Trading Market Research Articles

TFR: A Temporal Feature‐Refined Multi‐Stage Carbon Price Forecasting

ABSTRACTAccurate carbon price forecasting is crucial for effective carbon market analysis and decision‐making. We propose a novel Temporal Feature‐Refined (TFR) model to address the challenges of complex dependencies and high noise levels in carbon price time series data. The TFR model integrates Complete Ensemble Empirical Mode Decomposition with Adaptive Noise (CEEMDAN) for signal decomposition, an Autoencoder for feature optimization, and a Temporal Convolutional Network (TCN) for capturing long‐range temporal dependencies. It incorporates both traditional economic factors and unconventional determinants such as air quality, policy uncertainty, and public sentiment. Experiments on the Shanghai carbon trading market demonstrate that the TFR model significantly outperforms existing methods, achieving an 83.96% improvement in MAE over Support Vector Regression (SVR) and up to a 65.56% improvement over Long Short‐Term Memory (LSTM) networks. Further analyses, including comparisons with different decomposition models and ablation studies, confirm the effectiveness of each component and the overall model.

Price Impact for Different Market Models in Cryptocurrency Trading

Price impact in cryptocurrency trading plays a crucial role in understanding market dynamics and liquidity. The study presents a detailed mathematical analysis of price impact across three different market models: constant sum, constant product, and order book. Each model provides a unique perspective on how asset prices are influenced by trade execution, market depth, and available liquidity. By examining these models, the analysis highlights the relationship between trade volume and price changes, offering important insights into how large transactions affect the stability and behavior of prices in various liquidity environments. The results are relevant for traders and investors aiming to optimize their strategies in volatile markets, as well as for regulators seeking to mitigate the systemic risks posed by large-scale trades in the cryptocurrency space.

Research on Current Mining Trade Issues in China

Nowadays, although China's mining trade is expanding, some problems have emerged during the development process, preventing the mining trade from reaching its ideal level. Therefore, this paper mainly discusses several issues that have arisen in China's current mining trade, analyzes the problems existing in China's previous and current mining trade, and examines how these various issues may cause large or small damages to China's mining trade market and even the international trade market. It then specifies in detail what measures should be taken or what laws should be promulgated to improve China's issues in this aspect. China should keep up with the pace of the times, use green technology products as much as possible, so that Chinas mineral products can stand firm in the international market, have significant characteristics, and make competition more advantageous. The research significance of this paper is to help China's mining trade market analyze various factors unfavorable to our country's trade situation, thereby helping our country's mining market get rid of the disadvantages of international trade competition, helping the mining market introduce more international supply chains. This may help achieve good trust in international trade and the introduction of our country's minerals to foreign countries. At the same time, this paper also provides valuable value for China's mining-related personnel and researchers.

WTO Legality Analysis of the European Union's Carbon Border Adjustment Mechanism and China's Response

Now that the EU Carbon Border Adjustment Mechanism (CBAM) has entered into force and is entering a transition period, its compliance with WTO trade rules has been controversial. CBAM's differential treatment of goods from different origins based on their carbon content violates the GATT principle of non-discrimination and may result in WTO members being denied fair, just and equal treatment in international trade. CBAM is a tax on the carbon emissions of some specific imported goods, requiring high-carbon products imported into or exported from the EU to pay the corresponding amount of tax or refund the corresponding carbon emission quotas, thus affecting China's export trade. This study mainly utilizes literature research method and comparative research method. At the international level, analyze whether CBAM can meet the WTO legal requirements to construct international carbon tariff rules separately from the existing WTO rules, affecting the global environment and climate change issues. At the domestic level, the impact of CBAM on China's foreign trade is explored. China should adhere to multilateralism, strengthen international cooperation, continuously develop low-carbon technologies and improve the carbon trading market to cope with the impacts of CBAM. Thus, to the effect of safeguarding international interests and contributing to global development.

Can an energy-consuming rights trading system control environmental inequality?

