One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading.At the same time,significant structural changes in the industry have occurred,with pas...One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading.At the same time,significant structural changes in the industry have occurred,with passive investing gaining momentum.The intersection of these two major trends poses special challenges during market downturns,magnifying portfolio losses and leading to significant outflows.Emerging market(EM)investors have seen two major downturn events in the 2020s,namely the COVID-19 pandemic and the Russia-Ukraine conflict,both of which have strongly affected EM portfolios’risk-return profiles and increased their correlations with their developed market counterparts,eliminating much or all of EMs’diversification benefits.This has led to major capital outflows from EM countries,further destabilizing these fragile economies.Against this backdrop,we argue that capital need not exit these riskier markets during periods of turmoil and support this by developing a second-generation Automated Adaptive Trading System(AATS)back-tested on a relevant,diversified EM portfolio that tracks the Morgan Stanley Capital International(MSCI)Emerging Markets Index during a volatile period characterized by negative returns,high risk,and a high correlation with global markets for the buy-and-hold EM portfolio.The system incorporates an Autoregressive Moving Average-Generalized AutoRegressive Conditional Heteroskedasticity model that offers an interpretability advantage over machine-learning methods.The main strength of the AATS is its ability to allow the embedded hybrid forecasting model to adapt to the changing environments that characterize EMs.This is done by implementing a recursive window technique and running a user-specified fitness function to dynamically optimize the mean equation parameters throughout the lead time.Back-testing several configurations of the flexible AATS consistently reveals its superiority while assuring the robustness of the results.We conclude that with the right investment tools,EMs continue to offer compelling opportunities that should not be overlooked.The novel AATS proposed in this study is such a tool,providing active EM investors with substantial value-added through its ability to generate abnormal returns,and can help to enhance the resilience of EMs by mitigating the cost of crises for those countries.展开更多
We investigate high-frequency traders’behavior in the context of the fastest and most extreme price movements(EPMs)that can be observed in the market,specifically ultrafast flash events,challenging the methodologies ...We investigate high-frequency traders’behavior in the context of the fastest and most extreme price movements(EPMs)that can be observed in the market,specifically ultrafast flash events,challenging the methodologies employed in the academic and practitioner literature for identifying sudden liquidity black holes.To refine the price-shock identification methodology,we introduce a new approach called sequence-based flash events(SFEs),which relies on tick sequences instead of predetermined fixed-time intervals within which all flash events in the sample are assumed to occur.This alternative methodology offers the advantage of pinpointing the exact time and duration of a crash,which,in turn,provides a way to more accurately define the observation windows around it.We compare our sample of SFEs with both the so-called“mini flash crashes”,as identified by the Nanex detection algorithm,and the so-called EPMs,as identified by Brogaard et al.(2018).We use close and open prices,as well as high and low prices.Based on our sample of SFEs,we find no evidence that HFTs trigger extreme price shocks.However,we find that HFTs exacerbate SFEs by increasing the net imbalance in the direction of these shocks as they occur.Finally,we show that the choice of the price-shock identification methodology is critical.Thus,we urge regulators to exercise caution and avoid hasty conclusions regarding HFTs’contribution to price stability in stressful market conditions.展开更多
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
This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies.To identify the most suitable pairs and generate trading signals formulated from a reference asset for a...This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies.To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index,the study employs linear and nonlinear cointegration tests,a correlation coefficient measure,and fits different copula families,respectively.The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions,assessing its returns and risks.The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.展开更多
