Trading System Research Articles

RESOLVING TRADE DISPUTES IN A GLOBALIZED WORLD: THE EFFICACY AND CHALLENGES OF THE WTO DISPUTE SETTLEMENT MECHANISM

The World Trade Organization (WTO) stands at the heart of global trade governance, providing a framework for the liberalization and regulation of international trade. Central to its mandate is the Dispute Settlement Mechanism (DSM), which ensures that trade rules are respected and disputes are resolved in a structured, legal manner. This mechanism, often described as the "crown jewel" of the WTO, underpins the organization's credibility and facilitates a rules-based trading system. Yet, as international trade becomes increasingly complex, the WTO DSM faces significant challenges, necessitating a comprehensive reevaluation of its operations, relevance, and adaptability.

Article: The Contribution of the Canadian-European Partnership to the Integration of Sustainable Development Issues into the Multilateral Trading System

In their influential roles within the World Trade Organization (WTO), Canada and the European Union (EU) demonstrate a strong commitment to sustainable development and multilateralism, navigating challenges and opportunities. Plurilateral negotiations, particularly the Joint Statement Initiatives (JSI), underscore their dedication to addressing issues like services regulation and fostering inclusive trade. The institutional framework of their Comprehensive Economic and Trade Agreement (CETA) provides a structured mechanism, with specialized committees like the one on Trade and Sustainable Development, to ensure transparency, regulatory cooperation, and trade promotion aligned with sustainable development goals. However, challenges persist in achieving consensus on binding commitments. The temptation of unilateralism, exemplified by the EU’s carbon border adjustment mechanism (CBAM), raises concerns about its compatibility with WTO principles and the potential prioritization of sustainability over economic development. Despite these challenges, Canada and the EU continue to champion sustainable development in various international forums, emphasizing the importance of the WTO as the primary platform for reconciling trade and sustainability. The forthcoming thirteenth WTO Ministerial Conference in 2024 presents a crucial opportunity for these leaders to demonstrate a significant commitment to a balanced approach between economic development and sustainability in international trade.

Carbon Trading and Environmental Justice: A Juridical Examination of Fairness in Indonesia's Emissions Reduction Initiative

This paper provides a normative juridical analysis of Indonesia's carbon trading regulations, with a focus on environmental justice and fairness. It examines the legal framework established by Presidential Regulation No. 98 of 2021, which governs carbon pricing mechanisms, including carbon taxes and carbon credit trading. The analysis highlights key gaps in the regulation, particularly in terms of equitable benefit distribution and inclusion of vulnerable communities in decision-making processes. While Indonesia's carbon trading system aligns with global trends and its international obligations, significant improvements are needed to ensure that marginalized groups are not further disadvantaged. Recommendations include the redistribution of carbon trading revenues, enhanced public participation, stronger enforcement mechanisms, and alignment with international best practices.

The EU Emission Trading System Tax Regime and the Issue of Unfair Maritime Competition

The EU Emission Trading System Tax Regime and the Issue of Unfair Maritime Competition

Leveraging data mining and cybersecurity techniques to enhance algorithmic trading performance and forensic investigations in financial markets

In the evolving landscape of financial markets, the convergence of data mining, cybersecurity, and algorithmic trading plays a pivotal role in enhancing trading performance and forensic investigations. This study investigates how data mining techniques are leveraged to extract meaningful patterns and trends from vast financial datasets, improving the accuracy and profitability of algorithmic trading strategies. By identifying historical trends, price movements, and trade margins, data mining enables traders to optimize decision-making processes and manage risks more effectively. Cybersecurity emerges as a critical factor in safeguarding both trading algorithms and sensitive financial data from cyber threats. The integration of advanced cybersecurity measures ensures the integrity, confidentiality, and availability of trading systems, reducing vulnerabilities that could be exploited by malicious actors. Additionally, forensic investigation techniques are employed to detect fraudulent trading activities, such as insider trading and market manipulation, thereby protecting market participants and maintaining regulatory compliance. This research highlights the importance of combining secure data mining practices with robust cybersecurity measures to enhance the overall performance of algorithmic trading systems. Furthermore, it explores how forensic methodologies can help detect anomalies and ensure the transparency and fairness of financial markets. Through an integrated approach, this study emphasizes the potential of data mining and cybersecurity in transforming trading operations while mitigating risks associated with cyber threats and financial fraud.

