This study investigates the relationship between trading volume and returns in SET50 index Futures market in the period from April 2006 to December 2008 using 653 observations. From previous studies, we include three ...This study investigates the relationship between trading volume and returns in SET50 index Futures market in the period from April 2006 to December 2008 using 653 observations. From previous studies, we include three methodologies namely the GARCH model, the Generalized Method of Moments （GMM） to estimate systems of equations and the Granger causality test to investigate the relationship more thoroughly. In addition, we introduce the lagged volume as a new explanatory variable in the GARCH model. Overall, the results show the significant contemporaneous and dynamic relationships between trading volume and returns volatility which support the sequential information arrival hypothesis and imply some degree of market inefficiency. The results from this study also show that past information of trading volume can be used to improve the prediction of price volatility. Therefore, regulators and traders could include past information of trading volume of SET50 index futures in tracking and monitoring the market volatility level and the investment risk in order to make a timely decision.展开更多
To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that ...To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that integrates the laddered carbon trading mechanism with demand response.Firstly,a dual dimensional DR model is constructed based on the characteristics of load elasticity.The alternativeDRenables flexible substitution of energy loads through complementary conversion of electricity/heat/cold multi-energy sources,while the price DR relies on timeof-use electricity price signals to guide load spatiotemporal migration;Secondly,the LCT mechanism is introduced to achieve optimal carbon emission costs through a tiered carbon quota allocation mechanism.On this basis,an optimization decision model is established with the core objective of maximizing the annual net profit of the park.The objective function takes into account energy sales revenue,generator unit costs,and investment and operation costs of multiple types of energy storage facilities.Themodel constraint system covers three key dimensions:dynamic operation constraints of power generation units,including unit output limits,ramping capability,and minimum start-stop time;the physical boundary of an electric/hot/cold multi-energy storage system involves energy storage capacity and charge/discharge efficiency;The multi-energy network coupling balance equation ensures that the energy conversion and transmission process satisfies the law of conservation of energy.Using CPLEX mathematical programming solver for simulation verification,construct an energy storage capacity configuration decision process that includes LCT-DR synergistic effect.The research results show that compared with the traditional single energy storage configuration mode,this strategy effectively enhances the economic feasibility and engineering practicality of industrial park operation by coordinating demand side resource scheduling and finely controlling carbon costs,while maintaining stable system operation.Its methodological framework provides a technical path that combines theoretical rigor and practical operability for the low-carbon transformation of regional integrated energy systems.展开更多
To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon...To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.展开更多
Although the benefits of auditing are uncontroversial in developed markets,there is scant evidence about its effect in emerging economies.Auditing derives its value by increasing the credibility of financial statement...Although the benefits of auditing are uncontroversial in developed markets,there is scant evidence about its effect in emerging economies.Auditing derives its value by increasing the credibility of financial statements,which in turn increases investors’reliance on them in developed markets.Financial statement information is common to all investors and therefore increased reliance on it should reduce divergence in investors’assessment of firm value.We examine the effect of interim auditing on inter-investor divergence with a large sample of listed Chinese firms and find that it decreases more for firms whose reports are audited compared to non-audited firms.This finding suggests that investors rely more on audited financial information.Results of this study are robust to variations in event window length and specification of empirical measures.展开更多
Various information types and rational learning methods have shown that heterogeneous belief changes in a rational expectation model can explain many empirical findings in stock markets, such as momentum, contrarians,...Various information types and rational learning methods have shown that heterogeneous belief changes in a rational expectation model can explain many empirical findings in stock markets, such as momentum, contrarians, and technical trading. The methods have also shown that momentum and price movements can coexist in an asset market with only rational agents. The purpose of this paper is to provide a rational economic theory to explain these phenomena. Results of a dynamic programming model with heterogeneous beliefs show that the dynamic interactions between information diffusion and belief changes create continuation and reversals. The duration and magnitude of momentum and price movements are associated with trading volume. Therefore, rational investors should incorporate price and volume information in their trading decisions.展开更多
