Efficient Trade Research Articles

Bilateral trade with loss-averse agents

AbstractWe introduce expectations-based loss aversion, which can explain the empirically well-documented endowment and attachment effect, into the classical bilateral-trade setting (Myerson and Satterthwaite in J. Econ. Theory 29:265–281, 1983). We derive optimal mechanisms for different objectives and find that relative to no loss aversion, the platform designer optimally provides agents with partial insurance in the ownership dimension and with full insurance in the money dimension. Notably, the former is achieved either by increasing or decreasing the trade frequency, depending on the distribution of types. Finally, we show that the impossibility of inducing materially efficient trade persists with loss aversion.

Access to finance and intra-Africa trade efficiency

This paper examines the impact of access to finance on intra-Africa trade efficiency using a panel dataset comprising 44 African exporters. Between 2008 and 2021, a stochastic frontier version of the gravity model finds intra-Africa trade to have been lower than its full potential by $1.1 billion on average. Exporters with better access to finance such as South Africa, Morrocco and Mauritius operated closer to their full potential. The effect of access to finance on intra-Africa trade is found to have been stronger between distant trading partners. This suggests that access to finance largely promotes intra-Africa trade through financing transportation and logistics. In view of these results, the analysis recommends policy efforts to improve access to finance in order to maximize trade between African countries.

DEVELOPMENT OF THAILAND’S NATIONAL SINGLE WINDOW

The aim of this research was to investigate the current implementation of Thailand’s National Single Window and identify opportunities for improvement. Objectives included 1) assessing Thailand’s existing National Single Window, including its structure and function and agency and customer participation; 2) identifying problems with function and process of the National Single Window; and 3) identifying potential solutions to improve customs efficiency, port efficiency, and overall trade efficiency through Thailand’s National Single Window. Implementation of the National Single Window, which began to be considered in policy around 2005, is now substantially complete, but development activities and improvement are still ongoing. This study used a nested sequential qualitative-led mixed methods research design to investigate implementation of the National Single Window from the perspectives of multiple stakeholder groups. Data collection was conducted using a policy review (Phase 1), a questionnaire (n = 400) (Phase 2), and an open-ended questionnaire (n= 38) (Phase 3). The Phase 1 and Phase 3 data was analysed using content analysis, while Phase 2 data was analysed using descriptive statistics. The analysis showed that the National Single Window is currently being used and some aspects, such as implementation of import and export processes, is largely complete. This has provided benefits to users and agencies, especially reduced cost and time and improved efficiency. However, there are still some areas where improvements are needed, particularly interagency coordination of processes and information sharing and improvement of system stability. The study provides recommendations for continued development of the National Single Window based on these findings.

Kenya’s macroeconomic policies and trade efficiency within the East African Community: A stochastic frontier analysis

Despite Kenya dominating trade volumes in the East African Community (EAC), it has been trading below its potential within the region. This is also in spite of the increased scope of the country’s trade opportunities resulting from the region’s increased market integration. Empirical evidence has shown that macroeconomic policies influence international trade flows. However, there is limited empirical evidence on the effect of macroeconomic policies on trade efficiency. The few existing studies on this topic have also used estimation techniques that depict efficiency as being drawn from an average level of trade and not the optimal level of trade, as well as also not separating the effects of inefficiencies from the statistical noises. This, therefore, creates a knowledge gap that this study aims at investigating by using the stochastic frontier gravity model (SFGM), to determine the effect of Kenya’s macroeconomic policies on its trade efficiency within the EAC. The study used annual panel secondary data for the period 2000 to 2021. This study finds that the GDP of Kenya and that of its EAC trading partners, the geographical distance and border significantly affects trade volume. It also finds that globalization plays a significant role in influencing Kenya’s trade. For the inefficiency model variables, exchange rate depreciation was found to significantly increase trade efficiency, while increase in tariff rate had an adverse and significant effect. Further, the study included corruption as a control variable and found that it significantly increases trade inefficiency. Kenya, though trading at an average efficiency level of 86.91 percent, was found to have high unexploited trade potentials within the region especially with Tanzania and Uganda. The study recommends that Kenya’s policymakers deliberately advance the macroeconomic policies that promote trade efficiency while at the same time closely monitor the ones that hinder efficiency.