Can agricultural export trade openness improve residents' health in China

Agricultural trade, a vital component of the global trading system, significantly influences residents' health. This paper employs data from the 2010-2021 Chinese General Social Survey (CGSS) to conduct the first comprehensive analysis of the impacts of agricultural export trade openness on residents' health and the underlying mechanisms. The study reveals that agricultural export trade openness markedly enhances the health level of Chinese residents. The impact of agricultural export trade openness on the health of populations in different geographical areas and with different characteristics is heterogeneous. Mechanism analysis shows that the opening up of agricultural product export trade affects residents' health levels through three pathways: household income, working hours, and agrochemical use. This paper contributes new empirical evidence on the determinants of residents' health from a trade perspective, offering valuable insights for advancing agricultural trade openness and improving residents' health.

Artificial intelligence and the digitalization of finance in Latin America: evidence from Brazil

ABSTRACT For decades, the data-centric digital circuits of global finance have been driving financial markets toward algorithmic management. The penetration of algorithms into finance dates back to the 1980s, well before becoming a significant trend in Internet and social media companies. AI is now considered the new frontier of this process. This paper examines Brazil, the most important financial center in Latin America, as a case study to explore Machine Learning applications in capital markets, providing new evidence on the penetration of automated trading systems in the region. Grounded in a historical overview of the digitalization of capital markets in Brazil, the paper draws on the STS definition of infrastructures and the political economy of digitalized finance to analyze AI as an emerging financial infrastructure. Beyond its physical and material aspects, this approach also addresses how digitalization and AI relate to financialization and the emergence of new financial risks.

NAVIGATING THE REGULATORY LANDSCAPE OF VOLUNTARY SUSTAINABILITY STANDARDS IN INTERNATIONAL FOOD TRADE: A MULTILATERAL INTERPLAY - DOI: 10.12818/P.0304-2340.2024v85p171

Voluntary sustainability standards (VSSs) are non-mandatory criteria designed to foster sustainable practices throughout the global supply chain. These standards are particularly prevalent in the agri-food sector, where they aim to support wider public policy objectives such as sustainable food production, animal welfare, healthy lifestyle, and labour conditions. Significant challenges arises from the fact that VSSs, despite their voluntary nature, they often impose mandatory effect in the global supply chain. They also often lack easily-identifiable features of the product themselves. These creates systematic problems for the global food trade system, where conventional regulation address governmental measures on goods with specific inherent characteristics that can be clearly identified in the final product. This article explored the interrelationship among private VSSs, national regulations and international treaties within the agri-food industry. It highlights ambiguity surrounding the regulatory framework for VSSs under the WTO law, noting the absence of a consensus on how relevant provisions should be interpreted. Despite the diversity among various VSSs, VSSs, this article argues for the potential for harmonization and equivalence among VSSs. By aligning various VSSs and with technical regulations, it is possible to strike a balance between trade liberalization and promotion of sustainable practices.

Siamese Graph Convolutional Split-Attention Network with NLP based Social Sentimental Data for enhanced stock price predictions

