CAETE fosters real partnerships and drives China-Africa relations through business,culture and trust The city of Changsha recently concluded a remarkable chapter in China-Africa relations with the conclusion of the fo...CAETE fosters real partnerships and drives China-Africa relations through business,culture and trust The city of Changsha recently concluded a remarkable chapter in China-Africa relations with the conclusion of the fourth China-Africa Economic and Trade Expo(CAETE).Since its debut in 2019,the event has expanded in size and stature,emerging as a dynamic hub where business,innovation,and cultural exchange intersect.展开更多
Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambig...Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.展开更多
The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.Wh...The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.展开更多
Financial market liquidity is a popular research topic.Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate,the lower the returns.However,the...Financial market liquidity is a popular research topic.Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate,the lower the returns.However,the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models.To explore high machine-driven liquidity and the impact of high turnover rates on returns,this study establishes a dual-market quantitative trading system,introduces a variational modal decomposition(VMD)-bidirectional gated recurrent unit(BiGRU)model for data prediction,and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S.and Chinese stock markets.The experimental results show that given a principal amount of 210,000.00 CNY,the final predicted net return is 226,538.30 CNY,a net return of 107.86%,which is 40.6%higher than the net return of a single Chinese market.We conclude that,under machine-driven trading,increasing liquidity and turnover increase returns.This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.展开更多
Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainabili...Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.展开更多
Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MG...Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.展开更多
Objective To provide effective decision making for the subsidy policies given by the government to pharmaceutical enterprises and the coordination model adopted by pharmaceutical stakeholders under the carbon emission...Objective To provide effective decision making for the subsidy policies given by the government to pharmaceutical enterprises and the coordination model adopted by pharmaceutical stakeholders under the carbon emission trading policy.Methods The Stackelberg model was used to discuss the optimal profits of each member and the whole supply chain under different decision-making models while considering the technical capacity of emission reduction and cost sharing contract.Based on this,the impact of the combined contract decisionmaking model on the technical efforts of drug manufacturers to reduce carbon emission,the profits of supply chain members and the overall profits of supply chain was investigated.Results and Conclusion Research has found that improving the research and development efforts of emission reduction technologies by pharmaceutical enterprises can increase drug sales and enhance the expected profits of pharmaceutical supply chain members.The members of the secondary pharmaceutical supply chain can achieve the optimal expected profit when reaching cooperation.Besides,when the cost sharing contract and quantity discount contract meet the constraint conditions,the combined contract decision model can perfectly coordinate the pharmaceutical supply chain,enabling supply chain members to achieve Pareto improvement and gradually reach Pareto optimum.展开更多
A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing ...A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems.A hybrid carbon trading mechanism that combines shortterm and long-term carbon trading is constructed,and a fuzzy set based onWasserstein measurement is proposed to address the uncertainty of wind power access.Moreover,a robust scheduling optimization method for wind–fire storage systems is formed.Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions.展开更多
In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading...In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.展开更多
The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading mar...The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.展开更多
Blockchain,known for its secure encrypted ledger,has garnered attention in financial and data transfer realms,including the field of energy trading.However,the decentralized nature and identity anonymity of user nodes...Blockchain,known for its secure encrypted ledger,has garnered attention in financial and data transfer realms,including the field of energy trading.However,the decentralized nature and identity anonymity of user nodes raise uncertainties in energy transactions.The broadcast consensus authentication slows transaction speeds,and frequent single-point transactions in multi-node settings pose key exposure risks without protective measures during user signing.To address these,an alliance blockchain scheme is proposed,reducing the resource-intensive identity verification among nodes.It integrates multi-signature functionality to fortify user resources and transac-tion security.A novel multi-signature process within this framework involves neutral nodes established through central nodes.These neutral nodes participate in multi-signature’s signing and verification,ensuring user identity and transaction content privacy.Reducing interactions among user nodes enhances transaction efficiency by minimizing communication overhead during verification and consensus stages.Rigorous assessments on reliability and operational speed highlight superior security performance,resilient against conventional attack vectors.Simulation shows that compared to traditional solutions,this scheme has advantages in terms of running speed.In conclusion,the alliance blockchain framework introduces a novel approach to tackle blockchain’s limitations in energy transactions.The integrated multi-signature process,involving neutral nodes,significantly enhances security and privacy.The scheme’s efficiency,validated through analytical assessments and simulations,indicates robustness against security threats and improved transactional speeds.This research underscores the potential for improved security and efficiency in blockchain-enabled energy trading systems.展开更多
