Shanghai Market Research Articles

Heterogeneity of regional carbon emission markets in China: Evidence from multidimensional determinants

The Chinese carbon markets are heterogeneously distributed with different regional economies and natural resource endowments. This paper examines how carbon prices behave across regional emission markets in China using the dynamic model averaging (DMA) approach. The results show that the Beijing, Guangdong, and Hubei markets are increasingly affected by various factors. In contrast, the Shanghai market gradually becomes less affected after 2020, both in the current and forecast periods. Moreover, geopolitical risk and new energy price are important variables for the concurrent relation, and domestic oil price and geopolitical risk are important variables for predicting carbon prices. Nevertheless, there are significant differences in intensity and signs among the four carbon markets affected by various factors, as well as large differences between the results for the current and forecast periods. These results indicate the heterogeneity of regional carbon markets; therefore, caution should be taken when using a single carbon market to represent the entire Chinese market.

An Analysis of the Influence of ESG Rating Divergence on Investor Sentiment

The global financial markets have experienced a significant development with the emergence of the notions of "sustainable development" and "green investing". This development involves the integration of Environmental, Social, and Governance (ESG) information into investment decisions. A significant advancement in the worldwide financial market is the integration of ESG (Environmental, Social, and Governance) data into investing choices. ESG ratings have emerged as a significant benchmark for investors in this scenario. However, there is now a considerable disparity in the rating outcomes provided by various rating agencies for particular companies. This disparity distorts the ratings and thus triggers exaggerated reactions from investors. This study investigates the influence of differences in ESG ratings on investor sentiment and its underlying mechanism using a sample of businesses listed on the A-share market in Shanghai and Shenzhen between 2015 and 2022. Research has shown that when there is a significant difference in ESG ratings, it has a detrimental effect on investor sentiment. This means that when there is a large divergence in ESG ratings, it negatively affects investor sentiment, leading to a decrease in their evaluation and confidence in the company. Empirical investigations demonstrate that the focus and consideration given by analysts and research reports play a crucial role in how differences in ESG ratings impact investor sentiment. Additional investigation reveals that the adoption of GRI standards and the verification of ESG reports by third-party entities can somewhat reduce the influence of divergence on the fluctuation of investor mood, as well as enhance investor acknowledgment of revealed information. The paper examines the influence of disagreement in ESG ratings on investor mood, contributes to existing research on ESG ratings dispute, and presents empirical evidence to support the development of China's ESG ratings system and the establishment of a "rational" investment market.

Chinese stock market integration with developed world: A portfolio diversification analysis

This study investigates integration dynamics between the Chinese stock market and major developed counterparts—Australia, Germany, Japan, the UK, and the US—focusing on portfolio diversification. Using a comprehensive analytical approach from 2012 to 2022, encompassing events like the Belt and Road Initiative, the Shanghai market crash, US-China trade tensions, and the COVID-19 pandemic, the research employs descriptive statistics, unit root tests, cointegration analysis, and VECM-based Granger Causality Tests. Findings indicate modest integration, endorsing diversified portfolios for developed country investors due to higher returns in China with acceptable risk. Unit root analysis confirms cointegration with developed indices, indicating relatively low integration. Granger Causality Tests reveal bidirectional causality, emphasizing mutual influence. Notably, no causal link exists between the US and China, possibly due to regulatory disparities and the trade war. The study enhances understanding of Chinese stock market dynamics, supporting global economic intertwining and urging further openness of China's domestic shares for economic growth.

The Role of English in China’s Matchmaking Market

Research has focused on how English proficiency influences Chinese people’s education and occupational opportunities and choices. However, little is known about how it impacts the matchmaking practice in such context. Drawing on Bourdieu’s theory of capital, this study examines the role of English in the biggest matchmaking market in Shanghai, China. Field study was conducted in People’s park in Shanghai. Information regarding marriage candidates’ English competence as presented on matrimonial posters was collected and subjected to further grounded-theory based thematic analysis. The results reveal that English competence is primarily advertised as a form of institutionalized cultural capital (e.g., the possession of particular jobs, certificates, degrees, or citizenship) to boost candidates’ socioeconomic status, and used as a gatekeeper to weed out “disqualified” candidates, meaning that non-English speaking candidates may be rejected as ineligible for matchmaking opportunities. Issues related to the commodification of English competence in the matchmaking context are discussed at the end of the paper.

Ligilactobacillus cholophilus sp. nov., isolated from pickled potherb mustard (Brassica juncea Coss.).