Predicting stock market behavior using sentiment analysis has become increasingly popular, as customer responses on platforms like Twitter can influence market trends. However, most existing sentiment-based models struggle with two major issues: inaccuracy and high complexity. These problems lead to frequent prediction errors and make the models difficult to implement in real-time trading systems. To address these challenges, this paper proposes a new method called Siagra-ConSA-HSOA (Siamese Graph Convolutional Split-Attention Network with NLP-based Social Sentiment Data). Two data sources feed the model: specifically, NIFTY-50 Stock Market and real-time Twitter sentiment. Through Natural Language Processing (NLP), the raw data is pre-processed and key features are extracted before they are fused into a unified dataset using a cross-domain transformer, namely CDSFT, and then Circle-Inspired Optimization Algorithm (CIOA) selects the most important features from this dataset. This decreases the complexity of the model without losing essential information. Finally, a Graph Convolutional Split-Attention Network (SGCSAN) for promisingly predicting whether the stock prices are going to hit the ground and fly high again or is going to nosedive with Humboldt Squid Optimization Algorithm (HSOA) is introduced to further improve accuracy with lesser error generation. The proposed model Siagra-ConSA-HSOA achieved 99.9% accuracy and 99.8% recall in the testing stage, meaning that such a model performs better than the current approaches both in prediction accuracy and efficiency. Thus, this is a glimmer that the model shall be able to overcome some of the main problems with the current techniques used in predicting the behavior of the stock market.GitHub Repository: https://github.com/jramans2/Siamese-GCN-SplitAttention-Stock-Prediction.git

Do Emission Trading Systems, Green Technology, and Environmental Governance Matter for Environmental Quality? Evidence from the European Union

This study investigates how emission trading systems can diminish the ecological footprint in the European Union bloc by considering the contribution of eco-friendly technology and ecological governance through another market-based intervention (i.e., environmental tax). Using PQARDL in the 26 EU members for data during 2011–2021, the study demonstrates that carbon pricing emission trading systems successfully reduce emissions in medium and upper quantiles but have little influence on lower quantiles. As businesses and consumers face higher costs for carbon-intensive activities, they are encouraged to adopt cleaner technologies and more sustainable practices. Over time, this shift decreases overall carbon emissions and resource consumption, which helps lower ecological pressure. Additionally, Europe's robust regulatory framework and commitment to climate goals ensure that carbon pricing is enforced consistently, further amplifying its positive environmental impact. Eco-friendly technology innovation appears to be a powerful tool for achieving rapid environmental advantages, leading to a considerable reduction in environmental footprint across various levels. Climate-smart patents can significantly curb emissions within the ecosystem and help balance the economy and the environment. Environmental taxes work alongside carbon pricing and green patents to incentivize individuals and corporations to adopt more sustainable practices in the 26 EU members. By unraveling the multifaceted relationships underpinning emission trading systems, renewable energy, green patents, and environmental tax, this study provides actionable insights to guide strategic decisions and policy formulations to reduce the ecological footprint.

Deep Learning in Finance: A Survey of Applications and Techniques

Machine learning (ML) has transformed the financial industry by enabling advanced applications such as credit scoring, fraud detection, and market forecasting. At the core of this transformation is deep learning (DL), a subset of ML that is robust in processing and analyzing complex and large datasets. This paper provides a comprehensive overview of key deep learning models, including Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTMs), Deep Belief Networks (DBNs), Transformers, Generative Adversarial Networks (GANs), and Deep Reinforcement Learning (Deep RL). Beyond summarizing their mathematical foundations and learning processes, this study offers new insights into how these models are applied in real-world financial contexts, highlighting their specific advantages and limitations in tasks such as algorithmic trading, risk management, and portfolio optimization. It also examines recent advances and emerging trends in the financial industry alongside critical challenges such as data quality, model interpretability, and computational complexity. These insights can guide future research directions toward developing more efficient, robust, and explainable financial models that address the evolving needs of the financial sector.

Does emission trading system (ETS) deserve further promotion in the building sector? Evidence from China

Although the emission trading system (ETS) has been widely applied and recognized in energy-production side, its impact on the energy-demand side is still ambiguous. Does ETS deserve promotion in energy-demand sectors? If so, what preparations are necessary? In response to these pressing but highly publicized issues, this paper, for the first time, focuses on the building sector which is the most typical energy-demand sector based on energy consumption data from public buildings across 26 regions in China spanning 12 years by employing the Synthetic Control Method (SCM). China, as the most representative developing country with more than ten years of ETS pilot practice for buildings, is a good evidence base. The results indicate that ETS has overall applicability to the building sector despite regional variations. And the government support, industrial structure and economic development orientation are the most determinant of its effect, rather than the conventionally perceived heterogeneous sectoral characteristics of buildings. Moreover, for buildings in the stage of rigid growth, the ETS should be adopted first and foremost to reduce its carbon intensity rather than the total amount. The findings provide practical recommendations on whether and how to promote ETS in future buildings, particularly in developing countries.