A model of continuous-time insider trading in which a risk-neutral in-sider possesses two imperfect correlated signals of a risky asset is studied.By conditional expectation theory and filtering theory,we first establ...A model of continuous-time insider trading in which a risk-neutral in-sider possesses two imperfect correlated signals of a risky asset is studied.By conditional expectation theory and filtering theory,we first establish three lemmas:normal corre-lation,equivalent pricing and equivalent profit,which can guarantee to turn our model into a model with insider knowing full information.Then we investigate the impact of the two correlated signals on the market equilibrium consisting of optimal insider trading strategy and semi-strong pricing rule.It shows that in the equilibrium,(1)the market depth is constant over time;(2)if the two noisy signals are not linerly correlated,then all private information of the insider is incorporated into prices in the end while the whole information on the asset value can not incorporated into prices in the end;(3)if the two noisy signals are linear correlated such that the insider can infer the whole information of the asset value,then our model turns into a model with insider knowing full information;(4)if the two noisy signals are the same then the total ex ant profit of the insider is increasing with the noise decreasing,while down to O as the noise going up to infinity;(5)if the two noisy signals are not linear correlated then with one noisy signal fixed,the total ex ante profit of the insider is single-peaked with a unique minimum with respect to the other noisy signal value,and furthermore as the noisy value going to O it gets its maximum,the profit in the case that the real value is observed.展开更多
Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate ener...Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors.However,further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen(P2H)technology,focusing on participating in combined carbon-electricity market transactions.This study introduces an innovative Electro-Hydrogen Regional Energy System(EHRES)in this context.This system integrates renewable energy sources,a P2H system,cogeneration units,and energy storage devices.The core purpose of this integration is to optimize renewable energy utilization and minimize carbon emissions.This study aims to formulate an optimal operational strategy for EHRES,enabling its dynamic engagement in carbon-electricity market transactions.The initial phase entails establishing the technological framework of the electricity-hydrogen coupling system integrated with P2H.Subsequently,an analysis is conducted to examine the operational mode of EHRES as it participates in carbon-electricity market transactions.Additionally,the system scheduling model includes a stepped carbon trading price mechanism,considering the combined heat and power generation characteristics of the Hydrogen Fuel Cell(HFC).This facilitates the establishment of an optimal operational model for EHRES,aiming to minimize the overall operating cost.The simulation example illustrates that the coordinated operation of EHRES in carbon-electricity market transactions holds the potential to improve renewable energy utilization and reduce the overall system cost.This result carries significant implications for attaining advantages in both low-carbon and economic aspects.展开更多
How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop tradi...How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data,is struggling to deal with fastchanging markets due to sample inefficiency.This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning(RL)approaches in non-stationary markets for the first time.In our work,the history trading data is divided into multiple task data and for each of these data themarket condition is relatively stationary.Then amodel agnosticmeta-learning(MAML)-based tradingmethod involving a meta-learner and a normal learner is proposed.A trading policy is learned by the meta-learner across multiple task data,which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.To improve the adaptability of the MAML-based method,an ordered multiplestep updating mechanism is also proposed to explore the changing dynamic within a task market.The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%,200%,and 160%,increase the Sharpe ratio by 180%,90%,and 170%,and decrease the maximum drawdown by 30%,20%,and 40%,compared to the traditional RL approach in three stock index future markets,respectively.展开更多
Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one ...Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one of the most challenging tasks.Therefore,this study analyzes and extracts news media data,expert comments,social opinion data,and pandemic text data using natural language processing,and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices.An interval constraint-based trading strategy is constructed.Using data from several typical stocks in the Chinese stock market during the COVID-19 period,the empirical studies and trading simulations show,first,that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting.Second,the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus,can assist investors in decision-making.展开更多