In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the d...In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the dynamic bi-directional relationship between the trading behavior of investor groups(institutional and noninstitutional)and stock index fluctuations in different positions(long and short)and market conditions(the pre-COVID-19 and COVID-19 periods)in the Turkish stock market.The results indicate a bidirectional relationship between the stock index return(SIR)and the trading behavior of online individual traders(OIT)and equity mutual and pension funds(EMPF).However,this relationship varies depending on the trading positions of different investor groups.Also,there is a unidirectional relationship between the SIR and the trading behavior of the diversified equity funds(DEF).During the pandemic period,the role of online traders became more prominent,coinciding with their increased participation in the market,significantly affecting and being affected by stock index fluctuations.We also evaluated some behavioral biases(including overconfidence and asymmetric reaction)and the trading strategy of investor groups(with their performance).Results suggest that the online individual traders were less(more)overconfident than other groups in the prepandemic(pandemic)period.Additionally,all groups had an asymmetric reaction to the positive and negative SIR shocks.This research,contributing to the field of financial innovation and aligning with behavioral finance principles,reveals a fascinating finding:individual investors react to stock index fluctuations,largely driven by institutional investors,despite lacking access to new fundamental information about their portfolio stocks.These findings have significant implications for investors and market regulators.Recognizing and addressing behavioral biases is crucial for individual investors as they strive to make informed and successful financial decisions.It is concluded that the surge in retail investment is a phenomenon;hence,more effort is required for their investment stability in the Turkish stock market.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
This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data s...This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data sampling methods,such as time bars,often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time.To address this,we propose an alternative approach using information-driven sampling methods,including the CUSUM filter,range bars,volume bars,and dollar bars,and evaluate their performance using tick-level data from January 2018 to June 2023.Additionally,we introduce the Triple Barrier method for target labeling,which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction.We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies.The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction,achieving consistently positive trading performance even after accounting for transaction costs.Moreover,our system enables making trading decisions at any point in time on the basis of market conditions,providing an advantage over traditional methods that rely on fixed time intervals.Furthermore,the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder,FEDformer,and Autoformer—alongside other deep learning architectures and classical machine learning models,revealing insights into their relative performance.展开更多
The surge of distributed renewable energy resources has given rise to the emergence of prosumers,facilitating the low-carbon transition of distribution networks.However,flexible prosumers introduce bidirectional power...The surge of distributed renewable energy resources has given rise to the emergence of prosumers,facilitating the low-carbon transition of distribution networks.However,flexible prosumers introduce bidirectional power and carbon interaction,increasing the complexity of practical decision-making in distribution networks.To address these challenges,this paper presents a carbon-coupled network charge-guided bi-level interactive optimization method between the distribution system operator and prosumers.In the upper level,a carbon-emission responsibility settlement method that incorporates the impact of peer-to-peer(P2P)trading is proposed,based on a carbon-emission flow model and optimal power flow model,leading to the formulation of carbon-coupled network charges.In the lower level,a decentralized P2P trading mechanism is developed to achieve the clearing of energy and carbon-emission rights.Furthermore,an alternating direction method of multipliers with an adaptive penalty factor is introduced to address the equilibrium of the P2P electricity–carbon coupled market,and an improved bisection method is employed to ensure the convergence of the bi-level interaction.A case study on the modified IEEE 33-bus system demonstrates the effectiveness of the proposed model and methodology.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
The estimation of the probability of informed trading(PIN)model and its extensions poses significant challenges owing to various computational problems.To address these issues,we propose a novel estimation method call...The estimation of the probability of informed trading(PIN)model and its extensions poses significant challenges owing to various computational problems.To address these issues,we propose a novel estimation method called the expectation-conditional-maximization(ECM)algorithm,which can serve as an alternative to the existing methods for estimating PIN models.Our method provides optimal estimates for the original PIN model as well as two of its extensions:the multilayer PIN model and the adjusted PIN model,along with its restricted versions.Our results indicate that estimations using the ECM algorithm are generally faster,more accurate,and more memory-efficient than the standard methods used in the literature,making it a robust alternative.More importantly,the ECM algorithm is not limited to the models discussed and can be easily adapted to estimate future extensions of the PIN model.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
Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains.In this paper,we introduce an innovative methodology designed to detect wash-trading a...Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains.In this paper,we introduce an innovative methodology designed to detect wash-trading activities beyond surface-level transactions.Our approach integrates NFT ownership traces with the Ethereum Transaction Network,encompassing the complete historical record of all Ethereum-account normal transactions.By analyzing both networks,our method offers a notable advancement over techniques proposed in existing research.We analyzed the wash-trading activity of 7 notable NFT collections.Our results show that wash trading in unregulated NFT markets is an underestimated concern and is much more widespread in terms of both frequency and volume.Excluding the Meebits collection,which emerged as an outlier,wash trading constitutes up to 24%of the total trading volume.Specifically,for the Meebits collection,93%of the total trade volume was attributed to wash trading.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
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
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 th...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.展开更多
Distributed power market trading has the characteristics of large number of participants,scattered locations,small single trading scale,and point-to-point trading.The traditional centralized power trading model has th...Distributed power market trading has the characteristics of large number of participants,scattered locations,small single trading scale,and point-to-point trading.The traditional centralized power trading model has the problems of large load,low efficiency,high cost,reliance on third parties and unreliable data.With the characteristics of decentralization and nontampering,blockchain can establish a point-to-point trusted trading environment and provide effective solutions to the above problems.Therefore,this paper proposed a distributed power market trading framework based on blockchain.In this framework,the distributed power supply characteristics and trading needs of each participant are analyzed,a complete distributed trading process based on blockchain is designed.In addition,we have studied the key technologies of distributed power market trading.With the goal of power service reputation and maximum revenue of distributed power providers,we have established a matching degree model,a distributed power market trading optimization model,and designed a smart contract-based power market trading optimization strategy and power trading settlement strategy.Finally,we designed experiments to verify the performance of the proposed framework.展开更多
This study examines the impact of communication on investors’trading frequency based on a unique dataset drawn from a Chinese social trading platform.We find robust evidence that real-account portfolio owners on the ...This study examines the impact of communication on investors’trading frequency based on a unique dataset drawn from a Chinese social trading platform.We find robust evidence that real-account portfolio owners on the platform trade more frequently under the influence of the comments posted by their leaders(the owners of portfolios they have followed).Moreover,portfolio owners are more sensitive to the quantity than to the tone of leaders’comments.Finally,both trading frequency and leaders’comments negatively impact portfolio owners’future performance.Our find-ings support the notion that social interaction promotes active investment strategies.展开更多
The paper focuses on links between the EU ETS （European Union Emissions Trading Scheme） and selected （domestic） greenhouse gas ETS （emissions trading schemes） from Asia and North America which could open up a pe...The paper focuses on links between the EU ETS （European Union Emissions Trading Scheme） and selected （domestic） greenhouse gas ETS （emissions trading schemes） from Asia and North America which could open up a perspective to keep the idea of emissions trading alive on a global scale and confront the actual uncertainty in future climate policy. The approach consists of investigating qualitatively the essential requirements of this alternative bottom-up approach. It is evaluated if variations or inconsistencies in the structure and design of domestic ETS as well as legal and institutional characteristics harm or facilitate the concept of linking with the EU ETS. The evaluation of systems leads to the exclusion of systems with voluntary character, relative caps, unrestricted borrowing and price caps from the group of potential linking candidates.展开更多
基金funded by the EU’s NextGenerationEU instrument through the National Recovery and Resilience Plan of Romania-Pillar Ⅲ-C9-I8,managed by the Ministry of Research,Innovation and Digitalization,within the project entitled,Non-Gaussian self-similar processes:Enhancing mathematical tools and financial models for capturing complex market dynamics”,contract no.760243/28.12.2023,code CF 194/31.07.2023’.