This article examined the influence of the energy-consuming rights trading system (ERS) on environmental inequality, discussed the influence mechanism through the lenses of industrial structure upgrading and green technology innovation, and analyzed the possible spatial spillover effect of ERS with panel data at the provincial level in China from 2003 to 2021. The results indicate that ERS can significantly control provincial environmental inequality by advancing industrial upgrades and fostering green technology innovation. The controlling effect of ERS on environmental inequality is more obvious in regions with more serious environmental inequality, and the controlling effect on the old industrial base is also greater than on the non-old industrial base. Additionally, ERS has helped control environmental inequality in surrounding provinces. In light of the research findings, a series of policy recommendations have been put forth with the aim of further developing and enhancing China's energy-consuming trading market. Overall, this article presents new empirical evidence of the interplay between the energy consumption trading market and environmental inequality. In addition, it offers new approaches and references for policymakers to improve the energy-consuming trading market and the environmental inequalities.

The Impact of Green Finance Policies on Carbon Emission Trading

In the context of increasingly severe global climate change, the international community's demand for reducing greenhouse gas emissions is urgent. This paper explores the critical role of green finance policies in guiding capital flows to green and low-carbon areas. In order to achieve carbon reduction goals under the global climate change context. The main content of this paper includes how green finance, through green credit and green bonds, promotes the activity and efficiency of carbon emission trading, thus significantly affecting carbon emission intensity. Additionally, it details the policy background, development process, and challenges faced by the carbon emission trading system, including issues such as limited industry coverage and insufficient market liquidity. Finally, it proposes policy recommendations and future prospects, such as improving laws and regulations, promoting technological innovation, and expanding market coverage, and strengthening international cooperation to further optimize the carbon emission trading market and help achieve sustainable development goals.

Assessment of the level of influence of oil exporting countries’ organizations on the world oil market

Oil is one of the significant energy resources for the majority of the world’s states, so many of them monitor and analyze the price of black gold. Currently, there are such organizations of oil exporting countries as OPEC and OPEC+, which for a long time continue to influence not only the world market of this energy resource but also the events taking place in the economic and political spheres of the world. These organizations’ actions have a direct impact on market conditions, policy making, oil price, and the volume of its supply on the market in world trade. The collective responsibility of the member countries for a significant portion of global oil production makes the decisions and actions of OPEC and OPEC+ extremely important for the global oil industry and the economy as a whole. All the above-mentioned points to the relevance of the topic of the study, the objectives of which are to analyze the history of the emergence of the oil exporting countries’ organizations, the dynamics of their development during the history of their existence, as well as to assess the degree of their influence on the oil industry and the global economy as a whole.

Adaptive Hybrid Optimized LSTM Models: A Novel Computational Framework for Algorithmic Financial Trading System

In our modern society, with the development of Internet and information system, pre-programing algorithmic trading strategies for online automatic trading has also flourished, especially in the rapidly fluctuating trading market. Using quantitative trading programs that can automatically trade in response to market conditions has attracted the attention of major financial institutions and governments in the financial market. How to use self-adaptive programs to automatically trade in the ever-changing financial market has become a popular research topic for the development and pursuit of all financial markets in recent years. This research proposes an online self-adaptive trading algorithm that can be applied to financial markets such as stock market, currency markets, cryptocurrency markets, futures markets, etc. In the first part of the algorithm, the simplified swarm optimization will be used to optimize the parameters of the newly proposed flexible grid in this research. Then the data will be imported into the artificial neural network model for training in the latter part, helping the trading model automatically select the appropriate parameters for construction flexible grid corresponding to the market conditions. The greatest contribution of the research is to provide a whole new trading algorithm that can adapt to the dynamic trading market, automatically make suitable adjustments to current trading strategy and place real-time orders. The algorithm controlling both profit and risk, which can seem like a balanced trading algorithm that are robust and profitable.

China's Carbon Trading Market Globalization Strategy: A Conceptual Review

China has once participated in the international carbon market through the Clean Development Mechanism (CDM) faced problems such as falling certified emission reductions (CER) prices and carbon sink fraud, which led to the decline of China's carbon trading market. However, with China's announcement of the goal of peaking carbon emissions before 2030 and achieving carbon neutrality before 2060, China's carbon market has regained vitality and launched China Certified Emission Reductions (CCER) as the main trading product. The study discusses the opportunities and challenges of China in the globalization of the carbon trading market, including key obstacles such as differences in rules and inconsistent carbon absorption measurement methods. At the same time, the paper proposes strategies such as connecting China's carbon trading market with the global market through the Hong Kong platform, strengthening international cooperation, optimizing market mechanisms, and improving technological innovation capabilities to promote the deep integration and coordinated development of China's carbon trading market with the international market. It emphasizes China's position and role in the global carbon trading system in the future, as well as its positive role in promoting global low-carbon transformation and international exchanges and cooperation.

