The dynamics of price discovery between the U.S.and Chinese soybean market:a wavelet approach to understanding the effects of Sino-US trade conflict and COVID-19 pandemic

During geopolitical crises,the price stability of agricultural commodities is critical for national security.Understanding the dynamics of pricing power between the U.S.and China and how it varies over time can help smaller nations navigate unpredictable moments.This study uses a unified framework and wavelet approach to examine soybean price discovery in the U.S.and China from the standpoints of price interdependence and information flows.We begin by illustrating the integrated link between the soybean futures markets in the U.S.and China,which includes multiple structural breaks.The pricing difference between the two nations acts as the primary information spillover route for their integrated relationship.Furthermore,we show that the direction and degree of information spillover change dramatically in proportion to the strength of the U.S.–Chinese soybean interaction.Finally,we find that China’s recent retaliatory tax on the U.S.soybeans gave the Chinese market a more powerful position in soybean futures price discovery.After the first-stage trade deal was reached,and during the epidemic phase of the coronavirus pandemic,the pricing power of the U.S.soybean market showed no signs of full recovery.

Carbon emission trading system and stock price crash risk of heavily polluting listed companies in China:based on analyst coverage mechanism

This study reveals the inconsistencies between the negative externalities of carbon emissions and the recognition condition of accounting statements.Hence,the study identifies that heavily polluting enterprises in China have severe off-balance sheet carbon reduction risks before implementing the carbon emission trading system(CETS).Through the staggered difference-in-difference(DID)model and the propen-sity score matching-DID model,the impact of CETS on reducing the risk of stock price crashes is examined using data from China’s A-share heavily polluting listed companies from 2007 to 2019.The results of this study are as follows:(1)CETS can significantly reduce the risk of stock price crashes for heavily polluting companies in the pilot areas.Specifically,CETS reduces the skewness(negative conditional skewness)and down-to-up volatility of the firm-specific weekly returns by 8.7%and 7.6%,respectively.(2)Heterogeneity analysis further shows that the impacts of CETS on the risk of stock price crashes are more significant for heavily polluting enterprises with the bear market condition,short-sighted management,and intensive air pollution.(3)Mechanism tests show that CETS can reduce analysts’coverage of heavy polluters,reducing the risk of stock price crashes.This study reveals the role of CETS from the stock price crash risk perspective and helps to clarify the relationship between climatic risk and corporate financial risk.

Discussion on the Formation and Trading Mechanism of Art Market Capital Price

Under the market economy system,art is a new investment channel.With the improvement of people's living standards,it has a new understanding of art investment.Based on this,this paper takes the price of art as the research object,and elaborates the price formation and transaction of the art capital market from the aspects of the intrinsic elements of art,the investment of art,the supply and demand of art market,people's boastful consumption and social education.The constraints imposed by the mechanism.

Voluntary Disclosure and the Type of Product Market Competition： Capacity vs. Price

This paper investigates empirically the effect of different types of product market competition on levels of voluntary disclosure of proprietary information in financial markets. The author proposes that there are two types of strategic interaction settings relevant to disclosure： capacity competition and price competition. Capacity competition drives firms to disclose more information to attain financial market valuation-related benefits, while price competition drives them to disclose less to protect long-term product market advantages. The author finds that the type of product market competition affects the level of voluntary disclosure over and above the finn＇s external financing needs documented in the previous literature. That is, firms engaged in capacity competition disclose relatively more information than those in price competition. Further analysis shows that capacity competition firms disclose more information than no-strategic-interaction benchmark firms but that price competition firms do not disclose less information than the benchmark firms.

Spatial Interaction Network Analysis of Crude Oil Trade Relations between Countries along the Belt and Road

Based on the theories and methods of complex network,crude oil trade flows between countries along the Belt and Road(B&R,hereafter)are inserted into the Geo-space of B&R and form a spatial interaction network which takes the countries as nodes and takes the trade relations as edges.The networked mining and evolution analysis can provide important references for the research on trade relations among the B&R countries and the formulation of trade policy.This paper researches and discusses the construction,statistical analysis,top networks and stability of the crude oil trade network between the B&R countries from 2001 to 2020 from the perspectives of Geo-Computation for Social Sciences(GCSS)and spatial interaction.Firstly,evolutions of out-degree,in-degree,out-strength and in-strength of the top 10 countries in the crude oil trade network are computed and analyzed.Secondly,the top network method is used to explore the evolution characteristics of hierarchical structures.And finally,the sequential evolution characteristics of the crude oil trade network stability are analyzed utilizing the network stability measure method based on the trade relationship autocorrelation function.The analysis results show that Russia has the largest out-degree and out-strength,and China has the largest in-degree and in-strength.The crude oil trade volume of the top 10 import and export networks between 2001—2020 accounts for over 90%of the total trade volume of the crude oil trade network,and the proportion remains relatively stable.However,the stability of the network showed strong fluctuations in 2009,2012 and 2014,which may be closely related to major international events in these years,which could furtherly be used to build a correlation model between network volatility and major events.This paper explores how to construct and analyze the spatial interaction network of crude oil trade and can provide references for trade relations research and trade policy formulation of B&R countries.

