This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period...This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period should satisfy the requirements of power industry restructuring.Therefore, it is necessary to set up an appropriate pricing mechanism and system including thelinks of sales price to network, transmission and distribution price (T&D price) and sales price.In the light of various factors influencing increase and decrease in price, a forecast of electricitytariff is given in the five years to come.[展开更多
The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they h...The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.展开更多
In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hour...In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.展开更多
AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives ...AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.展开更多
We often hear statements like“the market raises expectations for central bank interest rate cuts,resulting in higher commodity prices”.Given the current situation,the People’s Bank of China might adopt a more accom...We often hear statements like“the market raises expectations for central bank interest rate cuts,resulting in higher commodity prices”.Given the current situation,the People’s Bank of China might adopt a more accommodative monetary policy to mitigate the impact of the China-U.S.trade friction.Will this further easing of the monetary environment lead to an increase in natural gas prices?展开更多
At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since th...At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.展开更多
Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attent...Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.展开更多
Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal pric...Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal prices from London Metal Exchange(LME).The results show that LME nonferrous metals prices still have a greater impact on Chinese nonferrous metals prices.However,the impact of Chinese nonferrous metals prices on LME nonferrous metals prices is still weak except for lead price.The co-movement of nonferrous metal prices between LME and China presents hysteretic nature,and it lasts for 7-8trading days.Furthermore,the co-movement between LME nonferrous metals prices and Chinese nonferrous metals prices has the characteristics of time-varying,and the correlation of lead prices between LME and China is the more stable than all other nonferrous metals prices.展开更多
Tungsten current price was transformed yearly to its constant price since 1900, which is roughly decomposed into four components as trend, cycle, impact and random. The core prices, consisting of the trend and the cyc...Tungsten current price was transformed yearly to its constant price since 1900, which is roughly decomposed into four components as trend, cycle, impact and random. The core prices, consisting of the trend and the cycle, present regularities that a long-run cycle is embedded within two major cycles, and major cycle is composed of low-price period and high-price period, along with the rapid rise into a tower, and along with deep down into next trough; three sharply upward shocks occur by the events in a tower. Fluctuations in prices trend to slow cycles and expand the bands. It can be expected that tungsten price will highly stand over 17 a, and is is a advice that reducing production and restricting export maybe maintain a high price level.展开更多
From the perspective of long-term and short-term, the methods of TY causality test, generalized impulse response function, variance decomposition were used to investigate the impacts of international oil prices and ma...From the perspective of long-term and short-term, the methods of TY causality test, generalized impulse response function, variance decomposition were used to investigate the impacts of international oil prices and macroeconomic variables on Chinese gold, silver and platinum prices, but also the feedback effects of Chinese precious metal prices under this impact. The results show that international oil prices play an important role in precious metal price variation both in long-term and short-term, and exchange rate only has an effect in short-term, while interest rate is ineffective in predicting precious metal prices. In addition, precious metal prices have some feedback effects on international oil prices and interest rate in short-term.展开更多
Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,...Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.展开更多
On April 2,the United States announced the implementation of the so-called“reciprocal tariffs”plan.Combined with factors such as the OPEC+plan to increase production starting in May,this led to a continuous plunge i...On April 2,the United States announced the implementation of the so-called“reciprocal tariffs”plan.Combined with factors such as the OPEC+plan to increase production starting in May,this led to a continuous plunge in the benchmark oil prices of WTI and Brent over the subsequent three trading days.Despite the significant impact of the United States’“reciprocal tariffs”plan on the global political and economic landscape,the fundamental dynamics of supply and demand remain the decisive factors in the fluctuations of international oil prices.The current trend of international oil price fluctuations is still primarily driven by the supply side,with both supply and demand factors playing a role.Investment,costs,and resource constraints on the supply side do not allow for a significant increase in crude oil production,while“consumption rigidity”on the demand side does not permit a significant decrease in crude oil demand.As a result,International oil prices are expected to fluctuate in the short term,but a significant decline is unlikely to be sustained in the near to medium term.In this context,Chinese oil companies should focus on four key areas to ensure the security of national oil and gas supplies:first,promoting high-quality increases in domestic oil and gas reserves and production;second,steadily strengthening the acquisition of overseas oil and gas resources;third,continuously driving innovation in oil and gas exploration and development technologies;fourth,enhancing the capacity for domestic oil and gas reserves in an orderly manner.展开更多