Prediction of ROI Achievements and Potential Maximum Profit on Spot Bitcoin Rupiah Trading Using K-means Clustering and Patterned Dataset Model

Since Satoshi Nakamoto first proposed the idea of bitcoin in 2009, the cryptocurrency and prediction methods for it have grown and changed exceptionally quickly. The Patterned Dataset Model was a valuable tool in earlier studies to explain how changes in the price of Bitcoin affect the movements of other cryptocurrencies in a digital trading market. Three different kinds of datasets are generated by this model: patterned datasets under full conditions, patterned datasets under dropping prices (Crash), and patterned datasets under rising prices (Moon). The K-means approach was then used to cluster these three datasets. Specifically, each dataset was split into two clusters, and the clustering score was determined by utilizing eight unique clustering metrics. Consequently, the best clustering score was found in the patterned dataset in the crash situation. Additionally, from 2022 to 2024, the raw data from this crash-condition-patterned dataset is used to determine the possibility of reaching maximum profit and return on investment (ROI) daily and monthly. According to the calculation results, the range computed over the course of a whole month (30 to 31 days) is significantly larger than the daily range (24 hours multiplied by one month), which represents the most significant profit and ROI attained before the emergence of the first diamond crash level. This research also covers the application of a deep learning model to forecast patterned datasets for crash scenarios that may occur many days in advance. The ConvLSTM2D Model performs better in predicting pattern dataset values for the subsequent crash scenario, according to the hyperparameter comparison between the Gated Recurrent Unit (GRU) Model and the 2D Convolutional Long Short-Term Memory Model.

Research on the impact of carbon trading on enterprises' green technology innovation

By treating the implementation of China's carbon trading policy as a quasi-natural experiment, the study analyzes data from A-share listed companies on China's Shanghai and Shenzhen exchanges between 2010 and 2023. Utilizing a multi-temporal Difference-in-Differences (DID) model, multiple intermediary effect model, moderation effect model and threshold effect model, we probe to verifies whether carbon trading policy can generate innovation incentive effects and investigate the transmission mechanisms. Empirical results show that carbon trading policy are beneficial for the green technology upgrading of enterprises and that this incentivizing effect is persistent. Mechanism analysis reveals that the mediating variables of financing constraints and R&D investment exhibit a chain mediation effect and nonlinear characteristics. Once the thresholds of financing constraints and R&D investment are surpassed, the green innovation effect of carbon trading policy is significantly enhanced. Digital finance has a moderating effect on the chain-multiple mediation transmission mechanism. Heterogeneity analysis indicates that the chain mediation effect is mainly observed in large-scale enterprises and enterprises in central regions. This paper offers theoretical support for evaluating carbon trading policy based on the micro-enterprise level and provides a scientific basis for how to efficiently play the function of carbon trading policy in practice, which helps to enhance the carbon trading policy and is of great practical significance for improving the operational efficiency of the national carbon trading market.

Does official media sentiment matter for the stock market? Evidence from China

We develop a novel official media sentiment index (NegGovOp) for China’ stock market using textual analysis combined with BERT. We find that NegGovOp predicts market return reversals. The return-reversal pattern is concentrated among difficult-to-arbitrage stocks, during recession period and the trading days which are not directly following weekends or holidays. We further find that official media sentiment primarily affects the trading activities of retail investors and significantly predicts market trading volume, market volatility and investor sentiment. Our findings are in line with the behavioral model that official media sentiment contributes to shaping irrational investor sentiment and resulting in temporary mispricing.

The Role and Prospect of Emission Trading in the Optimal Allocation of Environmental Resources

The emission trading system is an important institutional innovation which effectively uses market mechanisms to improve the efficiency of environmental governance, and plays an important role in exploiting the potential of emission reduction for government and enterprise, which would realize the optimal allocation of environmental resources. The study analyses the design mechanism of emission trading system, the crucial parts including verification of emission rights, paid use for resources, emission trading, reserve and control mechanism, as well as management and incentive policies. Thereby, the current problems of emission trading system in China have been found out, including the deficiency of top-level design, ineffective cohesive mechanism between different systems, inactive trading market, and insufficient supporting policy guarantee policy. Based on the development trend of national policies and the practical problems faced by emission trading, the study proposes four prospects for the emission trading efforts. First, the issuance of guidance documents for deepening the emission trading system at the national level is an important foundation for the development of the emission trading system. Second, convergence between the emission rights trading and the emission permit system is the inevitable trend of the development of the system. Third, working on promoting the construction of a trading system dominated by market mechanisms is the focus of the optimal allocation of resources and environment. Last, establishing and improving relevant supporting mechanisms is an important guarantee for the stable development of the emission rights trading market and the efficient allocation of environmental resources.