文摘One of the most notable developments in the asset management industry in recent decades has been the growth of algorithmic trading.At the same time,significant structural changes in the industry have occurred,with passive investing gaining momentum.The intersection of these two major trends poses special challenges during market downturns,magnifying portfolio losses and leading to significant outflows.Emerging market(EM)investors have seen two major downturn events in the 2020s,namely the COVID-19 pandemic and the Russia-Ukraine conflict,both of which have strongly affected EM portfolios’risk-return profiles and increased their correlations with their developed market counterparts,eliminating much or all of EMs’diversification benefits.This has led to major capital outflows from EM countries,further destabilizing these fragile economies.Against this backdrop,we argue that capital need not exit these riskier markets during periods of turmoil and support this by developing a second-generation Automated Adaptive Trading System(AATS)back-tested on a relevant,diversified EM portfolio that tracks the Morgan Stanley Capital International(MSCI)Emerging Markets Index during a volatile period characterized by negative returns,high risk,and a high correlation with global markets for the buy-and-hold EM portfolio.The system incorporates an Autoregressive Moving Average-Generalized AutoRegressive Conditional Heteroskedasticity model that offers an interpretability advantage over machine-learning methods.The main strength of the AATS is its ability to allow the embedded hybrid forecasting model to adapt to the changing environments that characterize EMs.This is done by implementing a recursive window technique and running a user-specified fitness function to dynamically optimize the mean equation parameters throughout the lead time.Back-testing several configurations of the flexible AATS consistently reveals its superiority while assuring the robustness of the results.We conclude that with the right investment tools,EMs continue to offer compelling opportunities that should not be overlooked.The novel AATS proposed in this study is such a tool,providing active EM investors with substantial value-added through its ability to generate abnormal returns,and can help to enhance the resilience of EMs by mitigating the cost of crises for those countries.
文摘We investigate high-frequency traders’behavior in the context of the fastest and most extreme price movements(EPMs)that can be observed in the market,specifically ultrafast flash events,challenging the methodologies employed in the academic and practitioner literature for identifying sudden liquidity black holes.To refine the price-shock identification methodology,we introduce a new approach called sequence-based flash events(SFEs),which relies on tick sequences instead of predetermined fixed-time intervals within which all flash events in the sample are assumed to occur.This alternative methodology offers the advantage of pinpointing the exact time and duration of a crash,which,in turn,provides a way to more accurately define the observation windows around it.We compare our sample of SFEs with both the so-called“mini flash crashes”,as identified by the Nanex detection algorithm,and the so-called EPMs,as identified by Brogaard et al.(2018).We use close and open prices,as well as high and low prices.Based on our sample of SFEs,we find no evidence that HFTs trigger extreme price shocks.However,we find that HFTs exacerbate SFEs by increasing the net imbalance in the direction of these shocks as they occur.Finally,we show that the choice of the price-shock identification methodology is critical.Thus,we urge regulators to exercise caution and avoid hasty conclusions regarding HFTs’contribution to price stability in stressful market conditions.
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financial support of the grant GAČR 22-19617 S“Modeling the structure and dynamics of energy,commodity,and alternative asset prices.”。
文摘This study introduces a novel pairs trading strategy based on copulas for cointegrated pairs of cryptocurrencies.To identify the most suitable pairs and generate trading signals formulated from a reference asset for analyzing the mispricing index,the study employs linear and nonlinear cointegration tests,a correlation coefficient measure,and fits different copula families,respectively.The strategy’s performance is then evaluated by conducting back-testing for various triggers of opening positions,assessing its returns and risks.The findings indicate that the proposed method outperforms previously examined trading strategies of pairs based on cointegration or copulas in terms of profitability and risk-adjusted returns.
