Trade Research Articles

The Possible Introduction of Digital Currency Exchange in Bangladesh: A Legal Analysis

Digital currency is the most interesting and demanding currency medium over the world. Digital currency means crypto currency and which means Bitcoin, NFT Ethereum, etc. Day by day those digital currencies are increasing in the crypto market. In Bangladesh this currency medium is not approved yet. But this digital currency can change the whole view of Bangladesh's perspective if it’s approved by the government. This digital currency can play an influential role in e-commerce and business and can change massively for the economy's growth. The acceptance of digital currency in Bangladesh is now a matter because this is an important issue in this globalized world. All the western countries approve of the digital currency dealing system which is binding by their law and they trust in their highly configured block chain security for this. The Russia-Ukraine war makes it more important to approve it because many economic issues and crises over the world arise. We know digital currency has no central control in terms of security. That is why the regulatory framework is very much necessary before approval. There is a large scope for digital currency in Bangladesh for use in different fields. Especially in e-commerce business or any kind of trading in the international market this digital currency can be the best transaction system. Right now, in Bangladesh, there is no specific law for digital currency exchange. But it should be approved to improve the economy in this globalized world. This paper tries to find out why this digital currency approval is important for Bangladesh and how it’s security can be ensured through law.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance.

Enhancing Financial Literacy and Investment Skills through a Virtual Stock Trading Application

Currently, there are many real trading applications but there are not many simple stock market simulation applications where users can practice trading in the Indian stock market. We all know about the Stock Market and its losses. we also have heard stories about scams and huge losses in stocks from relatives, friends, and family, But not trying is not the solution to the problem, then what can we do about it? We can learn how it works, practice and then try it in the real market. Then our solution to this problem is to create an application where users can trade stocks with the demo (Virtual) money with real-time experience so that they can learn and afford to make mistakes and learn from those mistakes and make some good fortune in the real stock market and this initiative is focused on Indian audience for there are 58% of US citizens take part in trading activities while in India it is only 2% so by this initiative we think it can India to make more in the Sector of finance. Key Words: Virtual stock trading, Stock market simulation, Financial literacy, Investment education, Online trading platforms, Stock market knowledge, Risk management, Investment skills, User experience, Financial education, Stock trading simulation, Virtual investing, Educational technology, Portfolio management.

BLOCKCHAIN AS A MEAN ENHANCING INTERNATIONAL TRADE

The foundation of the world economy is the international commerce system, but it faces difficulties like fraud, inefficiency, and a lack of confidence. The transformational potential of blockchain technology to improve the international trade landscape includes paperwork, delays, and hazards that complex relationships between importers, exporters, financial institutions, and regulatory authorities. Blockchain presents itself as a revolutionary force that provides efficiency, security, and transparency. Blockchain's smart contracts speed up verification of letters of credit (LCs) in trade finance. The precise blockchain security contributes to safety and fraud prevention by thwarting unauthorized access and tampering. This study explores the case studies to determine how blockchain technology might resolve issues, speed up transactions, and restore confidence in the global commerce ecosystem. Blockchain’s use ultimately is able to reshape trade procedures, promoting a climate of dependability, transparency, and cooperative development.

Analysis of Import and Export Trade Status in Guangdong Province

In 2023, the total volume of foreign trade in Guangdong Province is outstanding, and ranks first in the country for many consecutive years. As the largest province of foreign trade in the country, Guangdong should continue to play a good role in the ballast of foreign trade in the country. Based on the analysis of the current situation of foreign trade in Guangdong Province, this paper finds that the overall development of foreign trade is good, but there are factors such as weak growth, lack of independent innovation ability and external environment in the development process. Based on this, this paper puts forward suggestions from the aspects of market development, industrial upgrading and trade facilitation reform, in order to provide reference for the development of foreign trade in Guangdong Province.

Two-stage robust transaction optimization model and benefit allocation strategy for new energy power stations with shared energy storage considering green certificate and virtual energy storage mode

In the context of the large-scale participation of renewable energy in market trading, this paper designs a cooperation mode of new energy power stations (NEPSs) and shared energy storage (SES) to participate in the power-green certificate market, which divides SES into physical energy storage and virtual energy storage. Secondly, combining the advantages of scenario generation and robust optimization (RO), a two-stage RO model with improved uncertainty interval is proposed to determine the optimal trading strategy. Then, to better align the distribution results of cooperative benefits with the actual contributions of NEPSs and SES, an entropy weight modified Shapley value benefit allocation strategy is constructed. Finally, the new energy base in Qinghai Province, China is chosen for simulation. The results show: (1) Adding energy storage and using two-stage RO are able to effectively improve the ability of NEPSs to resist uncertainty, which increases the revenue of the alliance by 22.8%. (2) The application of SES has better economic benefits than each member equipped with energy storage separately. Compared with the latter, the deviation penalty cost of the former is reduced by 66.4%, and the revenue is increased by 3.4%. (3) The proposed entropy weight modified Shapley value method embodies the important auxiliary role of SES more obviously. Based on this method, the overall satisfaction of the alliance increases by 12.6%. Generally speaking, the optimization model and benefit allocation strategy proposed in this paper can provide guidance for NEPSs and SES participating in power trading, and promote the low-carbon transformation of the power sector.