Exploring the emission spillover effects in production networks under carbon trading market: Insights into complementary and competitive industries

As the division of labor deepens, carbon emissions may easily escape through the intertwined production network and well-connected market. As a result, the effectiveness of carbon trading market can no longer be evaluated solely for the regulated regions or industries. Their spillover effects along production networks are necessary for determining whether the policy simply cuts carbon emissions radically or just drives their transmission. To this end, we regarded the pilot carbon emission trading market (PET) in China, as a quasi-natural experiment and employed the multi-regional input-output model to depict the inter-industry linkages. Its spillover impacts on their complementary (including upstream and downstream) and competitive industries have been examined separately, along with the deep mechanism and its heterogeneous feature. Results show that the PET significantly suppresses carbon emissions in upstream industries, while promoting carbon emissions in competitive ones. Furthermore, the PET reduces emissions in upstream industries mainly by decreasing production scale and improving technological innovation. In contrast, the production scale of the downstream industry is not significantly affected, though its energy structure and carbon emission intensity have been improved to a certain extent. Worse still, competitive industries seize the market share of regulated industries and expand production, leading to an increase in carbon emissions.

Influencing Factors and Prediction of Carbon Trading Market Prices in China via Elliptical Factor Analysis

Influencing Factors and Prediction of Carbon Trading Market Prices in China via Elliptical Factor Analysis

Peer-to-Peer Transactive Energy Trading of Smart Homes/Buildings Contributed by A Cloud Energy Storage System

This paper presents a model for transactive energy management within microgrids (MGs) that include smart homes and buildings. The model focuses on peer-to-peer (P2P) transactive energy management among these homes, establishing a collaborative use of a cloud energy storage system (CESS) to reduce daily energy costs for both smart homes and MGs. This research assesses how smart homes and buildings can effectively utilize CESS while implementing P2P transactive energy management. Additionally, it explores the potential of a solar rooftop parking lot facility that offers charging and discharging services for plug-in electric vehicles (PEVs) within the MG. Controllable and non-controllable appliances, along with air conditioning (AC) systems, are managed by a home energy management (HEM) system to optimize energy interactions within daily scheduling. A linear mathematical framework is developed across three scenarios and solved using General Algebraic Modeling System (GAMS 24.1.2) software for optimization. The developed model investigates the operational impacts and optimization opportunities of CESS within smart homes and MGs. It also develops a transactive energy framework in a P2P energy trading market embedded with CESS and analyzes the cost-effectiveness and arbitrage driven by CESS integration. The results of the comparative analysis reveal that integrating CESS within the P2P transactive framework not only opens up further technical opportunities but also significantly reduces MG energy costs from $55.01 to $48.64, achieving an 11.57% improvement. Results are further discussed.

Deep recurrent Q-network algorithm for carbon emission allowance trading strategy

Against the backdrop of global warming, the carbon trading market is considered as an effective means of emission reduction. With more and more companies and individuals participating in carbon markets for trading, it is of great theoretical and practical significance to help them automatically identify carbon trading investment opportunities and achieve intelligent carbon trading decisions. Based on the characteristics of the carbon trading market, we propose a novel deep reinforcement learning (DRL) trading strategy - Deep Recurrent Q-Network (DRQN). The experimental results show that the carbon allowance trading model based on the DRQN algorithm can provide optimal trading strategies and adapt to market changes. Specifically, the annualized returns for the DRQN algorithm strategy in the Guangdong (GD) and Hubei (HB) carbon markets are 15.43% and 34.75%, respectively, significantly outperforming other strategies. To better meet the needs of the actual implementation scenarios of the model, we analyze the impacts of discount factors and trading costs. The research results indicate that discount factors can provide participants with clearer expectations. In both carbon markets (GD and HB), there exists an optimal discount factor value of 0.4, as both excessively small or large values can have adverse effects on trading. Simultaneously, the government can ensure the fairness of carbon trading by regulating the costs of carbon trading to limit the speculative behavior of participants.