This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even br...This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.展开更多
Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that ...Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.展开更多
Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which...Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which in turn stimulated an increase in sales.This study aimed to assess the impact of this policy on the pricing,utilization,and overall expenditure of targeted lung cancer therapies included in the NRDL.Using an interrupted time series analysis of procurement data from 698 healthcare institutions,the study evaluated both immediate and long-term effects.In terms of immediate effects,price negotiations resulted in a significant decline in the defined daily dose cost(DDDc)for all targeted therapies(P<0.05).Regarding long-term trends,a significant shift was observed only in the pricing trajectory of Gefitinib,Icotinib,and Ensartinib(P<0.05).In terms of immediate effects on drug utilization,all targeted medicines experienced a substantial increase in volume(P<0.05),except for Gefitinib and Icotinib.Over the long term,the usage of all targeted therapies exhibited a significant upward trend(P<0.05).With respect to expenditure,the immediate impact of NRDL inclusion resulted in a significant increase in spending on Afatinib,Crizotinib,Osimertinib,Alectinib,and Ensartinib(P<0.05).Over time,total spending on targeted medicines showed a significant increase(P<0.05),except for Erlotinib.Overall,NRDL price negotiations successfully reduced the economic burden on lung cancer patients,improving both accessibility and affordability of targeted therapies in China.展开更多
While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean e...While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean energy patents and fossil fuel patents.This study pioneers an investigation into the effects of energy patents and energy prices on renewable energy consumption.The study utilizes data from 2000Q1 to 2023Q4 and,due to the nonlinear nature of the series,applies wavelet quantile-based methods.Specifically,it introduces the wavelet quantile cointegration approach to evaluate cointegration across different quantiles and time horizons,along with the wavelet quantile-on-quantile regression method.The results confirm cointegration across different periods and quantiles,highlighting the significant relationships between energy patents,economic factors,and renewable energy consumption.Furthermore,we found that fossil energy patents negatively affect renewable energy consumption,while clean energy patents have a similar but weaker effect,especially in the short term.In addition,higher energy prices promote renewable energy adoption while economic growth positively influences renewable energy consumption,particularly in the short term.The study formulates specific policies based on these findings.展开更多
The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.G...The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.Grounded in information asymmetry theory,this study investigates the impact of geographical distance on stock price synchronization in the Chinese stock market.Using the data from the Shanghai and Shenzhen Stock Exchanges,we find that a greater geographical distance between mutual funds and firms considerably increases stock price synchronization,highlighting a strong positive relationship.Additional analysis show that firms in the regions with better external and internal governance,benefit more from reduced information asymmetry,than those in less regulated or transparent regions.These results have key implications for institutional investors and policymakers aiming to enhance information dissemination and market integration in China.展开更多
Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit si...Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit significantly affects housing price changes in major Chinese cities.This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research.It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022,assess the impact of credit on housing price fluctuations,and compare differences between first-tier and second-tier cities.Based on these findings,the paper proposes suggestions for regulating housing prices by controlling credit scale,aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.展开更多
Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation ...Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation between stock price and nine key financial indicators selected from three dimensions:profitability,development capability,and operating capability,including fixed asset growth rate,price-to-book ratio(P/B ratio),and gross profit margin.Through correlation analysis,multiple regression analysis,and curve fitting,the study finds that:fixed asset growth rate,P/B ratio,and gross profit margin show a significant positive correlation with stock price;return on equity(ROE),operating income,and accounts receivable turnover days show a significant negative correlation with stock price;earnings per share(EPS)and net profit growth rate do not show a significant correlation with stock price.The research results indicate that the stock price of Bengang Steel Plates Co.Ltd.is greatly affected by its asset scale and market valuation,while some profitability indicators have not been effectively transmitted to the stock price.Finally,countermeasures and suggestions are put forward from the aspects of cost control,technological innovation,market expansion,and financial structure optimization,so as to provide references for corporate operation and investment decisions.展开更多
This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology acros...This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology across full and sub-samples,the findings of this study challenge the conventional assumption of a stable long-term inverse correlation between USDI and GP,thereby validating the hypothesis that their relationship is nonlinear and time-dependent.During periods of heightened geopolitical and economic volatility,both the US dollar and gold function as safe-haven assets,with USDI fluctuations exerting a positive influence on GP.Conversely,under stable market conditions,the US dollar serves as the currency in which gold is denominated,resulting in a negative impact of USDI on GP.Notably,GP also demonstrates bidirectional causality,exhibiting both positive and negative effects on USDI.The analysis reveals that while a general inverse correlation persists between gold and the US dollar,this relationship transitions to positive during surges in global political and economic instability.In light of contemporary developments—including escalating geopolitical rivalries,tepid post-pandemic economic recovery,and elevated US interest rates driven by inflationary pressures—this study posit that the upward trajectory of gold prices retains a robust empirical foundation.展开更多
文摘This paper analyzes the level, characteristics and existing problems of current electricityprice in China. Under the present circumstances the overall orientation of power price reform inthe 10th Five-year Plan period should satisfy the requirements of power industry restructuring.Therefore, it is necessary to set up an appropriate pricing mechanism and system including thelinks of sales price to network, transmission and distribution price (T&D price) and sales price.In the light of various factors influencing increase and decrease in price, a forecast of electricitytariff is given in the five years to come.[
基金supported by the National Research Foundation of Korea grant funded by the Korea government(MSIT)(RS-2025-16067531:Kwangwon Ahn)Hankuk University of Foreign Studies Research Fund(0f 2025:Sihyun An).
文摘The nonlinearity of hedonic datasets demands flexible automated valuation models to appraise housing prices accurately,and artificial intelligence models have been employed in mass appraisal to this end.However,they have been referred to as“blackbox”models owing to difficulties associated with interpretation.In this study,we compared the results of traditional hedonic pricing models with those of machine learning algorithms,e.g.,random forest and deep neural network models.Commonly implemented measures,e.g.,Gini importance and permutation importance,provide only the magnitude of each explanatory variable’s importance,which results in ambiguous interpretability.To address this issue,we employed the SHapley Additive exPlanation(SHAP)method and explored its effectiveness through comparisons with traditionally explainable measures in hedonic pricing models.The results demonstrated that(1)the random forest model with the SHAP method could be a reliable instrument for appraising housing prices with high accuracy and sufficient interpretability,(2)the interpretable results retrieved from the SHAP method can be consolidated by the support of statistical evidence,and(3)housing characteristics and local amenities are primary contributors in property valuation,which is consistent with the findings of previous studies.Thus,our novel methodological framework and robust findings provide informative insights into the use of machine learning methods in property valuation based on the comparative analysis.
基金funded in part by Grant No.DF-091-135-1441 from the Deanship of Scientific Research(DSR)at King Abdulaziz University in Saudi Arabia.
文摘In this paper,we propose STPLF,which stands for the short-term forecasting of locational marginal price components,including the forecasting of non-conforming hourly net loads.The volatility of transmission-level hourly locational marginal prices(LMPs)is caused by several factors,including weather data,hourly gas prices,historical hourly loads,and market prices.In addition,variations of non-conforming net loads,which are affected by behind-the-meter distributed energy resources(DERs)and retail customer loads,could have a major impact on the volatility of hourly LMPs,as bulk grid operators have limited visibility of such retail-level resources.We propose a fusion forecasting model for the STPLF,which uses machine learning and deep learning methods to forecast non-conforming loads and respective hourly prices.Additionally,data preprocessing and feature extraction are used to increase the accuracy of the STPLF.The proposed STPLF model also includes a post-processing stage for calculating the probability of hourly LMP spikes.We use a practical set of data to analyze the STPLF results and validate the proposed probabilistic method for calculating the LMP spikes.
