weather conditions have a significant impact on the stock returns of the Shenzhen Exchange, indicating that the Shenzhen market is inefficient. Second, during the pre-QFII period, extreme weather conditions have a strong impact on the returns of the Shenzhen stock market, but the impact is significantly weaker in the period after QFII. This means that the efficiency of the Shenzhen stock market has significantly increased since the QFII program due to the market openness to foreign institutional investors. We emphasize the role of foreign investors not affected by local weather conditions by observing how market opening affects extreme weather impacts on stock market returns.

Spillover and Profitability of Intraday Herding on Cross-Listed Stocks

Companies are cross-listed on multiple exchanges in different countries to take advantage of different market features. Due to the difference in time zones, it is normally quite impossible to take advantage of instantaneous information spillover from market to market to generate abnormal returns. Situations can be different if the cross-listed firms are traded in markets within the same country and in the same time zone, but with different legislative regimes and levels of sophistication. Focusing on investors’ herd behavior and using hourly data, this article finds evidence of cross market information spillover in herding formation and abnormal returns in cross-listed stocks in China’s Shanghai, Shenzhen, and Hong Kong markets. More importantly, we find that investors can make excess returns upon observing herding by buying and holding Hong Kong’s small and median stocks in industrial sectors cross-listed in the Shenzhen market especially in the morning and the end of the trading day.

Does Shanghai-Hong Kong Stock Connect drive market comovement between Shanghai and Hong Kong: A new evidence

Using DCC, ADCC and GO-GARCH models, this paper examines whether the Shanghai-Hong Kong Stock Connect program drives market comovement between Shanghai and Hong Kong. We distinguish financial liberalization induced market comovement from that induced by other factors through comparing time-varying market correlations of Shanghai-Hong Kong with those of Shenzhen-Hong Kong. The results of the three variants of multivariate GARCH models consistently show that, if we ignore the period of market crash, the market correlation between Shanghai and Hong Kong does not significantly increase after the launch of the program. Furthermore, inconsistent with theoretical prediction, we find that the correlation between Hong Kong and financially non-liberalized Shenzhen market increases much more than that between Hong Kong and financially liberalized Shanghai market in the market turbulence. The results implicate the Shanghai-Hong Kong Stock Connect program is not the main fundamental force that drives market comovement between Shanghai and Hong Kong in the short run. This finding is further supported by the results of optimal hedge ratios and downside risk measures, which hold important risk management implications for investors in these markets.

Forecasting carbon prices in the Shenzhen market, China: The role of mixed-frequency factors

In this study, the hybrid of combination-mixed data sampling regression model and back propagation neural network (combination-MIDAS-BP) is proposed to perform real-time forecasting of weekly carbon prices in China's Shenzhen carbon market. In addition to daily energy, economy and weather conditions, environmental factor is introduced into predictive indicators. The empirical results show that the carbon price is more sensitive to coal, temperature and AQI (air quality index) than to other factors. It is also shown that the forecast accuracy of the proposed model is approximately 30% and 40% higher than that of combination-MIDAS models and benchmark models, respectively. Given these forecast results, China's government and enterprises can effectively manage nonlinear, nonstationary, and irregular carbon prices, providing a better investing and managing tool from behavioural economics.

Impact of Vaccine Events on Stocks in the Pharmaceutical Industry

What is the impact of the sensational vaccine incident in China on AC's stock and stocks throughout the market? By analyzing the stock data of the pharmaceutical industry, this paper finds that the incident has a great impact on the stock fluctuations of AC companies. The impact on different types of companies in different markets is different, and the impact on the Shenzhen market is greater than that of the Shanghai Stock Exchange. The least impact is on Large-cap, followed by Small-cap.

Stock return predictability and model instability: Evidence from mainland China and Hong Kong

This study examines the predictability of the Shanghai Composite, Shenzhen Composite and the Hang Seng China Enterprise index returns during the period 1993 to 2010, with emphasis on whether considering structural breaks in model parameters improves the stock return predictability.Results indicate higher linear stock return predictability for the Hong Kong market than for the Chinese markets. However, the results differ when model instability is considered. Specifically, using Bai and Perron’s (1998, 2003) approach, the results indicate the presence of structural breaks particularly for the Shenzhen market, which appear to coincide with major economic events or political and institutional changes. The predictable component in stock returns is also time-varying when re-estimating the model over different subsamples defined by the break. Overall, results highlight the importance of considering breaks in forecasting stock returns, and suggest that the Hong Kong market is a relatively ideal haven to park wealth for risk-averse investors whereas the Shenzhen market offers enhanced opportunities for risk-seeking investors.

Are Vietnam and Chinese stock markets out of the US contagion effect in extreme events?