Market Diversification and Competitiveness of Fresh Grape Exports in Peru

Global trade and globalization have driven growth and diversification in the horticultural industry. Fresh grapes, a key product, face challenges of market volatility, trade barriers, and logistics. Market diversification is essential for stability and competitiveness in international trade. This research focused on analyzing market diversification in Peru’s fresh grape exports during the 2013–2022 period, exploring its implications for international trade. A quantitative methodology was used, along with the analysis of publicly recorded data on the website of the National Superintendence of Customs and Tax Administration, specifically exports from companies shipping under the national subheading for Grapes (0806.10.00.00). Diversification was analyzed using the Herfindahl–Hirschman concentration index (HHI), and competitiveness through the Balassa index. The main findings were that between 2013 and 2022, Peruvian grape exports grew in value (CAGR of 12.02%) and volume (CAGR of 12.13%). The sector expanded with an average of 151.2 companies and 59.1 destination markets. Diversification varied: the USA (12.4%), the Netherlands (1.6%), Hong Kong (4.2%), and Mexico (63.2%). The Herfindahl–Hirschman index showed concentration in the USA (4533 in 2020, 4519 in 2022) and stability in companies (2318 in 2014, 2450 in 2022). Finally, it is recommended to strengthen the Peruvian viticultural sector by maintaining geographic diversification through policies seeking new markets, monitoring global trends, constantly analyzing market concentration, and promoting fair competition among participating companies, allowing new ones to enter this sector.

Herding behaviour in the Indian stock market: An empirical study

The rapid growth of the Indian stock market in recent decades has prompted much study on investing, focusing on understanding and analysing investors' tactics for investment and trading. Over the years, researchers have cited and conducted in-depth investigations into various psychological biases. Herding bias is extensively studied to understand how investors participate in investment and trading in the Indian stock market. The current study on herding provides an investigation into the existence of herd activity in the share market of India. The study attempts to identify herd activity during positive, negative, extreme positive, and extreme negative market conditions, as well as during structural breakpoints and trade volumes that are low as well as high. The researchers have used the modified CSAD (Cross Sectional Absolute Deviation) model to enhance its validity. The findings reveal the rationality of investors during the different market states. However, herding behaviour was evidenced during certain periods at the structural breakpoint. This implies that during positive as well as negative market conditions, herding behaviour is absent, except during certain structural breakpoints and high trading volumes.

Do ESG ratings improve capital market trading activities?

While ESG (Environmental, Social, and Governance) ratings provide an assessment of a firm's sustainable performance, the exact influence of these ratings on stock liquidity remains uncertain. Using listed Chinese A-share firms from 2016 to 2021, we find that ESG ratings improve stock liquidity, and the results are robust after employing alternative measures of ESG rating and liquidity and accounting for endogeneity. Mechanism tests demonstrate that ESG ratings attract market attention, proxied by institutional shareholdings, analysts' followings, and online comments by investors, and enhance corporate transparency, proxied by transparency grades and discretionary accruals, thereby improving stock liquidity. Cross-sectional tests results suggest that when information demand from investors is high, such as strong environmental monitoring, lower-level disclosure of CSR, and lower corporate governance, ESG ratings present a more significant information effect on stock liquidity. Moreover, utilizing COVID-19 as a quasi-natural shock, further analysis reveals that the coronavirus pandemic amplifies the relationship between ESG rating and stock liquidity. Our results contribute to the ongoing debate regarding whether ESG ratings can benefit shareholders by illustrating the positive consequence of ESG ratings on stock market performance.