基金supported by the grant of the Russian Science Foundation(RSF Code:23-18-01065).
文摘AI-driven fintech industries face critical vulnerabilities from volatile rare earth and metallic mineral prices,geopolitical instability,and inflationary pressures.Sovereign inflation-linked bonds serve as incentives for investors in technological industries,despite the risks associated with rising costs of goods.By analyzing global data(8 September 2020–9 September 2023)via cross-quantilogram,recursive cross-quantilogram and quantile vector autoregressive approaches,this study reveals how Russia–Ukraine geopolitical risk,sovereign inflation–linked bonds,rare earth and metallic mineral prices disrupt AI-driven fintech outputs.Key findings indicate that rising rare earth prices suppress fintech productivity in long-term growth periods,whereas sovereign inflation-linked bonds mitigate short-term inflationary risk.Geopolitical turmoil disproportionately harms fintech outputs during market downturns,with both mineral price volatility and conflict-driven shocks amplifying systemic instability in fintech outputs and sovereign inflation-linked bonds.These results urge policymakers to secure critical mineral supply chains,promote inflation-hedging financial instruments,and foster international cooperation to buffer AI-driven fintech sectors against geopolitical and resource-driven disruptions.
文摘We often hear statements like“the market raises expectations for central bank interest rate cuts,resulting in higher commodity prices”.Given the current situation,the People’s Bank of China might adopt a more accommodative monetary policy to mitigate the impact of the China-U.S.trade friction.Will this further easing of the monetary environment lead to an increase in natural gas prices?
文摘At the beginning of 2025,China’s national carbon market carbon price trend exhibited a continuous unilateral downward trajectory,representing a departure from the overall steady upward trend in carbon prices since the carbon market launched in 2021.The analysis suggests that the primary reason for the recent decline in carbon prices is the reversal of supply and demand dynamics in the carbon market,with increased quota supply amid a sluggish economy.It is expected that downward pressure on carbon prices will persist in the short term,but with more industries being included and continued policy optimization and improvement,a rise in China’s medium-to long-term carbon prices is highly probable.Recommendations for enterprises involved in carbon asset operations and management:first,refining carbon asset reserves and trading strategies;second,accelerating internal CCER project development;third,exploring carbon financial instrument applications;fourth,establishing and improving internal carbon pricing mechanisms;fifth,proactively planning for new industry inclusion.
基金funded by the project supported by the Natural Science Foundation of Heilongjiang Provincial(Grant Number LH2023F033)the Science and Technology Innovation Talent Project of Harbin(Grant Number 2022CXRCCG006).
文摘Stock price prediction is a typical complex time series prediction problem characterized by dynamics,nonlinearity,and complexity.This paper introduces a generative adversarial network model that incorporates an attention mechanism(GAN-LSTM-Attention)to improve the accuracy of stock price prediction.Firstly,the generator of this model combines the Long and Short-Term Memory Network(LSTM),the Attention Mechanism and,the Fully-Connected Layer,focusing on generating the predicted stock price.The discriminator combines the Convolutional Neural Network(CNN)and the Fully-Connected Layer to discriminate between real stock prices and generated stock prices.Secondly,to evaluate the practical application ability and generalization ability of the GAN-LSTM-Attention model,four representative stocks in the United States of America(USA)stock market,namely,Standard&Poor’s 500 Index stock,Apple Incorporatedstock,AdvancedMicroDevices Incorporatedstock,and Google Incorporated stock were selected for prediction experiments,and the prediction performance was comprehensively evaluated by using the three evaluation metrics,namely,mean absolute error(MAE),root mean square error(RMSE),and coefficient of determination(R2).Finally,the specific effects of the attention mechanism,convolutional layer,and fully-connected layer on the prediction performance of the model are systematically analyzed through ablation study.The results of experiment show that the GAN-LSTM-Attention model exhibits excellent performance and robustness in stock price prediction.