Strain BD7642T was isolated from Chinese pickled potherb mustard (Brassica juncea Coss.) purchased from a local market in Shanghai, PR China. A polyphasic approach, including 16S rRNA gene sequence, housekeeping gene, average nucleotide identity (ANI), digital DNA-DNA hybridization (dDDH), G+C content and phenotypic analyses, was employed to characterize strain BD7642T. Cells of the bacterium were short round rods, Gram-stain-positive, non-spore-forming and catalase-negative. The strain grew at 30-45 °C and pH 4.0-8.0. Optimum growth occurred at 35-40 °C and pH 6.0-7.0. The strain exhibited growth with salt (NaCl) concentrations of up to 5 % (w/v). The G+C content of the strain's genomic DNA was 31.37 mol%. The major fatty acids were C16 : 0, C18 : 1 c9 and summed feature 10 (C18 : 1 c11/t9/t6). 16S rRNA gene sequencing revealed that strain BD7642T represents a member of the genus Ligilactobacillus and it had high sequence similarity to Ligilactobacillus aviarius NBRC 102162T (96.73 %), Ligilactobacillus araffinosus LGM 23560 (96.66 %) and Ligilactobacillus salivarius JCM 1231T (95.82 %). The dDDH values between strain BD7642T and its phylogenetically related species within the genus Ligilactobacillus ranged from 12.6 to 25.4 %. The ANI values between strain BD7642T and its closely related taxa were far lower than the threshold (95 %-96 %) used for species differentiation. Results of phylogenetic, physiological and phenotypic characterization confirmed that strain BD7642T represents a novel species within the genus Ligilactobacillus, for which the name Ligilactobacillus cholophilus sp. nov. is proposed. The type strain is BD7642T (=CCTCC AB 2022398T=JCM 36074T).

How Does the COVID-19 Pandemic Affect Housing Market? Evidence from Shanghai, China

We examine the effect of the COVID-19 pandemic on the housing market in Shanghai, China. We employ a difference-in-differences hedonic approach and find that the COVID-19 pandemic significantly affects housing prices. Housing prices in infected communities decrease by 0.7% after the pandemic. The price effects are more pronounced for housing with smaller size, lower value and in an urban location. We further study the mechanisms and find that the COVID-19 pandemic lowers housing listing prices, extends the time-on-the-market and changes households’ preferences for housing and migration behavior. Our study suggests that the pandemic has contagion and distributional effects on the housing market. The pandemic increases the gap of housing wealth between homeowners in low-valued and high-valued housing.

The Relationship between Real Estate Bubble and Financial Crisis Taking Shanghai as an Example

In recent years, the real estate market has become increasingly hot, developing very quickly, and has become one of the important industries to promote the development of GDP in China. However, the rapid development of many negative effects, such as high prices and rising costs of living, has caused the real estate market to bubble, causing people to worry about whether it will cause a financial crisis, resulting in an economic crash. As a result, many scholars have conducted various studies on the real estate bubble, but they have not reached a consensus on the bubble. Economic crashes caused by real estate bubbles have occurred on occasion throughout history, and many financial crises are triggered by excessive real estate bubbles. This paper visually analyzes the development of the real estate market in Shanghai in recent years using a literature review method, analyzes the transmission relationship between the real estate bubble and the financial crisis, expounds on the harm caused by the bursting of the real estate bubble, and proposes corresponding countermeasures for the benign development of the real estate market.

Corporate Social Responsibility and Enterprise Performance: The Chain Mediating Effect Test Based on Corporate Reputation and Brand Value

Corporate social responsibility (hereinafter referred to as “CSR”) sometimes does not directly affect enterprise performance but through some mediator variable. Corporate reputation and brand value have chain-mediating effects in the influence mechanism of CSR on enterprise performance. Theoretically, this paper broadens the scope of study on CSR and enterprise performance, deeply explores the internal mechanism between them, and opens the “black box” in the process of transforming CSR into enterprise performance. Practically, it can encourage Chinese enterprises to actively undertake CSR, strengthen reputation management, enhance brand value, and finally promote the sustainable development of enterprises. A-share listed companies (listed companies in the Chinese A stock market) in Shanghai and Shenzhen stock exchanges from 2008 to 2020 in “China’s 500 Most Valuable Brands List” by World Brand Lab are studied in this paper. The results of the empirical study based on relevant data show a significant positive relationship between CSR and enterprise performance. It investigates the mediating chain effects of corporate reputation and brand value in the influence of CSR on enterprise performance. Listed companies must disclose the annual CSR report. CSR reports as an effective supplement to corporate financial information, which provides an important communication channel for the enterprise and stakeholders. Stakeholders can timely understand their rights and interests through the CSR information and have a more comprehensive and deeper understanding of enterprises. In turn, the enterprise can also win a good reputation, establish a good brand image, and get sustainable development.