This paper employs Chi-plots, Kendall (K)-plots and three different copula functions to empirically examine the tail dependence between the US stock market and stock markets in Vietnam and China in order to test contagion effects pre- and post- the US subprime mortgage crisis. The results based on data between 2003 and 2011 indicate the presence of left tail dependence before and after the crisis suggesting no change in dependence structure, but there exists stronger left tail dependence between the US and Vietnam stock markets. It is observed that the US and Vietnam stock markets are more prone to crashing than booming together. For the Chinese market, the US and Shanghai stock markets exhibit left tail dependence before the crisis, but no evidence of post-crisis tail dependency. On the contrary, the Shenzhen stock market is independent of the US market before and after the crisis which implies that an extreme event in the US market is less likely to influence the Shenzhen stock market. This suggests that there is significant potential for risk diversification by investing in the Shenzhen market by US investors after the financial crisis. These results have not been documented in the existing literature and provide a new insight into risk diversification between the two important Asian emerging stock markets.

Emission trading and carbon market performance in Shenzhen, China

China has developed its own domestic carbon markets by setting up emission trading schemes. This study addresses concerns about the functioning of these schemes and the financial performance of the Chinese carbon market. It aims to assess an actual outcome of this policy intervention, i.e. trading records, which were used in our analysis to examine a key financial property of the allowance-based market in Shenzhen. In a mature market, assets that incur higher risks are likely to yield higher returns, i.e. a positive relationship. To examine this property, we solicited historical data on the price and trading volume of emission allowances. We statistically estimated the degree of volatility in the Shenzhen market and its relationship with expected return premium. We found that the rate of return was negatively associated with expected risk. This stands at odds with the usual expectation in the financial market and the prediction of asset pricing theory. Also, kurtosis in trading volume was excessively high and its fluctuations were highly concentrated. We discuss these findings in terms of market liquidity and information uncertainties, and offer some policy recommendations. More regulatory attention and economic fixes are needed to improve market efficiency and eliminate sources of market distortions.

Stock Market Integration in China: Evidence from the Asymmetric DCC Model and Copula Approach

We investigate the dynamic dependence structure between the daily stock returns of the A and B shares of the Shanghai and Shenzhen stock markets in China, using time-varying conditional copula and asymmetric dynamic conditional correlation models. We find that the Shanghai market’s A and B shares are more integrated than those of the Shenzhen market. Further, the dynamic dependences between the shares for both markets are asymmetric and lower-tailed, and an increasing correlation with the opening up of the B shares market to Chinese citizens around 2001 is evident.

Geography and distance effect on financial dynamics in the Chinese stock market

Geography effect is investigated for the Chinese stock market including the Shanghai and Shenzhen stock markets, based on the daily data of individual stocks. The Shanghai city and the Guangdong province can be identified in the stock geographical sector. By investigating a geographical correlation on a geographical parameter, the stock location is found to have an impact on the financial dynamics, except for the financial crisis time of the Shenzhen market. Stock distance effect is further studied, with a crossover behavior observed for the stock distance distribution. The probability of the short distance is much greater than that of the long distance. The average stock correlation is found to weakly decay with the stock distance for the Shanghai stock market, but stays nearly stable for different stock distance for the Shenzhen stock market.

An application of the information-adjusted noise model to the Shenzhen stock market

Purpose – The purpose of this paper is to: first, test if information-adjusted noise model (IANM) can be applied in China; second, quantify noise trader risk, overreaction, underreaction and information pricing errors in that market; and third, explain the relationship between noise trader risk and return. Design/methodology/approach – The authors use a behavioural asset pricing model (BAPM), CAPM, the information-adjusted noise model and model proposed by Ramiah and Davidson (2010). Findings – The findings show that noise traders are active 99.7 per cent of the time on the Shenzhen A-share market. Furthermore, our results suggest that the Shenzhen market overreacts 41 per cent of the time, underreacts 18 per cent of the time and information pricing errors occur 40 per cent of the time. Originality/value – Various methods have been applied to the Chinese stock market in an effort to measure noise trading activities and all of them failed to account for information arrival. Our study uses a superior and alternative model to detect noise trader risk, overreaction and underreaction in China.

Stock returns in Chinese markets and lucky numbered days

Purpose– The purpose of this paper is to examine the stock return impact of “lucky” numbered days in markets dominated by Chinese participants. The existence of such patterns might present arbitrage opportunities for investors who do not share a belief in the Chinese system of “lucky” numbers.Design/methodology/approach– In univariate and multivariate analyses, the author examines the statistical significance of return differences between “lucky” numbered days and other days. The author examines samples which only consider single digit days and months, and the author also considers samples based on the last digit of the day or month. Based on the findings in these tests, the author designs and tests a trading strategy on the Shenzhen Exchange that produces significant risk-adjusted returns in excess of the buy-and-hold return on the Shenzhen Composite Index.Findings– The author shows that “lucky” numbered dates impact stock returns in Chinese markets and demonstrate a “lucky” number date trading strategy for the Shenzhen market that produces risk-adjusted returns in excess of the market return.Originality/value– Prior research on home address numbers and stock trading codes shows that, in markets dominated by Chinese participants, assets with identifiers containing numbers defined byFeng Shuias “lucky” sell at a premium and assets with identifiers containing “unlucky” numbers sell at a discount. In such markets, prices are more likely to end in a “lucky” number than an “unlucky” number. Chinese firms also tend to price their shares at IPO using “lucky” numbers and avoiding “unlucky” numbers. The author extends this literature to examine whether dates containing “lucky” and “unlucky” numbers experience stock returns significantly different than other days on Chinese stock exchanges.