Validation study of Yunnan ethnic culture industry stock model under epidemic situation based on chaotic particle swarm optimization neural networks

The new crown pneumonia epidemic is raging, in the context of global integration, the scope of the impact of this sudden event spread around the world, the stock market has not been spared, the financial risk has increased dramatically compared with the past, the emergence of the epidemic has led to the spread of investor panic, March 2020, the U.S. S&P 500 index appeared in the four plunge, and led to the market trading meltdown, the world’s financial markets have had an extremely serious impact. The study of the impact of Xin Guan Pneumonia on the company’s stock returns is not only conducive to enriching the theoretical study of public health emergencies, but also conducive to improving the coping strategy, stabilizing the general economic market, and enhancing the public’s awareness of risk response. This paper compares the effect of the four intelligent algorithms of chaotic particle swarm algorithm, chaotic bee colony algorithm, chaotic fruit fly algorithm and chaotic ant colony algorithm combined with neural network on the prediction of the stock price trend of Yunnan national culture, and the study shows that the speed of convergence of the chaotic particle swarm optimization neural network and the speed of descent is better than that of the two models of chaotic fruit fly and chaotic bee colony, and the coefficients of decision of the chaotic particle swarm optimization neural network are higher than that of the other three models, and the errors are lower than the other three models. Indexes are lower than the other three models and have high accuracy in stock prediction of Yunnan ethnic culture, this finding emphasizes the potential of PSO-BP model to provide robust stock market prediction, which is important for both investors and policy makers in dealing with volatile market conditions.

Nonlinear behavior of tail risk resonance and early warning: Insight from global energy stock markets

This article calculates the tail risk of the global energy market and explores the nonlinear behavior of tail risk resonance at the global, regional and national levels from a dynamic perspective. Moreover, we apply various machine learning models to perform the experimental training on the drivers that affect tail risk resonance to establish a risk warning system. By simulation training and evaluation, we screen the optimal risk warning model and identify the important drivers of risk resonance in the energy market. The findings indicate that the energy system has a strong tail risk resonance phenomenon, and the occurrence of extreme events as well as a collapse in oil prices can significantly increase the system's risk resonance. The European region is the dominant risk emitter and the American region is the main risk receiver. The Russian-Ukrainian war significantly increased the risk resonance across regions. The Argentine energy market is the main receiver of tail risk and the US energy market is the primary sender of tail risk. By incorporating the risk factors into several machine learning models, the Random Forest model is found to provide effective early warning of tail risks. Additionally, importance analysis reveals that inflation levels, stock market volatility and trade policy changes are important factors affecting risk resonance in the energy market and have nonlinear effects on the risk resonance of the energy system.

Fear of Missing out on Financial Gains: Associations Between Fear of Missing Out, Problem Gambling, and Speculative Trading in College Students

In recent years a fear of missing out (FOMO) on short-term monetary gains through speculative trading has been highlighted as a driving force of financial behaviors. Additionally, increasing evidence has likened speculative trading to gambling. The current study sought to determine whether financial FOMO is linked to stock market and cryptocurrency trading activities and problem gambling severity in both traditional gambling and financial trading domains, among a sample of 258 college students. Results of binomial regression and hurdle model analyses found that financial FOMO was linked to participation in stock market and cryptocurrency trading. Financial FOMO was also associated with problem gambling severity in traditional gambling and the presence of gambling problems in the stock market trading domain. Our results suggest that financial FOMO may be a salient risk factor of problem gambling in traditional gambling domains and, to a smaller degree, in the speculative trading domain among young adults.

Law Enforcement of Insider Trading in the Capital Market in Indonesia

The purpose of this study is to analyze: 1) How law enforcement criminal acts Insider Trading in the capital market in Indonesia? 2) What is the legal protection for investors who are harmed by the practice; Insider Trading in Indonesia?. The research method used is normative juridical with a statutory approach, concept approach, and case studies. The results showed that: 1) Law enforcement in the Indonesian capital market has not run effectively as a result of weak completeness of regulations, commitment, and coordination among law enforcement, so a new orientation and paradigm are needed that have more aggregate power in capital market management, as an effort to create an orderly and transparent capital market. Investor protection is one of the main principles of activities in the modern capital market, where economic activities carried out by investors must run under the umbrella and supervision system (market surveillance), and law enforcement that can determine the degree of market confidence, because philosophically business in the capital market itself is a trust business. 2) The form of legal protection for investors for Insider Trading actions is through UUPM, UUOJK and all laws and regulations related to Insider Trading.