The extended trading close(ETC)provides institutional investors an opportunity to trade at the closing price after the regular trading session(RTS)and disclosing the order imbalances to other market participants.ETCs ...The extended trading close(ETC)provides institutional investors an opportunity to trade at the closing price after the regular trading session(RTS)and disclosing the order imbalances to other market participants.ETCs exist in the Nasdaq,the SSE STAR,the SZSE ChiNext and the TWSE.To help a risk-averse institutional investor take advantage of the RTS and the ETC for liquidation,we develop a multistage dynamic programming model including the ETC,and derive recursive solutions for the multiple trading days scenario with closed-form solutions for the scenario with only two trading days.We also verify that the ETC is able to mitigate extreme price movements caused by fast liquidation,which is also a goal of the ETC set out by the SSE STAR and the SZSE ChiNext.Finally,we derive three results.First,an institutional investor can reduce execution costs after the introduction of the ETC.Second,a critical trading day exists,and to avoid prematurely revealing trading intentions,the investor should not trade in the ETC until such day.Third,even though the ETC orders submitted by the investor are unfilled,implementation of the ETC encourages the investor to change the liquidation strategy in the RTS,which reduces extreme price movements.In summary,the practical implications of this paper are that the investor should not trade during the ETC on the front few days to avoid prematurely revealing the investor’s trading intention by unfilled orders in the ETC and that introducing the ETC can reduce liquidation costs and extreme price movements.展开更多
China has remained Africa’s largest trading partner for 15 consecutive years,with bilateral trade reaching a record$282.1 billion in 2023,Ministry of Commerce official Jiang Wei said at a press conference on 31 Janua...China has remained Africa’s largest trading partner for 15 consecutive years,with bilateral trade reaching a record$282.1 billion in 2023,Ministry of Commerce official Jiang Wei said at a press conference on 31 January.Economic and trade cooperation is the ballast and propeller of China-Africa relations,he said.展开更多
Green and low carbon transition is a broad and profound economic and social systematic change.Green innovation is a critical way to promote energy saving and emission reduction.Has China continuously promoted a carbon...Green and low carbon transition is a broad and profound economic and social systematic change.Green innovation is a critical way to promote energy saving and emission reduction.Has China continuously promoted a carbon emission trading policy to significantly promote green innovation cooperation?Taking the implementation of the carbon emission trading pilot policy as a“quasi-natural experiment,”this study answers this question by exploring the impact of the policy on green innovation cooperation.Based on data on 274 cities from 2008 to 2020,the multi-time difference-in-differences model is used to evaluate the impact of the policy on green innovation cooperation.The results reveal that the carbon emission trading pilot policy significantly improved inter-and intra-city green innovation cooperation through the upgrading effect of industrial structure and the coverage effect of digital finance compared with the non-pilot cities at the city level.In addition,there are significant differences in the policy effects among cities with different degrees of openness to the outside world and command-and-control environmental regulation.展开更多
Price volatility in stock market brings potential profile positions to the traders. How to predict the direction of the stock market or stock price becomes the primary job for traders＇ trading model. We are looking f...Price volatility in stock market brings potential profile positions to the traders. How to predict the direction of the stock market or stock price becomes the primary job for traders＇ trading model. We are looking for the direction of the market in a given timeframe. High-frequency traders will consider the potential profile-out position in millisecond level. Long-term holder will look into month time scale. For most of average traders, the ideal timeframe will be on daily base. In this paper, for a non-news trading day, the author will introduce statistics method to predict the stock prices and bid-ask spread for day trading.展开更多
This paper explores the effects of China’s global value chain(GVC)participation on technological progress in trading-partner countries based on estimated data on value-added trade between China and 52 trading partner...This paper explores the effects of China’s global value chain(GVC)participation on technological progress in trading-partner countries based on estimated data on value-added trade between China and 52 trading partners.We find that,first,although China’s exports lowered the total factor productivity(TFP)of its trading partners(competitive effect),its imports greatly increased trading partners’TFP(effect of scale).This implies that China’s GVC participation is beneficial to its trading partners’technological progress in the form of a considerable technology dividend effect.Second,China’s export dividend effect compensates for the negative effect of Chinese competition on trading partners’technological progress;the innovation effects attributable to China’s imports reinforce the positive effects of scale on technological progress.When innovation is factored in,the China dividend thus becomes further reinforced.Third,China’s merchandise imports have a diminishing positive effect on technological progress in trading partners as geographical distance increases,but trade in services transcends geographical boundaries,and the positive technological progress effect of China’s service imports do not diminish as distance increases.We find that the“China dividend”from China’s GVC participation is a significant contributor to technological progress in partner nations,and China’s imports are conducive to innovation and technological progress in developed countries in the long run.展开更多
文摘CAETE fosters real partnerships and drives China-Africa relations through business,culture and trust The city of Changsha recently concluded a remarkable chapter in China-Africa relations with the conclusion of the fourth China-Africa Economic and Trade Expo(CAETE).Since its debut in 2019,the event has expanded in size and stature,emerging as a dynamic hub where business,innovation,and cultural exchange intersect.
