Electricity Carbon Quota Trading Scheme based on Certificateless Signature and Blockchain

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.

Unleashing the Power of Multi-Agent Reinforcement Learning for Algorithmic Trading in the Digital Financial Frontier and Enterprise Information Systems

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.

An Energy Trading Method Based on Alliance Blockchain and Multi-Signature

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.

Impact of correlated private signals on continuous-time insider trading

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.

Optimal Operation Strategy of Electricity-Hydrogen Regional Energy System under Carbon-Electricity Market Trading

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.

“China Shock”or China Dividend?-China GVC Participation’s Effects on Trading Partners’Technological Progress

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.

Trading in Fast-ChangingMarkets withMeta-Reinforcement Learning

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.

China Remains Africa’s Top Trading Partner

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.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

Blockchain-Based Secure and Fair IoT Data Trading System with Bilateral Authorization

These days,data is regarded as a valuable asset in the era of the data economy,which demands a trading platform for buying and selling data.However,online data trading poses challenges in terms of security and fairness because the seller and the buyer may not fully trust each other.Therefore,in this paper,a blockchain-based secure and fair data trading system is proposed by taking advantage of the smart contract and matchmaking encryption.The proposed system enables bilateral authorization,where data trading between a seller and a buyer is accomplished only if their policies,required by each other,are satisfied simultaneously.This can be achieved by exploiting the security features of the matchmaking encryption.To guarantee non-repudiation and fairness between trading parties,the proposed system leverages a smart contract to ensure that the parties honestly carry out the data trading protocol.However,the smart contract in the proposed system does not include complex cryptographic operations for the efficiency of onchain processes.Instead,these operations are carried out by off-chain parties and their results are used as input for the on-chain procedure.The system also uses an arbitration protocol to resolve disputes based on the trading proof recorded on the blockchain.The performance of the protocol is evaluated in terms of off-chain computation overhead and on-chain gas consumption.The results of the experiments demonstrate that the proposed protocols can enable the implementation of a cost-effective data trading system.

Incentive-compatible and budget balanced AGV mechanism for peer-to-peer energy trading in smart grids

Peer-to-peer(P2P)energy trading refers to a type of decentralized transaction,where the energy from distributed energy resources is directly traded between peers.A key challenge in peer-to-peer energy trading is designing a safe,efficient,and transparent trading model and operating mechanism.In this study,we consider a P2P trading environment based on blockchain technology,where prosumers can submit bids or offers without knowing the reports of others.We propose an Arrow-d’Aspremont-Gerard-Varet(AGV)-based mechanism to encourage prosumers to submit their real reserve price and determine the P2P transaction price.We demonstrate that the AGV mechanism can achieve Bayesian incentive compatibility and budget balance.Kernel density estimation(KDE)is used to derive the prior distribution from the historical bid/offer information of the agents.Case studies are carried out to analyze and evaluate the proposed mechanism.Simulation results verify the effectiveness of the proposed mechanism in guiding agents to report the true reserve price while maximizing social welfare.Moreover,we discuss the advantages of budget balance for decentralized trading by comparing the Vickrey-Clarke-Groves(VCG)and AGV mechanisms.

Impact of trading hours extensions on foreign exchange volatility:intraday evidence from the Moscow exchange