Pricing Mechanism of China’s Trans-regional and Trans-provincial Electricity Trading

With the large-scale development of Chinese electric power, the contradiction of China’s energy supply and demand that reverse distributed is very prominent, therefore, promoting electricity trading is one of the important measures to get optimized configuration of energy resources in nationwide. For the two kinds of trading method, the “power point to the grid” trading and the “grid to grid” trading, this paper designed pricing mechanism model, and took one area as an example, we analyzed the impact of the participants by using different pricing mechanism, and put forward reasonable policy proposals for China’s pricing mechanism of trans-regional and trans-provincial electricity trading.

Study on the investment value and investment opportunity of renewable energies under the carbon trading system

China has promised to start the national carbon trading system in 2017.In the carbon trading system,the renewable energy projects may obtain additional benefits through the Certified Carbon Emission Reduction(CCER) trade.As the carbon price fluctuates along with the market conditions,such fluctuation enables the renewable power projects to acquire the rights of an option,i.e.it may contain an even higher value due to the uncertainties in the future.While making an investment decision,the renewable power companies may choose to make the investment immediately,or postpone the investment and accumulate more information to increase the return of investment;and for immediate investments,the return must be sufficient to exceed the potential value of a waiting option.To study the investment in renewable power projects subject to the fluctuation of carbon price,this paper adopts the trinomial tree model of real options to estimate the net present value(NPV) and real option value(ROV) of three types of renewable power projects;according to the decision-making rules of real options to defer,all the three types of projects will exercise the option to postpone the investment decision.This thesis also calculates the benchmark prices of the three types of renewable projects at different times,in the two situations of having no government subsidy and having the government subsidy,so as to determine the investment opportunity of a project.The benchmark price decreases gradually along with the increase of government subsidy,indicating that the government subsidy will stimulate the investment in renewable projects.The benchmark price also increases gradually along with the lapse of time,indicating that the uncertainty will increase together with the time span and thus requires an even higher carbon price to determine the investment opportunity.This thesis also analyzes the sensitivity of factors affecting the investment in renewable projects and draws the conclusion that the fluctuation of carbon price is positively related with the benchmark price of renewable power projects,which indicates that the fluctuation of carbon price increases the option value of an investment but postpones the time of investment.As the China's carbon trading system improves gradually,the carbon price will reach a stable status,thus stimulate the power companies to invest in the renewable projects.

Low Carbon Beijing:Research on the Influencing Factors of Carbon Emission Trading Price

The international community has taken extensive actions to achieve carbon neutrality and sustainable development with the intensification of global warming and climate change.China has also carried out a long-term layout,setting the goal of achieving a carbon peak by 2030 and carbon neutrality by 2060.In 2021,with the official launch of a unified national carbon emissions trading market,China’s nationwide carbon emissions trading kicked off.Carbon emission trading is an important policy tool for China’s carbon peak and carbon-neutral action and an essential part of the country’s promotion of a comprehensive green transformation of the economy and society.This study uses a VAR(Vector Autoregressive)model to analyze the influencing factors of the Beijing carbon emissions trading price from January 2014 to December 2019.The study found that coal prices have the most significant impact on Beijing’s carbon emissions trading prices.Oil prices,industrial development indexes,and AQI(Air Quality Index)impacted Beijing’s carbon emissions trading prices.In contrast,natural gas prices and economic indexes have the most negligible impact.These findings will help decision-makers determine a reasonable price for carbon emissions trading and contribute to the market’s healthy development.

Chinese and US Economies in Comparison and Interaction:Now and Future as China Economist Surveys

China Economist has continuously carried out surveys among economists and this round of survey focuses on comparison and interactions between China＇s and the United States＇ economies. The result of the survey shows that economists are generally optimistic about the outlook of both countries＇economies. Respondents believed that great differences exist in the components of industrial competitiveness of China and the US; while the US leads in terms of talent, creativity, social system, industrial system integrity and financing, cost is the biggest barrier to improvement in US competitiveness. In comparison, China leads in infrastructure, cost competitiveness and government driving force but inadequate technology is the biggest barrier to improvement in China＇s competitiveness. Respondents believed that in the coming 20years, China＇s economic growth will be 5.2% and US growth will be 2.4%. Around 2034, China＇s economic aggregate will equal the US level but it will take over 60 years for China to catch up with the US in terms of per capita GDP. China＇s manufacturing technology will equal the US level around 2045. More than 62% of economists believed that the Trump administration will effectively re-shore manufacturing and the average score they give to Trump＇s ＂first 100 days ＂＂ in office is 76 points. More than 61% of economists considered it unlikely that a serious trade war will break out between China and the US. They generally believed that China and the US cooperate and compete with each other and that China-US trade enjoys great potential to grow. According to the survey, respondents are more confident about China＇s debt sustainability in comparison with the US.