Tail Contagion: Were Vietnam and China Stock Markets Out of the US Mortgage Crisis?

The paper applies non-parametric methods of Chi-plots and Kendall (K)-plots and three different copula functions to empirically examine the tail dependence between the U.S. stock market and stock markets in Vietnam and China in order to test contagion effect after the U.S. subprime mortgage crisis. The results indicate the presence of left tail dependence before and after the crisis suggesting no change in dependence structure but stronger left tail dependence between the U.S. and Vietnam stock markets. Thus, the US and Vietnam stock markets are more prone to crashing than booming together. Between the U.S. and Shanghai stock markets, the results provide evidence of a left tail dependence before the crisis, but no evidence of tail dependence after the crisis indicating that the dependence structure between U.S. and Shanghai stock markets changed after the crisis. On the contrary, the findings show that the Shenzhen stock market is independent of the U.S. market in both before and after crisis periods which imply that an extreme event in the U.S. market is less likely to influence the Shenzhen stock market. This suggests that there is significant potential for risk diversification by investing in the Shenzhen market by U. S. investors after the financial crisis. The results have not been documented in the existing literature and provide a new insight into risk diversification between the stock markets.

Lactobacillus shenzhenensis sp. nov., isolated from a fermented dairy beverage

Two Lactobacillus strains, designated LY-73(T) and LY-30B, were isolated from a dairy beverage, sold in Shenzhen market, China. The two isolates were Gram-positive, non-spore-forming, non-motile, facultatively anaerobic rods that were heterofermentative and did not exhibit catalase activity. Sequencing of the 16S rRNA, pheS and rpoA genes revealed that the two isolates shared 99.5, 99.8 and 99.9 % sequence similarity, which indicates that they belong to the same species. Phylogenetic analysis demonstrated clustering of the two isolates with the genus Lactobacillus. Strain LY-73(T) showed highest 16S rRNA gene sequence similarities with Lactobacillus harbinensis KACC 12409(T) (97.73%), Lactobacillus perolens DSM 12744(T) (96.96 %) and Lactobacillus selangorensis DSM 13344(T) (93.10 %). Comparative analyses of their rpoA and pheS gene sequences indicated that the novel strains were significantly different from other Lactobacillus species. Low DNA-DNA reassociation values (50.5 %) were obtained between strain LY-73(T) and its phylogenetically closest neighbours. The G+C contents of the DNA of the two novel isolates were 56.1 and 56.5 mol%. Straight-chain unsaturated fatty acids C18 : 1ω9c (78.85 and 74.29 %) were the dominant components, and the cell-wall peptidoglycan was of the l-Lys-d-Asp type. Based on phenotypic characteristics, and chemotaxonomic and genotypic data, the novel strains represent a novel species of the genus Lactobacillus, for which the name Lactobacillus shenzhenensis sp. nov. is proposed, with LY-73(T) ( = CCTCC M 2011481(T) = KACC 16878(T)) as the type strain.

An analysis of quasi-qualification, modified auditor opinions and special treatment status on China's Shenzhen market

The development of accounting, auditing and capital markets in China were an integral component of China's economic reforms; auditing was regarded as being critically important to achieving the desired policy objective of delivering a market economy (Yang and Yang, 1998). This article examines the quasi-qualification hypothesis and tests whether investors valued the introduction of Special Treatment (ST) status for firms in 1998. Our empirical analysis fails to find significant support for the quasi-qualification hypothesis. In contrast, it appears that the issuance of ST status was valued by investors; the issuance of ST status led investors to discriminate between firms.

Analysis of efficiency for Shenzhen stock market: Evidence from the source of multifractality

This paper extends the work in Wang, Liu and Gu (2009) [Analysis of efficiency for Shenzhen stock market based on multifractal detrended fluctuation analysis. International Review of Financial Analysis, 18, 271−276] by investigating the dynamics of two main sources of multifractality over time. Using multiscale detrended fluctuation analysis, we find that the medium-term and long-term auto-correlations of Shenzhen stock market became weaker and weaker over time but the degrees of short-term efficiency seemed to do not change. Evidence shows that the degree of the fat-tail distribution of the market returns abruptly decreased after the price-limited reform. After the reform, the degree of fat-tail distribution seemed to run an upward trend but it went down finally. From the contributions of two sources, we conclude that the multifractality generally displayed a downward trend indicating an upward trend of efficiency only in the medium- and long-term which further confirms the results in Wang, Liu and Gu (2009). We find that some abnormal “jumps” in the evolutions of short-term and medium-term correlation behaviors can be related to the abruptly exogenous events. Some problems about efficiency and operations of Shenzhen market are also discussed at last.