基金Project(71073177)supported by the National Natural Science Foundation of ChinaProject(12JJ4077)supported by the Natural Science Foundation of Hunan Province of ChinaProject(2012zzts002)supported by the Fundamental Research Funds of Central South University,China
文摘Using VAR-DCC-GARCH model,the literature on commodity price was extended by exploring the co-movement between Chinese nonferrous metal prices and global nonferrous metal prices represented by the nonferrous metal prices from London Metal Exchange(LME).The results show that LME nonferrous metals prices still have a greater impact on Chinese nonferrous metals prices.However,the impact of Chinese nonferrous metals prices on LME nonferrous metals prices is still weak except for lead price.The co-movement of nonferrous metal prices between LME and China presents hysteretic nature,and it lasts for 7-8trading days.Furthermore,the co-movement between LME nonferrous metals prices and Chinese nonferrous metals prices has the characteristics of time-varying,and the correlation of lead prices between LME and China is the more stable than all other nonferrous metals prices.
基金Project(2013ZK2001)supported by the Major Soft Science Program of Hunan Provice,ChinaProjects(1382ZD024,13BGL105)supported by the National Social Science Foundation of China
文摘Tungsten current price was transformed yearly to its constant price since 1900, which is roughly decomposed into four components as trend, cycle, impact and random. The core prices, consisting of the trend and the cycle, present regularities that a long-run cycle is embedded within two major cycles, and major cycle is composed of low-price period and high-price period, along with the rapid rise into a tower, and along with deep down into next trough; three sharply upward shocks occur by the events in a tower. Fluctuations in prices trend to slow cycles and expand the bands. It can be expected that tungsten price will highly stand over 17 a, and is is a advice that reducing production and restricting export maybe maintain a high price level.
基金Project(13&ZD169)supported by the Major Program of the National Social Science Foundation,ChinaProject(13YJAZH149)supported by Research Project in Humanities and Social Sciences Conducted by the Ministry of Education,China+2 种基金Project(2011ZK2043)supported by the Key Program of the Soft Science Research Project of Hunan Province,ChinaProject(2015JJ2182)supported by Natural Science Foundation of Hunan Province of ChinaProject(2009JYJR035)supported by Emergency Project "The Study of International Financial Crisis" of Ministry of Education of China
文摘From the perspective of long-term and short-term, the methods of TY causality test, generalized impulse response function, variance decomposition were used to investigate the impacts of international oil prices and macroeconomic variables on Chinese gold, silver and platinum prices, but also the feedback effects of Chinese precious metal prices under this impact. The results show that international oil prices play an important role in precious metal price variation both in long-term and short-term, and exchange rate only has an effect in short-term, while interest rate is ineffective in predicting precious metal prices. In addition, precious metal prices have some feedback effects on international oil prices and interest rate in short-term.
文摘Global climate change has created substantial difficulties in the areas of sustainability,development,and environmental conservation due to the widespread dependence on fossil fuels for energy production.Nevertheless,the promotion of renewable energy programs has the potential to significantly expedite endeavors aimed at tackling climate change.Thus,it is essential to conduct a thorough analysis that considers the financial aspects to fully understand the main hurdles that are preventing the advancement of renewable energy initiatives.Italy is a leading country in the worldwide deployment of renewable energy.The objective of this research is to assess the impact of financial growth,economic progress,and energy expenses on Italy’s adoption of renewable energy sources.By employing the Auto-Regressive Distributed Lag(ARDL)technique,we analyzed annual data spanning from1990 to 2022.Findings revealed that a 1%increase in financial and economic development would boost renewable energy consumption in the long run by 0.29%and 0.48%,respectively.Instead,a 1%increase in energy prices might reduce consumption of renewable energy by 0.05%in the long run.This study’s primary significance lies in furnishing actionable strategies for Italy to augment green finance for renewable energy,fostering sustained social and economic progress.Moreover,the analytical insights gleaned from this research offer valuable insights for energy-importing nations worldwide.