Factors affecting the pilot trading market of carbon emissions in China

Climate change and carbon emissions are major problems which are attracting worldwide attention. China has had its pilot carbon emission trading markets in seven regions for more than 3 years. What affects carbon emission trading market in China is a big question. More attention is paid to how China promotes the carbon emission trading schemes in the whole country. This paper addresses concerns about the functioning of carbon emission trading schemes in seven pilot regions and takes the weekly data from November 25, 2013, to March 19, 2017. We employ a vector autoregressive model to study how coal price, oil price and stock index have affected the carbon price in China. The results indicate that carbon price is mainly affected by its own historical price; coal price and stock index have negative effects on carbon price, while oil price has a negative effect on carbon price during the first 3 weeks and then has a positive effect on carbon price. More regulatory attention and economic measures are needed to improve market efficiency, and the mechanisms of carbon emission trading schemes should be improved.

The Research about the Trans-provincial Centralized Bidding Trading Market of East China Power Grid --I:Empirical Analysis

The clearing price and bidding price in electricity market are two key indicators to measure whether it is reasonable or not. Based on the grey incidence analysis, this paper studies the correlation coefficient between the clearing price and bidding price with the generation cost, the supervision and rules of the market, the supply and demand situation, the behavior of market members over the same period, which is based on the actual data of the trans-provincial centralized trading market of East China Power Grid. The results show that the factors affecting the clearing price and bidding price from largest to smallest are generation cost, supervision and rules of the market, the supply and demand situation, the behavior of market members. The conclusion is that the trans-provincial trading platform of East China Power Grid is a reasonable regional market which can discover the market cost, and regulate the market supply and demand balance, and promote healthy competition.

Modeling some long-term implications of CO_2 fertilization for global forests and forest industries

Background： This paper explored the long-term, ceteris-paribus effects of potential CO2 fertilization on the global forest sector. Based on the findings of Norby et al. （PNAS 2005, 102（50）） about forest response to elevated [CO2]. Methods： Forest productivity was increased in the Global Forest Products Model （GFPM） in proportion to the rising [CO2] projected in the IPCC scenario A1B, A2, and 132. Projections of the forest area and forest stock and of the production, consumption, prices, and trade of products ranging from fuelwood to paper and paperboard were obtained with the GFPM for each scenario, with and without CO2 fertilization beginning in 2011 and up to 2065. Results： CO2 fertilization increased wood supply, leading to lower wood prices which in turn induced modest lower prices of end products and higher global consumption. However, production and value added in industries decreased in some regions due to the relative competitive advantages and to the varying regional effects of CO2 fertilization. Conclusion： The main effect of CO2 fertilization was to raise the level of the world forest stock in 2065 by 9 to 10 % for scenarios A2 and B2 and by 20 % for scenario A1B. The rise in forest stock induced by fertilization was in part counteracted by its stimulation of the wood supply which resulted in lower wood prices and increased harvests.

A Decision Support System for Economic Development and Price Reform

In this paper, we present a real decision support system applied to China's macroeconomic management. The structure, design and functions of the system are discussed, and the problems occurring in designing and making the system are also studied. A case study for China's petroleum price reform is given at the end of the paper.

Price Limit, Superior Information and Investor Behavior:Evidence from China Stock Market

The mechanism of price limit impacts on informed traders’ behavior. To find out what effects related to price limit, three hypotheses caused by price limit are analyzed. Comprehensive method of event study and comparative grouping is used to test the performance of price limit in Chinese stock market. The result of test indicates that price limit policy impedes fulfillment of traders and delays the discovery of stock price so it should be abolished.

Profitability of technical trading strategies under market manipulation

Most technical trading strategies use the official closing price for analysis.But what is the effect when the official closing price is subject to market manipulation?This paper answers this question by testing the difference of profitabilities between using the official closing price and the last tick price.The results show a significant improvement of profitability by using the last tick price over the official closing price based on a data set in Hong Kong from 2011 to 2018.