Allocation or Skill? What is Driving Corporate Trading Performance in the EU ETS?

This study empirically analyzes factors related to companies’ profits from trading emission allowances in the European Union Emissions Trading System (EU ETS) for the period from 2005 to 2017 by combining information on trading activities with company characteristics of more than 6,000 companies. The factors considered include net position (i.e., free allocation of allowances minus emissions), strategic skills (i.e., banking of allowances, timing of trading, transaction frequency, use of intermediaries), skill-related structural factors (i.e., number of installations, sector affiliation), market pressure, year, and region effects. The results from estimating a Panel Heckman Selection Model suggest that companies’ profits from buying and selling emission allowances are related to a company’s net position, banking of allowances, timing of trading, and the number of installations. The findings further indicate that companies choose the number of banked allowances efficiently, that is, they take into account the opportunity costs of selling these allowances on the market. JEL Classification: Q48, Q54, Q58

Does Floor Trading Matter?

ABSTRACTAlthough algorithmic trading now dominates financial markets, some exchanges continue to use human floor traders. On March 23, 2020 the NYSE suspended floor trading because of COVID‐19. Using a difference‐in‐differences analysis around the closure of the floor, we find that floor traders are important contributors to market quality. The suspension of floor trading leads to higher spreads and larger pricing errors for treated stocks relative to control stocks. To explore the mechanism, we exploit two partial floor reopenings that have different characteristics. Our finding suggests that in‐person human interaction facilitates the transfer of valuable information that algorithms lack.

Effect of Integrated Customs Management Systems (ICMS) on Customs Duty Performance in Nairobi County, Kenya

Purpose: Tax is an important stream of revenue for government’s development projects and therefore all efforts must be made by governments to ensure that it is accurately and efficiently collected to facilitate the government’s operations. This study sought to determine the of integrated customs management system on factors affecting customs duty performance Nairobi County, Kenya. The theories supporting the study is comparative theory of international trade. Methodology: The study employed explanatory research design. The target population of this study was 438 respondents comprising customs heads of departments, customs officers and customs agents at Embakasi Inland Depot, Nairobi. with a sample size of 209, out of which 173 respondents correctly filled the questionnaires and submitted them accordingly. The data collected was analyzed through descriptive and inferential statistics. A linear regression model was used. Results: The coefficient study showed that a unit change in ICMS increases customs duty performance significantly β =0.205 p-value =0.000<0.05. Conclusion: The study recommends that the KRA and the government of Kenya prioritize the continuous improvement of the Integrated Customs Management System (ICMS). Customs management should ensure full utilization of the ICMS by training customs officials and stakeholders, given its positive impact on duty performance. Future research may be carried out on the effects of tax incentives on Customs duty performance

Emergence of information aggregation to rational expectations equilibria in markets populated by biased heuristic traders

Information aggregation is a key economic function of markets. We report results of a computational experiment with markets populated by simple algorithmic traders who follow two heuristics usually thought of as leading to biased information processing in behavioral economics literature (anchor-and-adjust, and representativeness). Outcomes of these markets either tend to cluster around (or fail to do so) rational expectations equilibria under specific conditions, consistent with markets populated by profit-motivated human traders. Algorithmic trader convergence is slower and noisier than that of human traders. Our results illustrate the emergence of rational expectations equilibria through complex interactions among actions of biased heuristic traders with limited information processing capabilities.