基金supported by the National Key Research and Development Program of China(No.2022YFF0610003)the BUPT Excellent Ph.D.Students Foundation(No.CX2022218)the Fund of Central University Basic Research Projects(No.2023ZCTH11).
文摘Data trading is a crucial means of unlocking the value of Internet of Things(IoT)data.However,IoT data differs from traditional material goods due to its intangible and replicable nature.This difference leads to ambiguous data rights,confusing pricing,and challenges in matching.Additionally,centralized IoT data trading platforms pose risks such as privacy leakage.To address these issues,we propose a profit-driven distributed trading mechanism for IoT data.First,a blockchain-based trading architecture for IoT data,leveraging the transparent and tamper-proof features of blockchain technology,is proposed to establish trust between data owners and data requesters.Second,an IoT data registration method that encompasses both rights confirmation and pricing is designed.The data right confirmation method uses non-fungible token to record ownership and authenticate IoT data.For pricing,we develop an IoT data value assessment index system and introduce a pricing model based on a combination of the sparrow search algorithm and the back propagation neural network.Finally,an IoT data matching method is designed based on the Stackelberg game.This establishes a Stackelberg game model involving multiple data owners and requesters,employing a hierarchical optimization method to determine the optimal purchase strategy.The security of the mechanism is analyzed and the performance of both the pricing method and matching method is evaluated.Experiments demonstrate that both methods outperform traditional approaches in terms of error rates and profit maximization.
基金supported by National Natural Science Foundation of China(52407126).
文摘The demand response(DR)market,as a vital complement to the electricity spot market,plays a key role in evoking user-side regulation capability to mitigate system-level supply‒demand imbalances during extreme events.While the DR market offers the load aggregator(LA)additional profitable opportunities beyond the electricity spot market,it also introduces new trading risks due to the significant uncertainty in users’behaviors.Dispatching energy storage systems(ESSs)is an effective means to enhance the risk management capabilities of LAs;however,coordinating ESS operations with dual-market trading strategies remains an urgent challenge.To this end,this paper proposes a novel systematic risk-aware coordinated trading model for the LA in concurrently participating in the day-ahead electricity spot market and DR market,which incorporates the capacity allocation mechanism of ESS based on market clearing rules to jointly formulate bidding and pricing decisions for the dual market.First,the intrinsic coupling characteristics of the LA participating in the dual market are analyzed,and a joint optimization framework for formulating bidding and pricing strategies that integrates ESS facilities is proposed.Second,an uncertain user response model is developed based on price‒response mechanisms,and actual market settlement rules accounting for under-and over-responses are employed to calculate trading revenues,where possible revenue losses are quantified via conditional value at risk.Third,by imposing these terms and the capacity allocation mechanism of ESS,the risk-aware stochastic coordinated trading model of the LA is built,where the bidding and pricing strategies in the dual model that trade off risk and profit are derived.The simulation results of a case study validate the effectiveness of the proposed trading strategy in controlling trading risk and improving the trading income of the LA.
基金supported by the National Natural Science Foundation of China(Grant Nos.:72032006 and 92146005).
文摘Financial market liquidity is a popular research topic.Investor-driven research uses the turnover rate to measure liquidity and generally finds that the higher the stock turnover rate,the lower the returns.However,the traditional financial liquidity theory has been impacted by new machine-driven quantitative trading models.To explore high machine-driven liquidity and the impact of high turnover rates on returns,this study establishes a dual-market quantitative trading system,introduces a variational modal decomposition(VMD)-bidirectional gated recurrent unit(BiGRU)model for data prediction,and uses the back-end Hong Kong foreign exchange market to develop a quantitative trading strategy using the same rotating funds in the U.S.and Chinese stock markets.The experimental results show that given a principal amount of 210,000.00 CNY,the final predicted net return is 226,538.30 CNY,a net return of 107.86%,which is 40.6%higher than the net return of a single Chinese market.We conclude that,under machine-driven trading,increasing liquidity and turnover increase returns.This study provides a new perspective on liquidity theory that is useful for future financial market research and quantitative trading practices.