Hybrid data decomposition-based deep learning for Bitcoin prediction and algorithm trading

In recent years,Bitcoin has received substantial attention as potentially high-earning investment.However,its volatile price movement exhibits great financial risks.Therefore,how to accurately predict and capture changing trends in the Bitcoin market is of substantial importance to investors and policy makers.However,empirical works in the Bitcoin forecasting and trading support systems are at an early stage.To fill this void,this study proposes a novel data decomposition-based hybrid bidirectional deep-learning model in forecasting the daily price change in the Bitcoin market and conducting algorithmic trading on the market.Two primary steps are involved in our methodology framework,namely,data decomposition for inner factors extraction and bidirectional deep learning for forecasting the Bitcoin price.Results demonstrate that the proposed model outperforms other benchmark models,including econometric models,machine-learning models,and deep-learning models.Furthermore,the proposed model achieved higher investment returns than all benchmark models and the buy-and-hold strategy in a trading simulation.The robustness of the model is verified through multiple forecasting periods and testing intervals.

Carbon Price Forecasting Approach Based on Multi-Scale Decomposition and Transfer Learning

Accurate carbon price forecasting is essential to provide the guidance for production and investment.Current research is mainly dependent on plenty of historical samples of carbon prices,which is impractical for the newly launched carbon market due to its short history.Based on the idea of transfer learning,this paper proposes a novel price forecasting model,which utilizes the correlation between the new and mature markets.The model is firstly pretrained on large data of mature market by gated recurrent unit algorithm,and then fine-tuned by the target market samples.An integral framework,including complexity decomposition method for data pre-processing,sample entropy for feature selection,and support vector regression for result post-processing,is provided.In the empirical analysis of new Chinese market,the root mean square error,mean absolute error,mean absolute percentage error,and determination coefficient of the model are 0.529,0.476,0.717%and 0.501 respectively,proving its validity.

An Empirical Study on the Relationship Between China's Maize Price and Maize Import and Export Trade

Based on the time series of China’s maize trade and domestic maize price,the article analyzes the relationship between domestic maize price and China’s maize international trade by using empirical methods.The results show that there is a connection between domestic maize prices and China's maize international trade,but the link is not very close.Domestic maize price is the cause of China’s maize international trade change,but its impact is very limited;China's maize international trade also can make some difference on the domestic maize price.Based on the results of research,the article brings three suggestion and expectation to adjust China’s maize international trade and the cost control reasonably in order to stabilize domestic maize price and maize yield,and to protect domestic grain security.

Effects of Financial Market Variables on Stock Prices:A Review of the Literature

Stock market plays a pivotal role in firms’expansion and turns economic growth.In the literature,because of the importance of stock markets to the real economy,the smooth and risk-free operation of the stock market has attracted significant attention.The finance literature contains a large number of studies that examine the stock price behaviour with some emphasis on the determinants of the relationship between the equity prices and the financial market activities.The present study reviews the previous works of the effect of financial market variables and stock price.Five selected financial market variables,market capitalization,earnings per share,price earnings multiples,dividend yield,and trading volume are reviewed in this study.In the past literature,there are the opinions of the positive significant relationship between market capitalization and stock price.To find the relationship between dividend yield and stock price,there are two broad schools of thoughts.Both of the relevance and irrelevance theory of Gordon and Modigliani have the strong evidence in the current literature that keeps on the dilemma and provides the scopes for future research.Price-earnings multiples are analyzed in the past literature by using different variables.Based on that,it is evidenced that price-earnings multiples have a negative significant effect on stock price.The reviewed studies state the cointegrating relationship between the stock price and the trading volume as the trading volume is a source of risk.

Research on Optimal Configuration of Energy Storage in Wind-Solar Microgrid Considering Real-Time Electricity Price

Capacity allocation and energy management strategies for energy storage are critical to the safety and economical operation of microgrids.In this paper,an improved energymanagement strategy based on real-time electricity price combined with state of charge is proposed to optimize the economic operation of wind and solar microgrids,and the optimal allocation of energy storage capacity is carried out by using this strategy.Firstly,the structure and model of microgrid are analyzed,and the outputmodel of wind power,photovoltaic and energy storage is established.Then,considering the interactive power cost between the microgrid and the main grid and the charge-discharge penalty cost of energy storage,an optimization objective function is established,and an improved energy management strategy is proposed on this basis.Finally,a physicalmodel is built inMATLAB/Simulink for simulation verification,and the energy management strategy is compared and analyzed on sunny and rainy days.The initial configuration cost function of energy storage is added to optimize the allocation of energy storage capacity.The simulation results show that the improved energy management strategy can make the battery charge-discharge response to real-time electricity price and state of charge better than the traditional strategy on sunny or rainy days,reduce the interactive power cost between the microgrid system and the power grid.After analyzing the change of energy storage power with cost,we obtain the best energy storage capacity and energy storage power.