文摘On April 2,the United States announced the implementation of the so-called“reciprocal tariffs”plan.Combined with factors such as the OPEC+plan to increase production starting in May,this led to a continuous plunge in the benchmark oil prices of WTI and Brent over the subsequent three trading days.Despite the significant impact of the United States’“reciprocal tariffs”plan on the global political and economic landscape,the fundamental dynamics of supply and demand remain the decisive factors in the fluctuations of international oil prices.The current trend of international oil price fluctuations is still primarily driven by the supply side,with both supply and demand factors playing a role.Investment,costs,and resource constraints on the supply side do not allow for a significant increase in crude oil production,while“consumption rigidity”on the demand side does not permit a significant decrease in crude oil demand.As a result,International oil prices are expected to fluctuate in the short term,but a significant decline is unlikely to be sustained in the near to medium term.In this context,Chinese oil companies should focus on four key areas to ensure the security of national oil and gas supplies:first,promoting high-quality increases in domestic oil and gas reserves and production;second,steadily strengthening the acquisition of overseas oil and gas resources;third,continuously driving innovation in oil and gas exploration and development technologies;fourth,enhancing the capacity for domestic oil and gas reserves in an orderly manner.
基金supported by the National Natural Science Foundation of China(Grant No.72401207 and 42101300)Beijing Municipal Education Commission,China(Grant No.SM202110038001).
文摘This paper investigates China's coal price volatility spreaders(CPVSs)from the supply side to locate the volatility source since coal price volatility may destabilize many downstream products'prices or even bring uncertainties to macroeconomic output.Especially in the carbon neutrality context,China's coal market is being reconstructed and responding to imbalances between supply and demand;identifying the CPVSs helps alleviate rising market instability and prevent energy-induced system risk.To achieve this objective,we explore causalities among 938 weekly coal prices reported by different coal-producing areas of China from 2006.9.4 to 2021.7.12 using the transfer entropy method.Then,coal price volatility influence is quantified to identify the CPVSs by conjointly using complex network theory and a rank aggregation method.The validity test demonstrates that the proposed hybrid method efficiently identifies the CPVSs as it correlates to many price determinants,e.g.,electricity and coal consumption and generation.The empirical results show that causalities among coal prices changed dramatically in 2016,2018,and 2020,affected by coal decapacity and carbon neutrality policies.Before 2018,coal-producing provinces with strong demand for coal and electricity,e.g.,Jiangxi,Chongqing,and Sichuan,were CPVSs;after 2019,those with comparative advantages in coal supply,e.g.,Gansu and Ningxia,were CPVSs.Overall,the coal market is unstable and sensitive to energy policy and external shocks.Policymakers and market participants are recommended to monitor and manage the CPVSs to improve energy security,avoid policy-induced instability and prevent risks caused by coal price fluctuations.