Using transaction-level tick-by-tick data of same-and next-day settlement of the Russian Ruble versus the US Dollar exchange rate(RUB/USD)traded on the Moscow Exchange Market during the period 2005–2013,we analyze the impact of trading hours extensions on volatility.During the sample period,the Moscow Exchange extended trading hours three times for the same-day settlement and two times for the next-day settlement of the RUB/USD rate.To analyze the effect of the implementations,various measures of historical and realized volatility are calculated for 5-and 15-min intraday intervals spanning a period of three months both prior to and following trading hours extensions.Besides historical volatility measures,we also examine volume and spread.We apply an autoregressive moving average-autoregressive conditional heteroscedasticity(ARMA-GARCH)model utilizing realized volatility and a trade classification rule to estimate the probability of informed trading.The extensions of trading hours cause a significant increase in both volatility and volume for further analyzing the reasons behind volatility changes.Volatility changes mostly occur after the opening of the market.The length of the extension has a significant positive effect on realized volatility.The results indicate that informed trading increased substantially after the opening for the rate of same-day settlement,whereas this is not observed for next-day settlement.Although trading hours extensions raise opportunities for more transactions and liquidity in foreign exchange markets,they may also lead to higher volatility in the market.Furthermore,this distortion is more significant at opening and midday.A potential explanation for the increased volatility mostly at the opening is that the trading hours extension attracts informed traders rather than liquidity providers.

Foreign exchange trading and management with the stochastic dual dynamic programming method

We present a novel tool for generating speculative and hedging foreign exchange(FX)trading policies.Our solution provides a schedule that determines trades in each rebalancing period based on future currency prices,net foreign account positions,and incoming(outgoing)flows from business operations.To obtain such policies,we construct a multistage stochastic programming(MSP)model and solve it using the stochastic dual dynamic programming(SDDP)numerical method,which specializes in solving high-dimensional MSP models.We construct our methodology within an open-source SDDP package,avoiding implementing the method from scratch.To measure the performance of our policies,we model FX prices as a mean-reverting stochastic process with random events that incorporate stochastic trends.We calibrate this price model on seven currency pairs,demonstrating that our trading policies not only outperform the benchmarks for each currency,but may also be close to ex-post optimal solutions.We also show how the tool can be used to generate more or less conservative strategies by adjusting the risk tolerance,and how it can be used in a vari-ety of contexts and time scales,ranging from intraday speculative trading to monthly hedging for business operations.Finally,we examine the impact of increasing trade policy uncertainty(TPU)levels on our findings.Our findings show that the volatility of currencies from emerging economies rises in comparison to currencies from devel-oped markets.We discover that an increase in the TPU level has no effect on the aver-age profit obtained by our method.However,the risk exposure of the policies increases(decreases)for the group of currencies from emerging(developed)markets.

Resource Trading and Miner Competition in Wireless Blockchain Networks with Edge Computing

To promote the application of edge com-puting in wireless blockchain networks,this paper presents a business ecosystem,where edge comput-ing is introduced to assist blockchain users in imple-menting the mining process.This paper exploits re-source trading and miner competition to enable se-cure and efficient transactions in the presented busi-ness ecosystem.The resource trading problem is for-mulated as a Stackelberg game between miner candi-dates and edge computing servers,where computing,caching,and communication resources are jointly op-timized to maximize the potential profit.Partial of-floading is introduced to further enhance the system performance when compared with the existing work.We analyze the existence and uniqueness of the Nash equilibrium and Stackelberg equilibrium.Based on the optimization result,winners are selected from the set of miner candidates by bidding and constitute the mining network.Simulation results demonstrate that the proposal is able to improve the social welfare of blockchain miners,thus stimulating more blockchain users to join the mining network.

Directed Acyclic Graph Blockchain for Secure Spectrum Sharing and Energy Trading in Power IoT

Peer-to-peer(P2P)spectrum sharing and energy trading are promising solutions to locally satisfy spectrum and energy demands in power Internet of Things(IoT).However,implementation of largescale P2P spectrum sharing and energy trading confronts security and privacy challenges.In this paper,we exploit consortium blockchain and Directed Acyclic Graph(DAG)to propose a new secure and distributed spectrum sharing and energy trading framework in power IoT,named spectrum-energy chain,where a set of local aggregators(LAGs)cooperatively confirm the identity of the power devices by utilizing consortium blockchain,so as to form a main chain.Then,the local power devices verify spectrum and energy micro-transactions simultaneously but asynchronously to form local spectrum tangle and local energy tangle,respectively.Moreover,an iterative double auction based micro transactions scheme is designed to solve the spectrum and energy pricing and the amount of shared spectrum and energy among power devices.Security analysis and numerical results illustrate that the developed spectrum-energy chain and the designed iterative double auction based microtransactions scheme are secure and efficient for spectrum sharing and energy trading in power IoT.