基金supported by National Key Research and Development Program(2024YFE0115600).
文摘Addressing climate change and facilitating the large-scale integration of renewable energy sources(RESs)have driven the development of hydrogen-coupled integrated energy systems(HIES),which enhance energy sustainability through coordinated electricity,thermal,natural gas,and hydrogen utilization.This study proposes a two-stage distributionally robust optimization(DRO)-based scheduling method to improve the economic efficiency and reduce carbon emissions of HIES.The framework incorporates a ladder-type carbon trading mechanism to regulate emissions and implements a demand response(DR)program to adjustflexible multi-energy loads,thereby prioritizing RES consumption.Uncertainties from RES generation and load demand are addressed through an ambiguity set,enabling robust decision-making.The column-and-constraint generation(C&CG)algorithm efficiently solves the two-stage DRO model.Case studies demonstrate that the proposed method reduces operational costs by 3.56%,increases photovoltaic consumption rates by 5.44%,and significantly lowers carbon emissions compared to conventional approaches.Furthermore,the DRO framework achieves a superior balance between conservativeness and robustness over conventional stochastic and robust optimization methods,highlighting its potential to advance cost-effective,low-carbon energy systems while ensuring grid stability under uncertainty.
基金supported by the State Key Laboratory of Technology and Equipment for Defense against Power System Operational Risks Program(grant number SGNR0000KJJS2302139).
文摘Microgrids (MGs) and active distribution networks (ADNs) are important platforms for distributed energy resource (DER) consumption. The increasing penetration of DERs has motivated the development ADNs coupled with MGs. This paper proposes a distributedco-optimization method for peer-to-peer (P2P) energy trading and network operation for an ADN integrated with multiple microgrids(MMGs). A framework that optimizes P2P energy trading among MMGs and ADN operations was first established. Subsequently, anenergy management model that aims to minimize the operation and energy trading costs was constructed for each MG. Accordingly, theMMGs’ cooperative game model was established based on Nash bargaining theory to incentivize each stakeholder to participate in P2Penergy trading, and a distributed solution method based on the alternating direction method of multipliers was developed. Moreover, analgorithm that adjusts the amount of energy trading between the ADN and MG is proposed to ensure safe operation of the distributionnetwork. With the communication between the MG and ADN, the MMGs’ P2P trading and ADN operations are optimized in a coordinated manner. Finally, numerical simulations were conducted to verify the accuracy and effectiveness of the proposed method.
文摘Objective To provide effective decision making for the subsidy policies given by the government to pharmaceutical enterprises and the coordination model adopted by pharmaceutical stakeholders under the carbon emission trading policy.Methods The Stackelberg model was used to discuss the optimal profits of each member and the whole supply chain under different decision-making models while considering the technical capacity of emission reduction and cost sharing contract.Based on this,the impact of the combined contract decisionmaking model on the technical efforts of drug manufacturers to reduce carbon emission,the profits of supply chain members and the overall profits of supply chain was investigated.Results and Conclusion Research has found that improving the research and development efforts of emission reduction technologies by pharmaceutical enterprises can increase drug sales and enhance the expected profits of pharmaceutical supply chain members.The members of the secondary pharmaceutical supply chain can achieve the optimal expected profit when reaching cooperation.Besides,when the cost sharing contract and quantity discount contract meet the constraint conditions,the combined contract decision model can perfectly coordinate the pharmaceutical supply chain,enabling supply chain members to achieve Pareto improvement and gradually reach Pareto optimum.
基金supported by Liaoning Provincial Doctoral Research Initiation Fund Project(2022-BS-225)Liaoning Provincial Department of Education Scientific Research Project(LJKZ1091).
文摘A robust scheduling optimization method for wind–fire storage system distribution based on the mixed carbon trading mechanism is proposed to improve the rationality of carbon emission quota allocation while reducing the instability of large-scale wind power access systems.A hybrid carbon trading mechanism that combines shortterm and long-term carbon trading is constructed,and a fuzzy set based onWasserstein measurement is proposed to address the uncertainty of wind power access.Moreover,a robust scheduling optimization method for wind–fire storage systems is formed.Results of the multi scenario comparative analysis of practical cases show that the proposed method can deal with the uncertainty of large-scale wind power access and can effectively reduce operating costs and carbon emissions.