文摘In contrast to previous studies that investigated the impact of the investment groups’trading volume on the volatility of the stock index,this research,inspired by behavioral finance literature,aims to evaluate the dynamic bi-directional relationship between the trading behavior of investor groups(institutional and noninstitutional)and stock index fluctuations in different positions(long and short)and market conditions(the pre-COVID-19 and COVID-19 periods)in the Turkish stock market.The results indicate a bidirectional relationship between the stock index return(SIR)and the trading behavior of online individual traders(OIT)and equity mutual and pension funds(EMPF).However,this relationship varies depending on the trading positions of different investor groups.Also,there is a unidirectional relationship between the SIR and the trading behavior of the diversified equity funds(DEF).During the pandemic period,the role of online traders became more prominent,coinciding with their increased participation in the market,significantly affecting and being affected by stock index fluctuations.We also evaluated some behavioral biases(including overconfidence and asymmetric reaction)and the trading strategy of investor groups(with their performance).Results suggest that the online individual traders were less(more)overconfident than other groups in the prepandemic(pandemic)period.Additionally,all groups had an asymmetric reaction to the positive and negative SIR shocks.This research,contributing to the field of financial innovation and aligning with behavioral finance principles,reveals a fascinating finding:individual investors react to stock index fluctuations,largely driven by institutional investors,despite lacking access to new fundamental information about their portfolio stocks.These findings have significant implications for investors and market regulators.Recognizing and addressing behavioral biases is crucial for individual investors as they strive to make informed and successful financial decisions.It is concluded that the surge in retail investment is a phenomenon;hence,more effort is required for their investment stability in the Turkish stock market.
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support of the University of Warsaw under’New Ideas 3B’competition in POB Ⅲ implemented under the’Excellence Initiative-Research University’Programme.
文摘This paper investigates the optimization of data sampling and target labeling techniques to enhance algorithmic trading strategies in cryptocurrency markets,focusing on Bitcoin(BTC)and Ethereum(ETH).Traditional data sampling methods,such as time bars,often fail to capture the nuances of the continuously active and highly volatile cryptocurrency market and force traders to wait for arbitrary points in time.To address this,we propose an alternative approach using information-driven sampling methods,including the CUSUM filter,range bars,volume bars,and dollar bars,and evaluate their performance using tick-level data from January 2018 to June 2023.Additionally,we introduce the Triple Barrier method for target labeling,which offers a solution tailored for algorithmic trading as opposed to the widely used next-bar prediction.We empirically assess the effectiveness of these data sampling and labeling methods to craft profitable trading strategies.The results demonstrate that the innovative combination of CUSUM-filtered data with Triple Barrier labeling outperforms traditional time bars and next-bar prediction,achieving consistently positive trading performance even after accounting for transaction costs.Moreover,our system enables making trading decisions at any point in time on the basis of market conditions,providing an advantage over traditional methods that rely on fixed time intervals.Furthermore,the paper contributes to the ongoing debate on the applicability of Transformer models to time series classification in the context of algorithmic trading by evaluating various Transformer architectures—including the vanilla Transformer encoder,FEDformer,and Autoformer—alongside other deep learning architectures and classical machine learning models,revealing insights into their relative performance.
基金supported by Institutional Research Fund from Sichuan University(0-1 Innovation Research Project,2023SCUH0002)the Sichuan Science and Technology Program(2024YFHZ0312)+1 种基金the Chengdu Science and Technology Program(2024YF0600012HZ)the National Natural Science Foundation of China(U2166211 and 52177103).
文摘The surge of distributed renewable energy resources has given rise to the emergence of prosumers,facilitating the low-carbon transition of distribution networks.However,flexible prosumers introduce bidirectional power and carbon interaction,increasing the complexity of practical decision-making in distribution networks.To address these challenges,this paper presents a carbon-coupled network charge-guided bi-level interactive optimization method between the distribution system operator and prosumers.In the upper level,a carbon-emission responsibility settlement method that incorporates the impact of peer-to-peer(P2P)trading is proposed,based on a carbon-emission flow model and optimal power flow model,leading to the formulation of carbon-coupled network charges.In the lower level,a decentralized P2P trading mechanism is developed to achieve the clearing of energy and carbon-emission rights.Furthermore,an alternating direction method of multipliers with an adaptive penalty factor is introduced to address the equilibrium of the P2P electricity–carbon coupled market,and an improved bisection method is employed to ensure the convergence of the bi-level interaction.A case study on the modified IEEE 33-bus system demonstrates the effectiveness of the proposed model and methodology.
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 the Scientific and Technological Research Council of Turkey(TUBITAK)[grant no 122K637].