Novel modelling strategies for high‑frequency stock trading data

Full electronic automation in stock exchanges has recently become popular,generat-ing high-frequency intraday data and motivating the development of near real-time price forecasting methods.Machine learning algorithms are widely applied to mid-price stock predictions.Processing raw data as inputs for prediction models(e.g.,data thinning and feature engineering)can primarily affect the performance of the prediction methods.However,researchers rarely discuss this topic.This motivated us to propose three novel modelling strategies for processing raw data.We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks.In these experiments,our strategies often lead to statistically significant improvement in predictions.The three strategies improve the F1 scores of the SVM models by 0.056,0.087,and 0.016,respectively.

Probability of informed trading during the COVID‑19 pandemic:the case of the Romanian stock market

Using data from the Bucharest Stock Exchange,we examine the factors influencing the probability of informed trading(PIN)during February—October 2020,a COVID-19 pandemic period.Based on an unconditional quantile regression approach,we show that PIN exhibit asymmetric dependency with liquidity and trading costs.Furthermore,building a customized database that contains all insider transactions on the Bucharest Stock Exchange,we reveal that these types of orders monotonically increase the infor-mation asymmetry from the 50th to the 90th quantile throughout the PIN distribution.Finally,we bring strong empirical evidence associating the level of information asym-metry to the level of fake news related to the COVID-19 pandemic.This novel result suggests that during episodes when the level of PIN is medium to high(between 15 and 50%),any COVID-19 related news classified as misinformation released during the lockdown period,is discouraging informed traders to place buy or sell orders condi-tioned by their private information.

A Distributed Power Trading Scheme Based on Blockchain and Artificial Intelligence in Smart Grids

As an emerging hot technology,smart grids(SGs)are being employed in many fields,such as smart homes and smart cities.Moreover,the application of artificial intelligence(AI)in SGs has promoted the development of the power industry.However,as users’demands for electricity increase,traditional centralized power trading is unable to well meet the user demands and an increasing number of small distributed generators are being employed in trading activities.This not only leads to numerous security risks for the trading data but also has a negative impact on the cost of power generation,electrical security,and other aspects.Accordingly,this study proposes a distributed power trading scheme based on blockchain and AI.To protect the legitimate rights and interests of consumers and producers,credibility is used as an indicator to restrict untrustworthy behavior.Simultaneously,the reliability and communication capabilities of nodes are considered in block verification to improve the transaction confirmation efficiency,and a weighted communication tree construction algorithm is designed to achieve superior data forwarding.Finally,AI sensors are set up in power equipment to detect electricity generation and transmission,which alert users when security hazards occur,such as thunderstorms or typhoons.The experimental results show that the proposed scheme can not only improve the trading security but also reduce system communication delays.

The special issue:“Financial innovation for Emission Trading Scheme”

Against the existential and ubiquitous threat of climate changes to the environment,human health,and economic well-being,the proliferation of carbon neutrality from both government and private sector recently prevails in order to strengthen preparedness for the future global crisis.More than 70 countries and regions have pledged to achieve the goal of carbon neutrality by 2050(ICAP,2020).While the momentum toward adopting carbon neutrality targets continue to build,a growing body of countries and subnational governments move toward carbon pricing through rolling out,speeding-up,or strengthening the construct of emission trading scheme(ETS)and carbon financial market.Notwithstanding the laudable objectives of ETS to enable emission reduction,the potential of ETS is still largely untapped,largely stemming from poor financial innovation.This special issue is,therefore,aimed at promoting research on the carbon financial innovations,financial derivatives services,the use of Fintech and the optimization on architectures of the ETS such that ETSs could drive the transformation toward low-carbon development path and the achievement of Carbon Neutrality targets.

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.