JUE Insight: The impact of the school admission restriction policy on the housing market in Shanghai

This study explores the influence of a regulation of informal school choice on the housing market in Shanghai to estimate the significance of loopholes in the school district system. The policy limited the priority for enrollment in a public elementary schools given to house owners in the school's district so that each housing property is given only one priority every five years; hence, informal acquisition of priorities through frequent resale of houses is limited. From the dataset of individual housing prices in Shanghai, we show that house price within the school districts of good schools increases after the policy. In contrast, house price decreases in areas that are neighboring the school but outside its school district because they are called “babysitting communities” and provide houses to live in for households who resell their houses earlier than graduation of their children. We also implement event study analysis to estimate time-varying influences of the policy, to show that the significant price increase occurs immediately after the policy implementation and shrinks a little in the short run.

Research on the Pertinence Between the Identity Characteristics of Independent Directors and Enterprise Innovation

The independent director system has multi aspect influence on enterprises.For public corporations, the connection between the independent director system and the capacity fro innovation of businesses is on of them. Take the state-owned enterprise in Chinese A stock market in Shanghai and Shenzhen from China between 2016 and 2021 as a research sample. This paper uses the basic econometric model to find the correlation between three factors include age, education background and whether independent directors are multiple identities and enterprise innovation through studying enterprise input and output and the number of patents. According to the study, an enterprise's capacity for innovation is favorably correlated with the age of its independent directors. Additionally, the educational experience of independent administrators has a favourable impact on the innovative capacity of an organization. The ability of businesses to innovate is favorably correlated with the identities of several independent directors.

Assessing the structure dependence between the Spanish stock market and some international financial markets. A time-varying copula analysis.

In this study we use time-varying copula analysis to investigate the dependence between the Spanish stock market, represented by the IBEX35 index, and some international stocks and commodities markets. The results indicate that: first, the European stock markets offer limited diversification possibilities. Second, American markets offer higher diversification possibilities than the European markets but the diversification may not work in an extreme market condition; here we find strong evidence of contagion effect. Third, the Asian markets outperform to the American markets offering higher diversifications possibilities even in extreme market conditions. Fourth, the assets negotiated in the Shanghai market may be considered hedge assets instead of diversifier assets; this feature is shared by the Bitcoin and Gold although the role of this last asset is highly volatile. These results provide useful information for those who seek to actively diversify their international portfolios and to manage their worldwide assets. Finally, we observe that degree of dependence derived from the correlation analysis is notably higher than the suggested by the copula analysis; this may be due the fact that correlation coefficient does not consider conditional heteroscedasticity so that correlations will be biased upwards.

Determination of 17α-methyltestosterone in aquatic products using high-performance liquid chromatography-tandem mass spectrometry

17α-methyltestosterone (MT) is widely used in aquaculture in order to increase harvest yield. However, illegal addition of MT is potentially toxic to human. A simple and rapid method for the determination of MT in aquatic products was established on HPLC-MS/MS. Sample preparation was optimized using simple liquid–liquid extraction techniques with ethyl acetate and n-hexane. The analyte was separated on Capcell PAK C18 MG II (100 mm × 2.0 mm, 5 µm), with a solution of 0.1 % formic acid in water and acetonitrile, and analyzed with multiple-reaction monitoring (MRM) mode on HPLC-MS/MS. Good linearity with high correlation coefficient (R2 ≥ 0.9997) was obtained between 2 and 200 µg/L. The limits of detection (LODs) and quantitation (LOQs) were 0.4 and 1 µg/kg respectively. Good recoveries were demonstrated between 86.4 and 104.6 %. The precisions of inter-day and intra-day were validated and the results indicated acceptable stability and reproducibility. The analyte showed stable up to 30 days storage at −20 °C. Samples purchased from Shanghai market for each matrix (shrimp, Chinese mitten crab, tilapia, eel, bullfrog and mussel) were tested for method application. In summary, the proposed method was validated for MT determination in aquatic products.

PCDD/Fs and DL-PCBs in Chinese Mitten Crab (Eriocheir sinensis) and Its Farming Environment in Shanghai, China.