文摘The estimation of the probability of informed trading(PIN)model and its extensions poses significant challenges owing to various computational problems.To address these issues,we propose a novel estimation method called the expectation-conditional-maximization(ECM)algorithm,which can serve as an alternative to the existing methods for estimating PIN models.Our method provides optimal estimates for the original PIN model as well as two of its extensions:the multilayer PIN model and the adjusted PIN model,along with its restricted versions.Our results indicate that estimations using the ECM algorithm are generally faster,more accurate,and more memory-efficient than the standard methods used in the literature,making it a robust alternative.More importantly,the ECM algorithm is not limited to the models discussed and can be easily adapted to estimate future extensions of the PIN model.
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.
基金the European Commission for funding the InnoRenew CoE project(H2020 Grant Agreement 739574)the PHArA-ON project(H2020 Grant Agreement 857188)+3 种基金the SRC-EDIH project(DIGITAL-2021-EDIH-01 call,project number:101083351)the Republic of Slovenia(Investment funding of the Republic of Sloveniathe European Union of the European Regional Development Fund)the Slovenian Research Agency(ARRS),for supporting project number J2-2504.
文摘Wash trading in decentralized markets remains a significant concern magnified by the pseudonymous and public nature of blockchains.In this paper,we introduce an innovative methodology designed to detect wash-trading activities beyond surface-level transactions.Our approach integrates NFT ownership traces with the Ethereum Transaction Network,encompassing the complete historical record of all Ethereum-account normal transactions.By analyzing both networks,our method offers a notable advancement over techniques proposed in existing research.We analyzed the wash-trading activity of 7 notable NFT collections.Our results show that wash trading in unregulated NFT markets is an underestimated concern and is much more widespread in terms of both frequency and volume.Excluding the Meebits collection,which emerged as an outlier,wash trading constitutes up to 24%of the total trading volume.Specifically,for the Meebits collection,93%of the total trade volume was attributed to wash trading.
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.
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.
文摘Distributed power market trading has the characteristics of large number of participants,scattered locations,small single trading scale,and point-to-point trading.The traditional centralized power trading model has the problems of large load,low efficiency,high cost,reliance on third parties and unreliable data.With the characteristics of decentralization and nontampering,blockchain can establish a point-to-point trusted trading environment and provide effective solutions to the above problems.Therefore,this paper proposed a distributed power market trading framework based on blockchain.In this framework,the distributed power supply characteristics and trading needs of each participant are analyzed,a complete distributed trading process based on blockchain is designed.In addition,we have studied the key technologies of distributed power market trading.With the goal of power service reputation and maximum revenue of distributed power providers,we have established a matching degree model,a distributed power market trading optimization model,and designed a smart contract-based power market trading optimization strategy and power trading settlement strategy.Finally,we designed experiments to verify the performance of the proposed framework.
基金National Natural Science Foundation of China(Grant No.7167030951).
文摘This study examines the impact of communication on investors’trading frequency based on a unique dataset drawn from a Chinese social trading platform.We find robust evidence that real-account portfolio owners on the platform trade more frequently under the influence of the comments posted by their leaders(the owners of portfolios they have followed).Moreover,portfolio owners are more sensitive to the quantity than to the tone of leaders’comments.Finally,both trading frequency and leaders’comments negatively impact portfolio owners’future performance.Our find-ings support the notion that social interaction promotes active investment strategies.
文摘The paper focuses on links between the EU ETS （European Union Emissions Trading Scheme） and selected （domestic） greenhouse gas ETS （emissions trading schemes） from Asia and North America which could open up a perspective to keep the idea of emissions trading alive on a global scale and confront the actual uncertainty in future climate policy. The approach consists of investigating qualitatively the essential requirements of this alternative bottom-up approach. It is evaluated if variations or inconsistencies in the structure and design of domestic ETS as well as legal and institutional characteristics harm or facilitate the concept of linking with the EU ETS. The evaluation of systems leads to the exclusion of systems with voluntary character, relative caps, unrestricted borrowing and price caps from the group of potential linking candidates.