In this paper, we develop a theoretical model to describe the dynamics of the trading volume under continuous double auction mechanism in limit order markets. We examine the formation process and statistical properti...In this paper, we develop a theoretical model to describe the dynamics of the trading volume under continuous double auction mechanism in limit order markets. We examine the formation process and statistical properties （including the mean, wriance, and realized value） of the buy side cumulative trading volume, sell side cumulative trading volume and total cumulative volume under continuous double auction mechanism by means of mathematical modeling based on Poisson process of order flows, and do some corresponding numerical simulations and comparative statics on the factors that would influence these three volumes aforementioned. The results indicate that these three volumes are all influenced by the factors including the arrival rate of orders, demands of each order, proportional structure between buy and sell orders, executed probability and time interval we examined. And our established theoretical model can well capture the dynamics of these three volumes under continuous double auction mechanism in limit order markets when all these factors interact.展开更多
Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading act...Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.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
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
The prices of lithium carbonate futures domestically have gone downward rapidly since mid-November this year,and the fierce battle between active stock buyers and active stock sellers has driven the main contract turn...The prices of lithium carbonate futures domestically have gone downward rapidly since mid-November this year,and the fierce battle between active stock buyers and active stock sellers has driven the main contract turnover of lithium futures to a record high since its listing in July,leading to trading limitation measures taken by the exchange on them to reduce daily trading frequencies.展开更多
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
Salience theory has been proposed as a new stock trading strategy.To assess the validity of this proposal,a complex decision trading system was constructed based on salience theory,a variational mode decomposition(VMD...Salience theory has been proposed as a new stock trading strategy.To assess the validity of this proposal,a complex decision trading system was constructed based on salience theory,a variational mode decomposition(VMD)model,a bidirectional gated recurrent unit(BiGRU)model,and high-frequency trading.The system selected 30 Chinese new energy concept stocks,ranked the stocks using salience theory,and selected the top and bottom three stocks for two portfolios.Twelve stages were established,following which the VMD and BiGRU models were applied to the predictions.The final predicted annualized returns for the high ST(salience theory value)group A(GA)and low ST group B(GB)were 194.06%and 165.88%,respectively.This finding validates the powerful utility of salience theory and deep learning to analyze the Chinese new energy market.Moreover,it explains the theoretical practicality issues that the short selling restriction is the essential reason,or even perhaps the only reason,that leads to the strength of salience theory.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
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
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
As an important carbon sink resource,urban park green space plays a key role in carbon absorption and storage in the carbon cycle.The existing assessment models provide the basis for evaluating carbon sink potential o...As an important carbon sink resource,urban park green space plays a key role in carbon absorption and storage in the carbon cycle.The existing assessment models provide the basis for evaluating carbon sink potential of urban park green spaces,but it is still necessary to explore a new index system suitable for it.From the perspective of policy environment,relevant cases and policy guidance at home and abroad all support the inclusion of urban park green spaces in carbon sink trading,and the price trend of carbon sink market is good,but it is necessary to balance maintenance costs and benefits.However,technical barriers such as data collection and monitoring still exist,and technological innovation is needed to break through.At the same time,the improvement of social awareness is also crucial,which needs to be achieved through education and publicity and the design of social participation mechanism.In the implementation path,the key steps should be clear in the short term,and the expected results should be displayed.In the long term,the continuous improvement mechanism should be established,and future goals should be set.展开更多
文摘This study investigates the relationship between trading volume and returns in SET50 index Futures market in the period from April 2006 to December 2008 using 653 observations. From previous studies, we include three methodologies namely the GARCH model, the Generalized Method of Moments （GMM） to estimate systems of equations and the Granger causality test to investigate the relationship more thoroughly. In addition, we introduce the lagged volume as a new explanatory variable in the GARCH model. Overall, the results show the significant contemporaneous and dynamic relationships between trading volume and returns volatility which support the sequential information arrival hypothesis and imply some degree of market inefficiency. The results from this study also show that past information of trading volume can be used to improve the prediction of price volatility. Therefore, regulators and traders could include past information of trading volume of SET50 index futures in tracking and monitoring the market volatility level and the investment risk in order to make a timely decision.
基金funded by Science and Technology Projects from State Grid Corporation of China,(Research on Adaptive Balance Optimization and Simulation Technology of Industrial community Energy System with High Proportion of Distributed Energy,No.:5100-202355752A-3-4-SY).