Comparative Analysis of Market Clearing Mechanisms for Peer-to-Peer Energy Market Based on Double Auction

This paper aims to develop an optimisation-based price bid generation mechanism for the sellers and buyers in a double-auction-aided peer-to-peer (P2P) energy trading market. With consumers being prosumers through the continuous adoption of distributed energy resources, P2P energy trading models offer a paradigm shift in energy market operation. Thus, it is essential to develop market models and mechanisms that can maximise the incentives for participation in the P2P energy market. In this sense, the proposed approach focuses on maximising profit at the sellers, as well as maximising cost savings at the buyers. The bids generated from the proposed approach are integrated with three different market clearing mechanisms, and the corresponding market clearing prices are compared. A numerical analysis is performed on a real-life dataset from Ausgrid to demonstrate the bids generated from sellers/buyers, as well as the associated market clearing prices throughout different months of the year. It can be observed that the market clearing prices are lower when the solar generation is higher. The statistical analysis demonstrates that all three market clearing mechanisms can achieve a consistent market clearing price within a range of 5 cents/kWh for 50% of the time when trading takes place.

Investors’ reaction to banning IFRS use by domestic firms in alternative market

AbstractThis paper analyses the regulatory context, i.e., how IFRS preparers investing in Poland react to banning IFRS use by domestic firms in the alternative trading market (NewConnect) after six years of regulatory arbitrage. In other words, it studies their portfolio investment and foreign direct investment (FDI) outward sensitivity to the end of regulatory arbitrage opportunity given by the alternative market’s regulation in the country where accounting law limits IFRS use to regulated markets and business groups where the parent company uses IFRS. The contribution is built on the natural experiment of prohibition of domestic IFRS preparers issuing shares on the alternative (not-regulated and cheaper) trading market after 2012. The originality is delivered by implementing a de facto measure of IFRS adoption by foreign investors to identify a 69% growth rate of IFRS preparers investing in Polish firms in 2012 (317% growth of Swedish investors and 219% of Portuguese) that dropped to 8% in 2013 and 3% in 2018. Thus, we check whether investing decisions consider opportunities to gain funds or attract other investors and build trust through IPOs on the unregulated market dedicated to start-ups and engaged in R&D activity. We use difference-in-differences, GMM dynamic panel-data analysis with Arellano-Bond and Arellano-Bover/Blundel-Bond estimators for the Knowledge-Capital model on portfolio investment, outward FDI overall, and three FDI components based on debt, equity, and earning reinvestment in 2003–2019. We found that banning IFRS use by domestic firms in the alternative market discourages portfolio investments, debt-based FDI flows and FDI earnings reinvestment. Also, total FDI and equity-based FDI from countries with more IFRS preparers have been reduced since 2013 due to the IFRS ban for domestic firms.

A comprehensive review on agricultural greenhouse gas emission reductions in China: opportunities and challenges

China is the world’s largest carbon emitter. Reducing greenhouse gas emissions from agriculture, one of the main sources, is a channel for achieving China’s carbon peaking and carbon neutrality targets on schedule. To clarify the status and prospects of greenhouse gas (GHG) emissions reduction in Chinese agriculture, this paper focuses on macro-level factors such as policies, economy, society, and technology, meso-level factors such as agricultural digital transformation and carbon trading market construction, and micro-level factors such as individual farmers and household influences on agricultural emissions reduction. Therefore, this article examines the strengths, weaknesses, opportunities, and threats in China’s agricultural greenhouse emission reductions by SWOT. It is concluded that optimized industrial structure and technological progress are strengths, while weak low-carbon awareness among farmers and the limitations of traditional small-scale production methods are weaknesses. Aligning with national development direction, absorbing foreign experiences, and integrating with digitization are opportunities. The imbalance between the economy and emission reduction and the significant regional differences are the threats. Overall, China’s efforts are on the right track, but urgent action is needed to improve the quality of farmers and transform the agricultural economy, which requires fundamental changes. Based on this, suggestions are proposed for farmers, businesses, and government from four aspects: promoting and expanding pilot demonstration areas for agricultural carbon sequestration trading, building regionally superior agricultural products and specialty industries, synergistic cooperation for technology development and promotion, improving farmer educational level in the form of agricultural cooperatives, so as to help balance economic and environmental benefits in reducing agricultural GHG emissions.