基金This project was funded by Deanship of Scientific Research(DSR)at King Abdulaziz University,Jeddah underGrant No.(IFPIP-1127-611-1443)the authors,therefore,acknowledge with thanks DSR technical and financial support.
文摘In the rapidly evolving landscape of today’s digital economy,Financial Technology(Fintech)emerges as a trans-formative force,propelled by the dynamic synergy between Artificial Intelligence(AI)and Algorithmic Trading.Our in-depth investigation delves into the intricacies of merging Multi-Agent Reinforcement Learning(MARL)and Explainable AI(XAI)within Fintech,aiming to refine Algorithmic Trading strategies.Through meticulous examination,we uncover the nuanced interactions of AI-driven agents as they collaborate and compete within the financial realm,employing sophisticated deep learning techniques to enhance the clarity and adaptability of trading decisions.These AI-infused Fintech platforms harness collective intelligence to unearth trends,mitigate risks,and provide tailored financial guidance,fostering benefits for individuals and enterprises navigating the digital landscape.Our research holds the potential to revolutionize finance,opening doors to fresh avenues for investment and asset management in the digital age.Additionally,our statistical evaluation yields encouraging results,with metrics such as Accuracy=0.85,Precision=0.88,and F1 Score=0.86,reaffirming the efficacy of our approach within Fintech and emphasizing its reliability and innovative prowess.
基金the National Fund Project No.62172337National Natural Science Foundation of China(No.61662069)China Postdoctoral Science Foundation(No.2017M610817).
文摘The carbon tradingmarket can promote“carbon peaking”and“carbon neutrality”at low cost,but carbon emission quotas face attacks such as data forgery,tampering,counterfeiting,and replay in the electricity trading market.Certificateless signatures are a new cryptographic technology that can address traditional cryptography’s general essential certificate requirements and avoid the problem of crucial escrowbased on identity cryptography.However,most certificateless signatures still suffer fromvarious security flaws.We present a secure and efficient certificateless signing scheme by examining the security of existing certificateless signature schemes.To ensure the integrity and verifiability of electricity carbon quota trading,we propose an electricity carbon quota trading scheme based on a certificateless signature and blockchain.Our scheme utilizes certificateless signatures to ensure the validity and nonrepudiation of transactions and adopts blockchain technology to achieve immutability and traceability in electricity carbon quota transactions.In addition,validating electricity carbon quota transactions does not require time-consuming bilinear pairing operations.The results of the analysis indicate that our scheme meets existential unforgeability under adaptive selective message attacks,offers conditional identity privacy protection,resists replay attacks,and demonstrates high computing and communication performance.
文摘Blockchain,known for its secure encrypted ledger,has garnered attention in financial and data transfer realms,including the field of energy trading.However,the decentralized nature and identity anonymity of user nodes raise uncertainties in energy transactions.The broadcast consensus authentication slows transaction speeds,and frequent single-point transactions in multi-node settings pose key exposure risks without protective measures during user signing.To address these,an alliance blockchain scheme is proposed,reducing the resource-intensive identity verification among nodes.It integrates multi-signature functionality to fortify user resources and transac-tion security.A novel multi-signature process within this framework involves neutral nodes established through central nodes.These neutral nodes participate in multi-signature’s signing and verification,ensuring user identity and transaction content privacy.Reducing interactions among user nodes enhances transaction efficiency by minimizing communication overhead during verification and consensus stages.Rigorous assessments on reliability and operational speed highlight superior security performance,resilient against conventional attack vectors.Simulation shows that compared to traditional solutions,this scheme has advantages in terms of running speed.In conclusion,the alliance blockchain framework introduces a novel approach to tackle blockchain’s limitations in energy transactions.The integrated multi-signature process,involving neutral nodes,significantly enhances security and privacy.The scheme’s efficiency,validated through analytical assessments and simulations,indicates robustness against security threats and improved transactional speeds.This research underscores the potential for improved security and efficiency in blockchain-enabled energy trading systems.