文摘To achieve the goals of sustainable development of the energy system and the construction of a lowcarbon society,this study proposes a multi-energy storage collaborative optimization strategy for industrial park that integrates the laddered carbon trading mechanism with demand response.Firstly,a dual dimensional DR model is constructed based on the characteristics of load elasticity.The alternativeDRenables flexible substitution of energy loads through complementary conversion of electricity/heat/cold multi-energy sources,while the price DR relies on timeof-use electricity price signals to guide load spatiotemporal migration;Secondly,the LCT mechanism is introduced to achieve optimal carbon emission costs through a tiered carbon quota allocation mechanism.On this basis,an optimization decision model is established with the core objective of maximizing the annual net profit of the park.The objective function takes into account energy sales revenue,generator unit costs,and investment and operation costs of multiple types of energy storage facilities.Themodel constraint system covers three key dimensions:dynamic operation constraints of power generation units,including unit output limits,ramping capability,and minimum start-stop time;the physical boundary of an electric/hot/cold multi-energy storage system involves energy storage capacity and charge/discharge efficiency;The multi-energy network coupling balance equation ensures that the energy conversion and transmission process satisfies the law of conservation of energy.Using CPLEX mathematical programming solver for simulation verification,construct an energy storage capacity configuration decision process that includes LCT-DR synergistic effect.The research results show that compared with the traditional single energy storage configuration mode,this strategy effectively enhances the economic feasibility and engineering practicality of industrial park operation by coordinating demand side resource scheduling and finely controlling carbon costs,while maintaining stable system operation.Its methodological framework provides a technical path that combines theoretical rigor and practical operability for the low-carbon transformation of regional integrated energy systems.
基金supported by the Key Project of Shanghai(Project Number A1-0224-25-002-02-040,Municipal Key Course—Heat Transfer)funded by the National Natural Science Foundation of China(Grant No.52077137).
文摘To enhance the low-carbon economic efficiency and increase the utilization of renewable energy within integrated energy systems(IES),this paper proposes a low-carbon dispatch model integrating power-to-gas(P2G),carbon capture and storage(CCS),hydrogen fuel cell(HFC),and combined heat and power(CHP).The P2G process is refined into a two-stage structure,and HFC is introduced to enhance hydrogen utilization.Together with CCS and CHP,these devices form a multi-energy conversion system coupling electricity,heat,cooling,and gas.A laddertype carbon trading approach is adopted to flexibly manage carbon output by leveraging marginal cost adjustments.To evaluate the resilience of the proposed low-carbon scheduling strategy involving multiple energy units under the variability of renewable energy,a two-level robust optimization framework is developed.This model captures the most adverse scenarios of wind and solar generation.The dispatch strategy is validated against these extreme conditions to demonstrate its flexibility and effectiveness.The problem is solved using the GUROBI optimization tool.Results from simulations indicate that themodel increases renewable energy integration by 39.1%,and achieves reductions of 15.96%in carbon emissions and 16.29%in operational expenditures.The results demonstrate that the strategy ensures both economic efficiency and environmental performance under uncertain conditions.Compared with existing studies that separately model two-stage P2G or CCS devices,this paper integrates HFC,CHP,and CCS into a unified dispatchable system,enabling refined hydrogen utilization and flexible carbon circulation.Furthermore,the introduction of a laddertype carbon pricing mechanism,combined with multi-energy storage participation in implicit demand response,creates a dynamic and cost-sensitive dispatch framework.These modeling strategies go beyond conventional linear IES formulations and provide more realistic system representations.The proposed approach not only deepens the coupling among electric,thermal,and gas systems,but also offers a feasible pathway for high-penetration renewable integration in low-carbon energy systems.
文摘Although the benefits of auditing are uncontroversial in developed markets,there is scant evidence about its effect in emerging economies.Auditing derives its value by increasing the credibility of financial statements,which in turn increases investors’reliance on them in developed markets.Financial statement information is common to all investors and therefore increased reliance on it should reduce divergence in investors’assessment of firm value.We examine the effect of interim auditing on inter-investor divergence with a large sample of listed Chinese firms and find that it decreases more for firms whose reports are audited compared to non-audited firms.This finding suggests that investors rely more on audited financial information.Results of this study are robust to variations in event window length and specification of empirical measures.