One-way ticket to the moon? An NLP-based insight on the phenomenon of small-scale neo-broker trading

We present an Natural Language Processing based analysis on the phenomenon of “Meme Stocks”, which has emerged as a result of the proliferation of neo-brokers like Robinhood and the massive increase in the number of small-scale stock investors. Such investors often use specific Social Media channels to share short-term investment decisions and strategies, resulting in partial collusion and planning of investment decisions. The impact of online communities on the stock prices of affected companies has been considerable in the short term. This paper has two objectives. Firstly, we chronologically model the discourse on the most prominent platforms. Secondly, we examine the potential for using collaboratively made investment decisions as a means to assist in the selection of potential investments.. To understand the investment decision-making processes of small-scale investors, we analyze data from Social Media platforms like Reddit, Stocktwits and Seeking Alpha. Our methodology combines Sentiment Analysis and Topic Modelling. Sentiment Analysis is conducted using VADER and a fine-tuned BERT model. For Topic Modelling, we utilize LDA, NMF and the state-of-the-art BERTopic. We identify the topics and shapes of discussions over time and evaluate the potential for leveraging information of the decision-making process of investors for trading choices. We utilize Random Forest and Neural Network Models to show that latent information in discussions can be exploited for trend prediction of stocks affected by Social Network driven herd behavior. Our findings provide valuable insights into content and sentiment of discussions and are a vehicle to improve efficient trading decisions for stocks affected from short-term herd behavior.

TriChronoNet: Advancing electricity price prediction with Multi-module fusion

This study introduces a novel architecture for electricity price forecasting, comprising four modules designed for prediction and information fusion. Three modules are dedicated to preliminary price prediction, while the fourth integrates information from prior predictions to generate final forecasts. Experimental evaluations demonstrate the effectiveness of the proposed model, showcasing superior performance compared to models from classical ones to cutting-edge ones in time-series modeling. Specifically, results show improvements of 3.51%–53.09% on RMSE, and 4.77%–59.19% on MAE. Additionally, we conduct an ablation study to analyze the robustness of the proposed model and the distinct contributions of its modules. The findings highlight the different roles of each component and provide valuable insights for future research in electricity price prediction. Given the critical role of accurate electricity price prediction in promoting the efficiency of electricity trading and market health, this method offers a promising avenue for advancing prediction techniques in this domain.

Pro Trader RL: Reinforcement learning framework for generating trading knowledge by mimicking the decision-making patterns of professional traders

This study proposes a novel reinforcement learning (RL) framework, professional trader RL (Pro Trader RL), which mimics the decision-making patterns and trading philosophy of professional traders in stock trading. By exploiting the characteristics of RL, the framework aims to learn efficient trading strategies while mimicking the trading philosophy and risk management methods of professional traders. The framework takes into account the complex nature of the stock market and presents an integrated approach to RL, from data pre-processing to buying, selling and stop-loss. Pro Trader RL consists of four main modules. Data Preprocessing, Buy Knowledge RL, Sell Knowledge RL and Stop Loss Rule, each of which plays the role of professional traders knowledge. The results of three experiments show that the framework achieves high returns and Sharpe ratio regardless of market conditions and has stable performance with low maximum drawdown (MDD), which is superior to the state-of-the-art research. The proposed framework provides a novel approach to applying RL to the stock market and is expected to be useful and applicable in real-world trading settings.

The efficiency of China's export trade with Africa and its influence mechanism

AbstractThis paper calculates the trade efficiency of China's exports to Africa by considering trade inefficiency factors, such as Chinese aid to Africa and Chinese contracted projects. Based on a stochastic frontier gravity model and data for China's exports to 38 African countries from 2010 to 2020, this study draws the following conclusions. First, trade inefficiencies, such as China's aid and contracted projects, play a positive role in promoting China's export trade to Africa. Second, the efficiency of China's export trade to Africa varies significantly in different regions, among which countries with high export trade efficiency are concentrated in North and South Africa. Third, the trade potential and trade expansion space of China's exports to Africa vary significantly among different product sectors, which provides much space for promoting trade policy. We highlight that China's government should develop differentiated regional trade policies due to regional heterogeneity in terms of export trade efficiency between China and African countries.

Implementation of User Experience Design Approach in Web Based E-Commerce for the Agricultural Sector

The technological advancements of the past have transformed various sectors, including information, education, and commerce. Many utilized the internet to enhance business and trade efficiency. Pantai Gading Village was a significant contributor to agricultural production. Its residents traditionally sold agricultural products locally, resulting in a narrow market scope. Consequently, a web-based E-commerce platform was developed using the User Experience Design Process to aid farmers and expand the market for agricultural products in the village. E-commerce facilitated cost reduction for companies, consumers, and management while enhancing service quality and speed. Through this platform, farmers could promote and sell their products online, overcoming the limitations of the local market and enhancing the village's global visibility. User Experience Design (UXD) improved user satisfaction with products through enhanced usability, accessibility, and satisfaction in interactions. This approach yielded designs that were neat, simple, intuitive, flexible, and appealing, providing users with a unique experience and differentiating products or services from competitors. The author of this study employed the Research and Development (R&D) methodology and the Waterfall development method. The system developed incorporated user experience design processes derived from questionnaire results. Users expressed the need for features such as live chat for each product, shipping options, displaying reviews, and offering Cash on Delivery payment method. This system facilitated and streamlined the marketing of agricultural products, thus boosting sales in Pantai Gading Village.