文摘A model of continuous-time insider trading in which a risk-neutral in-sider possesses two imperfect correlated signals of a risky asset is studied.By conditional expectation theory and filtering theory,we first establish three lemmas:normal corre-lation,equivalent pricing and equivalent profit,which can guarantee to turn our model into a model with insider knowing full information.Then we investigate the impact of the two correlated signals on the market equilibrium consisting of optimal insider trading strategy and semi-strong pricing rule.It shows that in the equilibrium,(1)the market depth is constant over time;(2)if the two noisy signals are not linerly correlated,then all private information of the insider is incorporated into prices in the end while the whole information on the asset value can not incorporated into prices in the end;(3)if the two noisy signals are linear correlated such that the insider can infer the whole information of the asset value,then our model turns into a model with insider knowing full information;(4)if the two noisy signals are the same then the total ex ant profit of the insider is increasing with the noise decreasing,while down to O as the noise going up to infinity;(5)if the two noisy signals are not linear correlated then with one noisy signal fixed,the total ex ante profit of the insider is single-peaked with a unique minimum with respect to the other noisy signal value,and furthermore as the noisy value going to O it gets its maximum,the profit in the case that the real value is observed.
基金supported financially by InnerMongoliaKey Lab of Electrical Power Conversion,Transmission,and Control under Grant IMEECTC2022001the S&TMajor Project of Inner Mongolia Autonomous Region in China(2021ZD0040).
文摘Given the“double carbon”objective and the drive toward low-carbon power,investigating the integration and interaction within the carbon-electricity market can enhance renewable energy utilization and facilitate energy conservation and emission reduction endeavors.However,further research is necessary to explore operational optimization methods for establishing a regional energy system using Power-to-Hydrogen(P2H)technology,focusing on participating in combined carbon-electricity market transactions.This study introduces an innovative Electro-Hydrogen Regional Energy System(EHRES)in this context.This system integrates renewable energy sources,a P2H system,cogeneration units,and energy storage devices.The core purpose of this integration is to optimize renewable energy utilization and minimize carbon emissions.This study aims to formulate an optimal operational strategy for EHRES,enabling its dynamic engagement in carbon-electricity market transactions.The initial phase entails establishing the technological framework of the electricity-hydrogen coupling system integrated with P2H.Subsequently,an analysis is conducted to examine the operational mode of EHRES as it participates in carbon-electricity market transactions.Additionally,the system scheduling model includes a stepped carbon trading price mechanism,considering the combined heat and power generation characteristics of the Hydrogen Fuel Cell(HFC).This facilitates the establishment of an optimal operational model for EHRES,aiming to minimize the overall operating cost.The simulation example illustrates that the coordinated operation of EHRES in carbon-electricity market transactions holds the potential to improve renewable energy utilization and reduce the overall system cost.This result carries significant implications for attaining advantages in both low-carbon and economic aspects.
文摘How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data,is struggling to deal with fastchanging markets due to sample inefficiency.This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning(RL)approaches in non-stationary markets for the first time.In our work,the history trading data is divided into multiple task data and for each of these data themarket condition is relatively stationary.Then amodel agnosticmeta-learning(MAML)-based tradingmethod involving a meta-learner and a normal learner is proposed.A trading policy is learned by the meta-learner across multiple task data,which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.To improve the adaptability of the MAML-based method,an ordered multiplestep updating mechanism is also proposed to explore the changing dynamic within a task market.The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%,200%,and 160%,increase the Sharpe ratio by 180%,90%,and 170%,and decrease the maximum drawdown by 30%,20%,and 40%,compared to the traditional RL approach in three stock index future markets,respectively.
基金partly supported by the National Natural Science Foundation of China under Grants No.72171223,No.71801213,and No.71988101the National Key R&D Program of China under Grants No.2021ZD0111204。
文摘Developing effective strategies to earn excess returns in the stock market is a cutting-edge topic in the field of economics.At the same time,stock price forecasting that supports trading strategies is considered one of the most challenging tasks.Therefore,this study analyzes and extracts news media data,expert comments,social opinion data,and pandemic text data using natural language processing,and then combines the data with a deep learning model to forecast future stock price patterns based on historical stock prices.An interval constraint-based trading strategy is constructed.Using data from several typical stocks in the Chinese stock market during the COVID-19 period,the empirical studies and trading simulations show,first,that the sentiment composite index and the deep learning model can improve the accuracy of stock price forecasting.Second,the interval constraint-based trading strategy based on the proposed approach can effectively enhance returns and thus,can assist investors in decision-making.