文摘Understanding how renewable energy generation affects electricity prices is essential for designing efficient and sustainable electricity markets.However,most existing studies rely on regression-based approaches that capture correlations but fail to identify causal relationships,particularly in the presence of non-linearities and confounding factors.This limits their value for informing policy and market design in the context of the energy transition.To address this gap,we propose a novel causal inference framework based on local partially linear double machine learning(DML).Our method isolates the true impact of predicted wind and solar power generation on electricity prices by controlling for high-dimensional confounders and allowing for non-linear,context-dependent effects.This represents a substantial methodological advancement over standard econometric techniques.Applying this framework to the UK electricity market over the period 2018-2024,we produce the first robust causal estimates of how renewables affect dayahead wholesale electricity prices.We find that wind power exerts a U-shaped causal effect:at low penetration levels,a 1 GWh increase reduces prices by up to£7/MWh,the effect weakens at mid-levels,and intensifies again at higher penetration.Solar power consistently reduces prices at low penetration levels,up to£9/MWh per additional GWh,but its marginal effect diminishes quickly.Importantly,the magnitude of these effects has increased over time,reflecting the growing influence of renewables on price formation as their share in the energy mix rises.These findings offer a sound empirical basis for improving the design of support schemes,refining capacity planning,and enhancing electricity market efficiency.By providing a robust causal understanding of renewable impacts,our study contributes both methodological innovation and actionable insights to guide future energy policy.
基金Research on Innovative Method of Drug Rational Use Supervision Decision Based on Big Data of Medical Insurance(Grant No.82273899)。
文摘Between 2016 and 2024,the Chinese government incorporated several innovative drugs into the National Reimbursement Drug List(NRDL)through price negotiations.These negotiations led to significant price reductions,which in turn stimulated an increase in sales.This study aimed to assess the impact of this policy on the pricing,utilization,and overall expenditure of targeted lung cancer therapies included in the NRDL.Using an interrupted time series analysis of procurement data from 698 healthcare institutions,the study evaluated both immediate and long-term effects.In terms of immediate effects,price negotiations resulted in a significant decline in the defined daily dose cost(DDDc)for all targeted therapies(P<0.05).Regarding long-term trends,a significant shift was observed only in the pricing trajectory of Gefitinib,Icotinib,and Ensartinib(P<0.05).In terms of immediate effects on drug utilization,all targeted medicines experienced a substantial increase in volume(P<0.05),except for Gefitinib and Icotinib.Over the long term,the usage of all targeted therapies exhibited a significant upward trend(P<0.05).With respect to expenditure,the immediate impact of NRDL inclusion resulted in a significant increase in spending on Afatinib,Crizotinib,Osimertinib,Alectinib,and Ensartinib(P<0.05).Over time,total spending on targeted medicines showed a significant increase(P<0.05),except for Erlotinib.Overall,NRDL price negotiations successfully reduced the economic burden on lung cancer patients,improving both accessibility and affordability of targeted therapies in China.
文摘While the significant role of technological innovation in promoting renewable energy has been extensively explored in the literature,limited attention has been paid to the impact of energy patents,particularly clean energy patents and fossil fuel patents.This study pioneers an investigation into the effects of energy patents and energy prices on renewable energy consumption.The study utilizes data from 2000Q1 to 2023Q4 and,due to the nonlinear nature of the series,applies wavelet quantile-based methods.Specifically,it introduces the wavelet quantile cointegration approach to evaluate cointegration across different quantiles and time horizons,along with the wavelet quantile-on-quantile regression method.The results confirm cointegration across different periods and quantiles,highlighting the significant relationships between energy patents,economic factors,and renewable energy consumption.Furthermore,we found that fossil energy patents negatively affect renewable energy consumption,while clean energy patents have a similar but weaker effect,especially in the short term.In addition,higher energy prices promote renewable energy adoption while economic growth positively influences renewable energy consumption,particularly in the short term.The study formulates specific policies based on these findings.
基金supported by the National Natural Science Foundation of China(72141304,72201190).
文摘The effects of geographic factors on information dissemination among investors have been extensively studied;however,the relationship between the geographical distance and stock price synchronization remains unclear.Grounded in information asymmetry theory,this study investigates the impact of geographical distance on stock price synchronization in the Chinese stock market.Using the data from the Shanghai and Shenzhen Stock Exchanges,we find that a greater geographical distance between mutual funds and firms considerably increases stock price synchronization,highlighting a strong positive relationship.Additional analysis show that firms in the regions with better external and internal governance,benefit more from reduced information asymmetry,than those in less regulated or transparent regions.These results have key implications for institutional investors and policymakers aiming to enhance information dissemination and market integration in China.