文摘Various information types and rational learning methods have shown that heterogeneous belief changes in a rational expectation model can explain many empirical findings in stock markets, such as momentum, contrarians, and technical trading. The methods have also shown that momentum and price movements can coexist in an asset market with only rational agents. The purpose of this paper is to provide a rational economic theory to explain these phenomena. Results of a dynamic programming model with heterogeneous beliefs show that the dynamic interactions between information diffusion and belief changes create continuation and reversals. The duration and magnitude of momentum and price movements are associated with trading volume. Therefore, rational investors should incorporate price and volume information in their trading decisions.
文摘In this paper, we develop a theoretical model to describe the dynamics of the trading volume under continuous double auction mechanism in limit order markets. We examine the formation process and statistical properties （including the mean, wriance, and realized value） of the buy side cumulative trading volume, sell side cumulative trading volume and total cumulative volume under continuous double auction mechanism by means of mathematical modeling based on Poisson process of order flows, and do some corresponding numerical simulations and comparative statics on the factors that would influence these three volumes aforementioned. The results indicate that these three volumes are all influenced by the factors including the arrival rate of orders, demands of each order, proportional structure between buy and sell orders, executed probability and time interval we examined. And our established theoretical model can well capture the dynamics of these three volumes under continuous double auction mechanism in limit order markets when all these factors interact.
文摘Using daily BTC-USD data from September 19,2014 to January 21,2024,this paper re-examines whether weekends differ from weekdays for Bitcoin along three margins:average returns,close-to-close volatility,and trading activity.We implement Welch mean comparisons and HAC-robust OLS with month fixed effects(bandwidths 5,7,and 14).In the full sample and across subsamples(2016–2019;2020–2023;early 2024),we find no detectable weekend–weekday gap in average returns,while volatility and trading activity are lower on weekends.The patterns are robust to using squared returns as a volatility proxy.The joint evidence is consistent with liquidity and attention mechanisms—quieter weekends rather than compensating return premia.Replication files reproduce all tables and figures.
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 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.
文摘The prices of lithium carbonate futures domestically have gone downward rapidly since mid-November this year,and the fierce battle between active stock buyers and active stock sellers has driven the main contract turnover of lithium futures to a record high since its listing in July,leading to trading limitation measures taken by the exchange on them to reduce daily trading frequencies.
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 Natural Science Foundation of China(Grant Nos.72032006 and 92146005).
文摘Salience theory has been proposed as a new stock trading strategy.To assess the validity of this proposal,a complex decision trading system was constructed based on salience theory,a variational mode decomposition(VMD)model,a bidirectional gated recurrent unit(BiGRU)model,and high-frequency trading.The system selected 30 Chinese new energy concept stocks,ranked the stocks using salience theory,and selected the top and bottom three stocks for two portfolios.Twelve stages were established,following which the VMD and BiGRU models were applied to the predictions.The final predicted annualized returns for the high ST(salience theory value)group A(GA)and low ST group B(GB)were 194.06%and 165.88%,respectively.This finding validates the powerful utility of salience theory and deep learning to analyze the Chinese new energy market.Moreover,it explains the theoretical practicality issues that the short selling restriction is the essential reason,or even perhaps the only reason,that leads to the strength of salience theory.
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 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 Jiangxi Forestry Science and Technology Innovation Project(Innovation Project[2025]16)the Forestry Science and Technology Promotion Demonstration Project of Central Finance(JXTG[2025]19).
文摘As an important carbon sink resource,urban park green space plays a key role in carbon absorption and storage in the carbon cycle.The existing assessment models provide the basis for evaluating carbon sink potential of urban park green spaces,but it is still necessary to explore a new index system suitable for it.From the perspective of policy environment,relevant cases and policy guidance at home and abroad all support the inclusion of urban park green spaces in carbon sink trading,and the price trend of carbon sink market is good,but it is necessary to balance maintenance costs and benefits.However,technical barriers such as data collection and monitoring still exist,and technological innovation is needed to break through.At the same time,the improvement of social awareness is also crucial,which needs to be achieved through education and publicity and the design of social participation mechanism.In the implementation path,the key steps should be clear in the short term,and the expected results should be displayed.In the long term,the continuous improvement mechanism should be established,and future goals should be set.