Study on the Impact of RCEP National Logistics Performance on Vietnam's Cross-Border E-Commerce Export Trade

In the era of global economic integration, the Regional Comprehensive Economic Partnership (RCEP) plays a pivotal role in shaping trade dynamics, particularly by enhancing Vietnam's cross-border e-commerce exports through improved logistics. This study meticulously reviews a vast array of both domestic and international literature and employs Stata 17.0 software to analyze panel data spanning from 2014 to 2022 across RCEP member countries. It focuses on six core logistics performance indicators: customs and logistics management, logistics infrastructure, timeliness of shipments, quality of logistics services, tracking and tracing capabilities, and ease of international shipments.The findings from this analysis reveal that these logistics performance indicators significantly influence Vietnam's e-commerce exports. Infrastructure quality and the ease of shipments have the most substantial impact, suggesting that robust logistics infrastructure and efficient shipment processes are crucial for enhancing trade efficiency. Despite the overall positive trends, the study notes significant variations in logistics development within the RCEP region. While countries like Singapore exhibit advanced logistics capabilities, ASEAN countries generally lag behind, indicating a disparate level of logistics progress across the region.The research highlights the potential for targeted logistics improvements to directly boost Vietnam's cross-border e-commerce exports. By advancing logistics performance, especially in areas lagging behind, there is an opportunity to not only enhance trade but also to level the playing field within the RCEP. This study underscores the critical importance of developing cohesive and efficient logistics systems to support the burgeoning e-commerce sector, thereby fostering economic growth and deeper regional integration within the RCEP framework.

Investigating the Economic Dynamics of Mobile Money in Post-Conflict Economies: Empirical Evidence from Somalia

This study investigates the economic dynamics of mobile money in Somalia, focusing on its transformative potential to impact economic activities in the short and long term. Adopting a quantitative research design, the study utilizes secondary data from 2010 to 2022. The autoregressive distributed lag (ARDL) approach is employed to assess the impact of mobile money on household consumption, real income, exports, and imports. Theoretical insights from the quantity theory of money further guide the examination of how mobile money influences economic activities. The findings demonstrate that mobile money has a significant positive effect on household consumption, real income, export growth, and import activity both in the short and long term. The study reveals that mobile money not only facilitates financial transactions but also enhances trade efficiency and promotes financial inclusion, thereby stimulating economic activities and growth. The study underscores the need for a supportive regulatory environment for mobile money services. It highlights that financial institutions and mobile money service providers should expand innovative services to include the unbanked population. By doing so, they can leverage mobile money to foster greater economic engagement and empowerment. Policymakers are encouraged to develop strategies that promote financial inclusion and economic development to harness the full potential of mobile money. This study enriches the existing literature by uniquely investigating the economic impacts of mobile money in Somalia, a post-conflict setting, thereby broadening the understanding of how financial technologies can catalyze economic activities and growth in similar contexts.

Enhancing renewable energy certificate transactions through reinforcement learning and smart contracts integration

Given the complexity of issuing, verifying, and trading green power certificates in China, along with the challenges posed by policy changes, ensuring that China’s green certificate market trading system receives proper mechanisms and technical support is crucial. This study presents a green power certificate trading (GC-TS) architecture based on an equilibrium strategy, which enhances the quoting efficiency and multi-party collaboration capability of green certificate trading by introducing Q-learning, smart contracts, and effectively integrating a multi-agent trading Nash strategy. Firstly, we integrate green certificate trading with electricity and carbon asset trading, constructing pricing strategies for the green certificate, carbon, and electricity trading markets; secondly, we design a certificate-electricity-carbon efficiency model based on ensuring the consistency of green certificates, green electricity, and carbon markets; then, to achieve diversified green certificate trading, we establish a multi-agent reinforcement learning game equilibrium model. Additionally, we propose an integrated Nash Q-learning offer with a smart contract dynamic trading joint clearing mechanism. Experiments show that trading prices have increased by 20%, and the transaction success rate by 30 times, with an analysis of trading performance from groups of 3, 5, 7, and 9 trading agents exhibiting high consistency and redundancy. Compared with models integrating smart contracts, it possesses a higher convergence efficiency of trading quotes.