文摘Fluctuations in real estate prices are closely linked to the macro-economy,exerting a profound influence on social investment and consumption levels.As a key source of funding for the real estate market,bank credit significantly affects housing price changes in major Chinese cities.This paper explores the transmission mechanisms and pathways of bank credit on real estate prices through theoretical analysis and empirical research.It constructs a panel regression model to empirically analyze the relationship between bank credit scale and housing prices in 35 large and medium-sized Chinese cities from 2012 to 2022,assess the impact of credit on housing price fluctuations,and compare differences between first-tier and second-tier cities.Based on these findings,the paper proposes suggestions for regulating housing prices by controlling credit scale,aiming to deepen the understanding of the relationship between bank credit and housing prices and support the stable development of China’s macro-economy and real estate market.
基金Innovation Team Project of Liaoning Institute of Science and Technology:“Smart Economy Practice and Innovation Team”College Students’Innovation and Entrepreneurship Training Program Projects:“Research on the Application of Big Data Analysis Tools”and“Zhice Quantitative Investment Studio”+2 种基金Teaching and Research Project:“Research on the Path of AI-Enabled Undergraduate Education and Teaching Reform Based on the Needs of Liaoning’s Revitalization and Development(Project No.:LKJY202510)”Teaching Reform Project:“Research and Practice on the Evaluation of Digital Talents in Application-Oriented Universities(Project No.:LNKJ202412)”Project of Liaoning Federation of Social Sciences:“Research on the Key Elements and Practical Paths of Educational Digital Transformation(Project No.:2025lslybkt-050)”。
文摘Based on the financial data and stock price information of Bengang Steel Plates Co.Ltd.from 2004 to 2023,this paper uses SPSS 26 software,combined with DuPont Analysis and Wall Score Method,to explore the correlation between stock price and nine key financial indicators selected from three dimensions:profitability,development capability,and operating capability,including fixed asset growth rate,price-to-book ratio(P/B ratio),and gross profit margin.Through correlation analysis,multiple regression analysis,and curve fitting,the study finds that:fixed asset growth rate,P/B ratio,and gross profit margin show a significant positive correlation with stock price;return on equity(ROE),operating income,and accounts receivable turnover days show a significant negative correlation with stock price;earnings per share(EPS)and net profit growth rate do not show a significant correlation with stock price.The research results indicate that the stock price of Bengang Steel Plates Co.Ltd.is greatly affected by its asset scale and market valuation,while some profitability indicators have not been effectively transmitted to the stock price.Finally,countermeasures and suggestions are put forward from the aspects of cost control,technological innovation,market expansion,and financial structure optimization,so as to provide references for corporate operation and investment decisions.
基金Project of National Social Science Fund of China(Project No.:23BGJ010)。
文摘This study examines the dynamic interplay between the US Dollar Index(USDI)and gold prices(GP)to assess the sustainability of gold price trends.Employing a rolling window bootstrapping causality test methodology across full and sub-samples,the findings of this study challenge the conventional assumption of a stable long-term inverse correlation between USDI and GP,thereby validating the hypothesis that their relationship is nonlinear and time-dependent.During periods of heightened geopolitical and economic volatility,both the US dollar and gold function as safe-haven assets,with USDI fluctuations exerting a positive influence on GP.Conversely,under stable market conditions,the US dollar serves as the currency in which gold is denominated,resulting in a negative impact of USDI on GP.Notably,GP also demonstrates bidirectional causality,exhibiting both positive and negative effects on USDI.The analysis reveals that while a general inverse correlation persists between gold and the US dollar,this relationship transitions to positive during surges in global political and economic instability.In light of contemporary developments—including escalating geopolitical rivalries,tepid post-pandemic economic recovery,and elevated US interest rates driven by inflationary pressures—this study posit that the upward trajectory of gold prices retains a robust empirical foundation.