Most polychlorinated dibenzo-p-dioxins and dibenzofurans (PCDD/Fs) and dioxin-like polychlorinated biphenyls (DL-PCBs) in the human body are acquired from dietary intake. The chronic exposure of humans to PCDD/Fs and DL-PCBs is a major health concern, and these compounds are strictly controlled in many areas. This study measured the levels of PCDD/Fs and DL-PCBs in Chinese mitten crab (Eriocheir sinensis) farms in Shanghai and determined potential sources. The mean concentrations of PCDD/Fs and DL-PCBs in the studied crab samples were 264.20 ± 260.14 and 506.25 ± 226.80 pg/g ww (wet weight), respectively. The range of the toxic equivalent (TEQ) for the total PCDD/Fs and DL-PCBs in the crab samples was 1.20–29.04 pg TEQ/g ww. Further analysis revealed that the TEQ input to crabs in aquacultural water was 1.6 times higher than the TEQ in edible crab parts. Aquatic plants, shore plants, and feed contributed about 0.05% of the total TEQ input to crabs. The TEQ contribution from sediment was 317 times that found in edible crab parts, and sediment may be the most prevalent source of PCDD/Fs and DL-PCBs in farm crabs. The evaluation of the Shanghai market crab revealed different levels of PCDD/Fs and DL-PCBs. The TEQs for the mean PCDD/F and DL-PCB levels were 1.55 ± 1.96 and 1.05 ± 0.55 pg TEQ/g ww, respectively. The tolerable daily intake (TDI) levels of adults and children were lower than the prescribed range (1–4 pg TEQ/kg (weight)·d), indicating no significant chronic or acute ingestion risk for adults and children.

Can energy predict the regional prices of carbon emission allowances in China?

The carbon emission trading is an important market-oriented tool in the process of China's carbon neutrality, which makes companies face tremendous pressure to reduce emissions while having strong energy demands. In order to evaluate whether energy prices can be robust predictors of the prices of emission allowances, this study perform extreme bounds analysis (EBA) in four representative markets. The empirical results reveal that energy prices can indeed predict the prices of emission allowances, but the robustly predictive capabilities of different energy prices vary with regions. Among them, thermal coal is the robustly positive predictor for Guangdong, Hubei and Shanghai market; natural gas is the robustly negative predictor for all the four chosen regions; and crude oil can only positively predict Hubei market with robustness. Meanwhile, the horizons that predictions from energy to emission allowance can be performed as well as the predictive coefficients also vary with energy types and regions. And some trading implications are also provided alongside.

The influence of the Shanghai crude oil futures on the global and domestic oil markets

The newly established Shanghai crude oil futures market provides a natural experimental setting for studying the price cointegration and causal relationships between the Chinese domestic benchmark (Daqing) and international benchmarks (West Texas Intermediate and Brent). We find a significant change in the relationships between domestic and global oil prices before and after the launch of the Shanghai market. Moreover, using a Johansen cointegration test and a vector error correction model, we find long-term equilibrium and short-term relationships between the Shanghai market and global benchmarks. Shanghai crude oil futures prices also play a leading role in the price discovery of domestic crude oil spot prices, reflecting their dominant influence on the domestic market. The prices of the domestic crude oil market are thus becoming less influenced by global benchmarks but more influenced by the Shanghai crude oil futures market. The examined results of dynamic ARDL simulations model support the long run and short run relations between the Shanghai market and global benchmarks. The results also shows that the Shanghai crude oil futures market has response lags relative to those benchmarks.

Hybrid intelligent framework for carbon price prediction using improved variational mode decomposition and optimal extreme learning machine

As the climate problem continues to worsen, carbon trading markets for energy conservation and emission reduction have been established in many countries. Accurate forecasting of carbon trading prices is not only a realistic problem, but also brings huge challenges to relevant researches. In this study, a novel predicting model is proposed to predict carbon price. And this model combines the advantages of the improved variational mode decomposition (IVMD) algorithm, multiscale entropy (MSE) algorithm, and the extreme learning machine (ELM) model improved by the intelligent optimization algorithm. Firstly, center frequency (CF) and mutual information (MI) entropy are utilized to jointly determine the number of decomposition layers of the variational mode decomposition (VMD), and avoid the problem of excessive decomposition. Subsequently, the complexity of each intrinsic mode function (IMF) from the improved variational mode decomposition is calculated by multiscale entropy, and intrinsic mode functions are recombined to reduce the complexity of subsequent modeling. At the last, the extreme learning machine optimized by the sparrow search algorithm (SSA) is adopted to model and predict the different sequence combinations. The performance indicators of the proposed model are significantly lower than others. For example, the root mean square error (RMSE) of the proposed model is 0.6653 in Hubei market, 0.9719 in Guangdong market and 1.2819 in Shanghai market. Additionally, the optimized extreme learning machine model is more suitable for the prediction of time series, which also provides an effective forecasting tool for related researchers.