The Impact of Digital Economy on Domestic Trade and Investment Efficiency Measurement and Heterogeneity Analysis

Against the backdrop of a new round of booming technological revolution, the digital economy has brought about high-quality development for trade. This paper is based on the 2011-2019 China's trade and investment in China, which will have a profound impact on China's trade and investment. This paper is based on the2011-2019 China. The spatial panel data of 30 provinces (excluding Tibet, Hong Kong, Macao and Taiwan) were analysed by using OLS model, panel solid model and panel data. Construct correlation regression models using fixed effects model, panel random effects model, differential GMM and systematic GMM methods. type and "Digital Economy Development Level Indicator System" to study the impact of the digital economy on the efficiency of domestic trade and investment.so as to contribute to the promotion of high-level opening up to the outside world, the deepening of domestic demand strategy, and the construction of " trade power".

China’s ecological footprint via biomass import and consumption is increasing

As China’s economic and population demands grow, the need for biomass imports rises, presenting challenges for sustainable development and global ecosystem protection. Here we examine China’s increasing influence on global ecosystems through its biomass product consumption and trade using the Human Appropriation of Net Primary Productivity framework, analyzing data from 2004 to 2017 with projections until 2050. In 2017, China was the world’s largest biomass consumer, consuming approximately 1.75 petagrams of carbon per year, projected to rise by 317.6% by 2050, highlighting China’s major role in global ecosystem stress. Our findings also show an increase in biomass imports from lower-middle-income and low-income countries between 2004 and 2017, and these imports are expected to continue increasing by 402.9% in 2050. The analysis reveals that domestic product demand and changes in foreign production efficiency are key drivers of this trend, suggesting the need for China to shift towards more efficient trade practices and support cleaner production methods internationally.

Optimization model of monthly continuous bidding transaction in electricity market considering risk hedging

In order to further alleviate the problems of large assessment deviations, low efficiency of trading organisation and difficulties in system optimisation in medium- and long-term market trading, the article proposes an optimisation model for continuous intra-month bidding trading in the electricity market that takes into account risk hedging. Firstly, the current situation of market players’ participation in medium and long-term trading is analysed; secondly, the impact of contract trading on reducing operational risks is analysed based on the application of hedging theory in the primary and secondary markets; finally, the continuous bidding trading mechanism is designed and its optimisation effect is verified. The proposed model helps to improve the efficiency of contract trading in the secondary market, maintain the stability of market players’ returns and accelerate the formation of a unified, open, competitive and well-governed electricity market system.

Crime, efficiency of labour and trade: a theoretical analysis

Crime, efficiency of labour and trade: a theoretical analysis

Design of an Iterative Method for Optimizing Solar Power Systems using Quad LSTM with IoT Integration Operations

The growing necessity for sustainable energy solutions underscores the critical need for optimizing solar power systems. Existing methodologies predominantly focus on static strategies with limited adaptability to dynamic environmental and operational conditions, often resulting in suboptimal performance and inefficiency. This research introduces a comprehensive, integrated framework employing Internet of Things (IoT) technologies alongside advanced machine learning and deep learning methodologies to enhance solar power system efficiency and reliability levels. The proposed model integrates four key components: predictive maintenance using Support Vector Machines (SVM) for enhanced anomaly detection, solar power forecasting via Quad Long Short-Term Memory (QLSTM) neural networks, dynamic load balancing through Reinforcement Learning with Deep Q-learning, and the integration with smart grids employing Decentralized Multi-Agent Systems (MAS) with Auction-Based Mechanisms. Each method is selected based on its suitability to address specific challenges within the solar power domain: SVMs for their effectiveness in high-dimensional anomaly detection, QLSTMs for their superior temporal pattern recognition in forecasting, Deep Q-learning for its adaptability in dynamic load management, and MAS for efficient decentralized energy resource coordination operations. The implementation of these methodologies demonstrates significant advancements over traditional approaches. Predictive maintenance facilitated by SVMs leads to a 20% reduction in maintenance costs, while QLSTM-based forecasting achieves a 95% accuracy rate, thereby enhancing grid management and reducing revenue losses. Moreover, reinforcement learning optimizes energy utilization, decreasing wastage and system downtime by 10% and 5% respectively. Lastly, the MAS framework promotes a 20% increase in energy trading efficiency, yielding a 10% reduction in transaction costs and bolstering grid resilience levels. This work represents a significant leap forward in solar power optimization, offering a scalable, efficient, and intelligent framework that paves the way for more sustainable and reliable energy systems. The integration of IoT with machine learning and deep learning presents a paradigm shift in renewable energy management, marking a critical step toward achieving global sustainability objectives..