Between abolition and opium: civility and force across Asian and Caribbean British imperialisms

ABSTRACT This paper explores the relationship between Britian’s abolition of slavery in its Caribbean colonies in 1838 and its instigation of the First Opium War with China in 1840. In so doing, it works against the occlusion of this relationship in the study of British imperialism despite the centrality of these events to the birth of global systems of free trade and free labour. It draws on the parliamentary debates and correspondence of British Foreign Secretary Lord Palmerston to analyze Britain’s negotiation of anti-slavery efforts with Haiti and its trade of Indian opium with China. It argues that these negotiations position British masculine civility against less-civilized foreign others in ways that legitimate the use of maritime force. This logic of imperial civility produces contradictory effects by demanding an end to the brutality of African enslavement while condoning the devastation of Asian opium addiction enacted in the name of free trade.

On the legality of national carbon pricing instruments alongside the new EU ETS 2

With the EU ETS 2 a new EU-wide emissions trading system is introduced that covers the greenhouse gas emission of the buildings and road transport sectors, i.e. sectors the decarbonization of which has so far been the responsibility of the Member States under the Effort Sharing Regulation. Since they will remain responsible for the achievement of their national greenhouse gas emission reduction targets under that regime, the question arises whether the Member States can maintain or introduce additional carbon pricing instruments alongside the new EU ETS 2. Hence, this paper examines the legality of such an approach, by assessing relevant provisions of EU secondary and primary law. Without going deep into the economic and political considerations, it concludes that from a legal perspective, the coexistence of national carbon pricing instruments and the EU ETS 2 is not prohibited.With the latest reform of the EU Emissions Trading System Directive (ETS Directive), the European Union (EU) has introduced a new EU emissions trading system for buildings and road transport (EU ETS 2)1. The decarbonization of those sectors traditionally falls within the responsibility of the EU Member States under the regime of the Effort Sharing Regulation (ESR). The ESR introduces legally binding national greenhouse gas (GHG) emission reduction targets. Hence, over the last few years, the Member States have introduced different measures in order to reach their targets. Those include carbon pricing instruments, understood as measures that put a price on the emission of GHG and thus create an incentive to reduce those emissions. Carbon pricing instruments include carbon taxes, as well as emissions trading systems2. With the introduction of the new ETS 2, the question arises whether the Member States can maintain (or introduce) such national carbon pricing instruments in parallel to the new EU ETS 2.

Dissecting environmental efficiency: The role of technology adoption and usage

How could firms best reduce their environmental impact? Should they change technology? Or could they do better with what they already have? This paper shows that one size does not fit all. We analyse a sample of polluting production plants (i.e. installations) regulated under the EU Emission Trading System. We employ a mixture model estimation to dissect environmental efficiency into a technology adoption component (what type of technology is used) and a technology usage component (how a technology is used). Our installation-level analysis shows that the share of installations adopting frontier technologies is about 21%. We also find that the average environmental efficiency gains that installations could reach by improving technology adoption and technology usage are 75% and 80% respectively. The analysis of balance-sheet data on parent companies reveals that better environmental technologies are adopted by larger, listed, multi-installation and international companies, while older firms and firms with higher intangible assets intensity more commonly show improved technology usage.

Resource nationalism: the intersection of politics and economics

Resource nationalism has a complex economic, political, and cultural history and reality. The evolution of the supply and demand structure of the global mineral metal market influences it. It is closely linked to the “economic-political” system within resource-rich countries. We analyze the ‘economic-political’ logic of resource nationalist behavior using the two-tier game theory. We examine 261 cases of resource nationalism since 1990 and test the theoretical hypotheses using quantitative techniques. The results show that rising mineral prices are the main trigger for contemporary resource nationalism. However, ideology, institutional quality, social climate, and economic dependence within resource countries play a non-negligible role. Resource wealth dependence in host countries makes it difficult for policymakers to escape national resource interventions. We argue that (1) The global economic trading system imbalance is the root of resource nationalism. (2) Resource nationalism is an endeavor by resource-rich countries to seek entitlements from mineral resource endowments. (3) Resource nationalism is not a zero-sum competition in the national economic trading system. In the face of the potential risks posed by resource nationalism to the global supply of minerals, the relevant interest groups should have sufficient strategic reserves to cope with the possible game of interests.