文摘The extended trading close(ETC)provides institutional investors an opportunity to trade at the closing price after the regular trading session(RTS)and disclosing the order imbalances to other market participants.ETCs exist in the Nasdaq,the SSE STAR,the SZSE ChiNext and the TWSE.To help a risk-averse institutional investor take advantage of the RTS and the ETC for liquidation,we develop a multistage dynamic programming model including the ETC,and derive recursive solutions for the multiple trading days scenario with closed-form solutions for the scenario with only two trading days.We also verify that the ETC is able to mitigate extreme price movements caused by fast liquidation,which is also a goal of the ETC set out by the SSE STAR and the SZSE ChiNext.Finally,we derive three results.First,an institutional investor can reduce execution costs after the introduction of the ETC.Second,a critical trading day exists,and to avoid prematurely revealing trading intentions,the investor should not trade in the ETC until such day.Third,even though the ETC orders submitted by the investor are unfilled,implementation of the ETC encourages the investor to change the liquidation strategy in the RTS,which reduces extreme price movements.In summary,the practical implications of this paper are that the investor should not trade during the ETC on the front few days to avoid prematurely revealing the investor’s trading intention by unfilled orders in the ETC and that introducing the ETC can reduce liquidation costs and extreme price movements.
文摘China has remained Africa’s largest trading partner for 15 consecutive years,with bilateral trade reaching a record$282.1 billion in 2023,Ministry of Commerce official Jiang Wei said at a press conference on 31 January.Economic and trade cooperation is the ballast and propeller of China-Africa relations,he said.
基金supported by the National Natural Science Foundation of China under Grant Nos.72174107,22&ZD094,71701191the Soft Science Research Project of China National Intellectual Property Administration under Grant Nos.SS22-B-15University of science and technology of China(USTC)introduces talents for scientific research and starts special fund project under Grant Nos.KY2160000003.
文摘Green and low carbon transition is a broad and profound economic and social systematic change.Green innovation is a critical way to promote energy saving and emission reduction.Has China continuously promoted a carbon emission trading policy to significantly promote green innovation cooperation?Taking the implementation of the carbon emission trading pilot policy as a“quasi-natural experiment,”this study answers this question by exploring the impact of the policy on green innovation cooperation.Based on data on 274 cities from 2008 to 2020,the multi-time difference-in-differences model is used to evaluate the impact of the policy on green innovation cooperation.The results reveal that the carbon emission trading pilot policy significantly improved inter-and intra-city green innovation cooperation through the upgrading effect of industrial structure and the coverage effect of digital finance compared with the non-pilot cities at the city level.In addition,there are significant differences in the policy effects among cities with different degrees of openness to the outside world and command-and-control environmental regulation.
文摘Price volatility in stock market brings potential profile positions to the traders. How to predict the direction of the stock market or stock price becomes the primary job for traders＇ trading model. We are looking for the direction of the market in a given timeframe. High-frequency traders will consider the potential profile-out position in millisecond level. Long-term holder will look into month time scale. For most of average traders, the ideal timeframe will be on daily base. In this paper, for a non-news trading day, the author will introduce statistics method to predict the stock prices and bid-ask spread for day trading.
文摘This paper explores the effects of China’s global value chain(GVC)participation on technological progress in trading-partner countries based on estimated data on value-added trade between China and 52 trading partners.We find that,first,although China’s exports lowered the total factor productivity(TFP)of its trading partners(competitive effect),its imports greatly increased trading partners’TFP(effect of scale).This implies that China’s GVC participation is beneficial to its trading partners’technological progress in the form of a considerable technology dividend effect.Second,China’s export dividend effect compensates for the negative effect of Chinese competition on trading partners’technological progress;the innovation effects attributable to China’s imports reinforce the positive effects of scale on technological progress.When innovation is factored in,the China dividend thus becomes further reinforced.Third,China’s merchandise imports have a diminishing positive effect on technological progress in trading partners as geographical distance increases,but trade in services transcends geographical boundaries,and the positive technological progress effect of China’s service imports do not diminish as distance increases.We find that the“China dividend”from China’s GVC participation is a significant contributor to technological progress in partner nations,and China’s imports are conducive to innovation and technological progress in developed countries in the long run.