A Study on Export Trade Efficiency and Potential of Korean Semiconductor Industry: Empirical Evidence from 72 Trade Export Countries

Purpose - The semiconductor industry is an integral part of the Korean economy. Trade competition in semiconductors, the core component of the fourth industrial revolution, has become increasingly fierce. In this context, this study aims to empirically analyze the current situation, international competitiveness, and challenges faced by Korea’s semiconductor export trade from the perspective of trade efficiency by using Korea’s semiconductor export trade data. Design/Methodology/Approach - Using a stochastic frontier gravity model, this study analyzes the data of 72 countries and regions that have been the main targets of Korea’s semiconductor exports for the past 22 years using an empirical approach to investigate the main factors affecting Korea’s semiconductor export trade, trade efficiency, and trade potential. Findings - From the division of semiconductor export market type, there are 13 saturated countries, and the trade volume accounts for 77.86% of the total trade volume of Korea’s semiconductor exports. There were 31, 16, and 12 expansion, development, and iceberg-type countries, respectively. In addition, the regression results show that Korea’s population size and the R&D expenditures of the countries with which it trades have a significantly positive effect on semiconductor exports. Research Implications - To date, no more systematic empirical research literature on the efficiency of Korean semiconductor export trade has been found, and this study fills the gap in the literature in this area through an empirical analysis approach. This study aims to provide a reference value in Korea’s semiconductor export trade.

Policy recommendations for integrating artificial intelligence into global trade agreements

The integration of artificial intelligence (AI) into global trade agreements presents a transformative opportunity to enhance efficiency, competitiveness, and innovation in international commerce. This review outlines policy recommendations aimed at facilitating the seamless incorporation of AI technologies into the framework of global trade agreements. As AI technologies continue to proliferate across various sectors, including manufacturing, logistics, and services, understanding the implications and challenges of their integration into trade agreements becomes paramount. This review provides insights into the current landscape of AI in global trade, highlighting both the existing challenges and opportunities for leveraging AI to drive economic growth and sustainable development. Key policy considerations include the harmonization of AI standards, addressing intellectual property rights and data ownership, facilitating cross-border data flows, ensuring transparency and accountability in AI decision-making, mitigating potential job displacement and inequality, and promoting ethical AI practices. Drawing on case studies and best practices from regional trade agreements and industry-specific implementations, this review offers actionable recommendations for policymakers, businesses, and international organizations. These recommendations emphasize the importance of collaborative efforts, capacity building, and monitoring mechanisms to effectively harness the benefits of AI while mitigating potential risks. By embracing AI as a driver of innovation and efficiency in global trade, stakeholders can foster a more inclusive and resilient trading environment that maximizes the benefits of technological advancements for all participants.

Variable Neighborhood Search for Multi-Port Berth Allocation with Vessel Speed Optimization

This paper delves into the multi-port berth allocation problem (MBAP), enriching the traditional berth allocation problem (BAP) with vessel speed optimization (VSO). In the MBAP, it is assumed that there is cooperation between the port and the shipping companies, and the operation of the vessels and the ports is planned to maximize the overall benefits. Exploring this potential collaboration between ports and shipping entities has the potential to mitigate, or even resolve, the challenges plaguing maritime transportation, e.g., port congestion and suboptimal vessel schedules, to ultimately enhance the efficiency of maritime trade. In this paper, a new mixed-integer linear programming (MILP) model for the MBAP is formulated, which attempts to minimize the total cost incurred during operations, with various constraints such as vessel sailing, the vessel space–time relationship in ports, and the planning period. Meanwhile, an innovative variable neighborhood search (VNS) algorithm is presented, in which the initial solution generation method and neighborhood structures are proposed according to the MBAP characteristics. Furthermore, two sets of MBAP instances are generated to test the proposed MILP and VNS, of which the first set is based on real-world port data and the second on existing studies. The numerical experiments verify that the VNS can efficiently and reliably solve instances of all scales, with each neighborhood structure contributing uniquely to the iterative process. In addition, by analyzing the impact of varying oil prices on the MBAP, the study offers valuable management insights. Finally, a case study based on real data from a port group in the Yangtze River Basin is presented to further demonstrate the necessity of considering vessel service time window and planning period in the MBAP as well as the important role of the VSO in scheduling.