Graph Convolutional Network-Based Model for Megacity Real Estate Valuation

It is challenging to make precise assessments of real estate prices due to its elevated individual prices, complicated influencing factors, and ambiguous attribute selection. As a result of the high demand for owner-occupied and investment properties, real estate is also a substantial concern for society. How to accurately evaluate its price has been a hot issue for research by major institutions. Real-world applications of real estate valuation impose stringent requirements on the acquisition of datasets and the generalizability of models. On the basis of SRGCNN, a spatial regression model with excellent generalizability, this paper introduces an external attention mechanism to construct the A-SRGCNN model and compares it to the benchmark model utilizing data from Shanghai, Melbourne, and San Diego. For spatial regression, A-SRGCNN employs graph convolutional neural networks, and the external attention mechanism implicitly considers the relationship between property data. Experiments indicate that the A-SRGCNN model outperforms the benchmark model and has improved real estate price estimation accuracy. In the meantime, this paper employs the A-SRGCNN model to conduct zonal experiments and time-division experiments on the secondary real estate market in Shanghai to analyze the real estate price linkages between different zones and the real estate price linkages at different times. It is revealed that Shanghai real estate prices exhibit spatial aggregation and price aggregation, with comparable prices within the same zones, and that the A-SRGCNN model is effective at predicting house prices.

Detrended multiple moving average cross-correlation analysis and its application in the correlation measurement of stock market in Shanghai, Shenzhen, and Hong Kong

Based on the traditional detrended moving average cross-correlation analysis (DMCA) method and the detrended multiple cross-correlation analysis (DMC) method, we propose the detrended multiple moving average cross-correlation analysis method (DMMC) to quantify the correlation between non-stationary signals on different time scales. It will be the generalization of the correlation between pairwise time series and can be used to measure the multivariate correlation between the dependent variable y with the others i independent variable x. To validate the DMMC method, we determined the DMMC coefficient into numerical simulations on the standard Gaussian distribution, Lognormal distribution t distribution and ARFIMA long memory random sequences. Then we apply the proposed method to explore the multiple correlations between China’s Shanghai, Shenzhen, and Hong Kong stock markets and analyze the dynamic time-varying characteristics of the correlation with the sliding window approach. The results show that DMMC is suitable to measure multiple cross-correlations. This new method can be further applied to multiple cross-correlation in different areas of knowledge, such as energy, meteorology, medicine, transportation, etc.

A multi-plug filtration (m-PFC) cleanup method based on carboxylic multi-walled carbon nanotubes for the detection of 14 perfluorinated compounds and dietary risk assessment of chicken, beef, and mutton collected from Shanghai markets

Dietary intake is a major pathway of human exposure to perfluorinated compounds (PFCs). Livestock and poultry meats (chicken, beef, mutton) account for a significant proportion of dietary consumption in China, and thus it is crucial to investigate the levels and composition profiles of PFCs in consumer meats to assess and mitigate potential health risks. However, rapid and simple analytical methods for such investigations are lacking. This study addresses this shortcoming by developing a rapid filtration clean-up method using carboxylic multi-walled carbon nanotubes (CMWCNTs) for the detection of 14 PFCs. Good linearities (R2 > 0.99) were obtained for all 14 PFCs in chicken, beef, and mutton. Recoveries ranged from 65 to 145% (except perfluorotetradecanoic acid, PFTeDA) with relative standard deviations (RSDs) lower than 16%. Method limits of detection (LODs) were between 0.12 and 0.83 ng g−1d.w. Subsequently, this method was applied for the analysis of 46 livestock and poultry meat samples collected from markets in Shanghai. PFCs were detected in all samples with total concentrations of 1.61–4.27 ng g−1 wet weight (w.w) in chicken, 1.51–5.56 ng g−1 w.w in beef and 1.61–8.15 ng g−1 w.w in mutton. Perfluorooctanoic acid (PFOA) and perfluorobutanoic acid (PFBA) were the predominant compounds in these samples and accounted for over 92% of ΣPFCs. For regulated compounds such as PFOA, risk quotients (RQs) were determined to be much lower than 1. This suggests that chicken, beef, and mutton from Shanghai market pose very low PFC exposure risk to residents. However, high detection frequency of the unregulated PFBA demands further investigation of short-chain PFCs in consumer meats.

Surveillance and analysis of parasitic infection in food on market in Shanghai during 2015—2019

Surveillance and analysis of parasitic infection in food on market in Shanghai during 2015—2019