“Impact of ESG disclosure on firm performance and cost of debt: Empirical evidence from India”

Despite several research evaluating the connection between sustainability and the bottom line of firms, the existing research is inconclusive. In addition, most studies focus on multinational firms based in the United States or Europe, while research in the context of emerging economies is scant. This paper endeavours to investigate the strength of the association between Environment, Social and Governance (ESG) disclosure scores and the financial performance of 272 Indian firms listed on National Stock Exchange for the period 2015 to 2021 by employing panel data regression techniques. The results indicate that organisations having better ESG performance outperform their industry peers financially and are valued more by the market. Moreover, the data indicates that financial markets for lenders consider ESG disclosure when determining creditworthiness. In accordance with this, businesses can examine their operations and become more aligned with eco-friendly methods. Even though ESG disclosure appears to be associated with improved financial success, this is not the case across all industries and during COVID-19 pandemic. This study is relevant due to the growing significance of sustainability and the role of emerging nations in international commerce. It helps managers prioritize resource allocations to ESG-related activities that may affect financial performance differently across industry sectors, thus adding to the literature on the financial effects of ESG disclosures, particularly industry-specific aspects. This research represents a pioneering effort by assessing the impact of ESG disclosure on the profitability, market value, and cost of financing of Indian firms using both aggregation and disaggregation methods.

Investigating the impact of the future carbon market on the profitability of carbon capture, utilization, and storage (CCUS) projects; the case of oil fields in southern Iraq

Carbon capture utilization and storage for enhanced oil recovery (CCUS-EOR) is considered a promising solution to meet future global energy needs while significantly reducing carbon dioxide emissions. However, the future growth of CCUS-EOR is uncertain due to the global economic recession and low oil prices. This research analyzes the impact of uncertain oil and carbon market prices on the economic value of a CCUS-EOR project which contributes to appropriate investment and future policy decisions. To explore the potential trade-offs between available carbon dioxide (CO2) quantities for trading or injection for enhanced oil recovery, the market's trading mechanism is analyzed by predicting future oil and carbon prices. The forecasted market prices are then used in an optimization model to estimate the optimal cumulative cash flow from implementing a proposed CCUS-EOR project to capture CO2 from the Hartha power plant located in the southern Basra Province of Iraq, considering three different oil fields of Rumaila, Nassriya, and Noor. The results revealed that the profitability of the proposed CCUS-EOR project is affected by both oil and carbon market prices, and a higher carbon price is needed to compensate for the financial setbacks, resulting from reduced oil prices. The research also features the strategic decision-making of oil fields across different sizes in response to various price scenarios. The medium-sized fields consistently prioritize carbon injection for enhanced oil recovery (EOR) over market trading. In contrast, small and giant fields adapt their approach based on the prevailing market price dynamics.

Analysis of China's carbon market price fluctuation and international carbon credit financing mechanism using random forest model.

This study aims to investigate the price changes in the carbon trading market and the development of international carbon credits in-depth. To achieve this goal, operational principles of the international carbon credit financing mechanism are considered, and time series models were employed to forecast carbon trading prices. Specifically, an ARIMA(1,1,1)-GARCH(1,1) model, which combines the Generalized Autoregressive Conditional Heteroskedasticity (GARCH) and Autoregressive Integrated Moving Average (ARIMA) models, is established. Additionally, a multivariate dynamic regression Autoregressive Integrated Moving Average with Exogenous Inputs (ARIMAX) model is utilized. In tandem with the modeling, a data index system is developed, encompassing various factors that influence carbon market trading prices. The random forest algorithm is then applied for feature selection, effectively identifying features with high scores and eliminating low-score features. The research findings reveal that the ARIMAX Least Absolute Shrinkage and Selection Operator (LASSO) model exhibits high forecasting accuracy for time series data. The model's Mean Squared Error, Root Mean Squared Error, and Mean Absolute Error are reported as 0.022, 0.1344, and 0.1543, respectively, approaching zero and surpassing other evaluation models in predictive accuracy. The goodness of fit for the national carbon market price forecasting model is calculated as 0.9567, indicating that the selected features strongly explain the trading prices of the carbon emission rights market. This study introduces innovation by conducting a comprehensive analysis of multi-dimensional data and leveraging the random forest model to explore non-linear relationships among data. This approach offers a novel solution for investigating the complex relationship between the carbon market and the carbon credit financing mechanism.

ETradeChain: Blockchain-Based Energy Trading in Local Energy Market (LEM) Using Modified Double Auction Protocol

ETradeChain: Blockchain-Based Energy Trading in Local Energy Market (LEM) Using Modified Double Auction Protocol

Production Choices and Food Security: A Review of Studies Based on a Micro-Diversity Perspective.

Given the 'subsistence' character of smallholder production, agricultural production diversification is often seen as an effective strategy for smallholders to improve their diets' diversity and nutritional status, yet the existing evidence remains inadequate. The study applies bibliometric data from the "Web of Science" database to synthesize 46 papers from developing countries to explore the relationship between production diversity, dietary diversity, and nutrition in smallholder households. The study identifies the most influential journals, authors, organizations, and countries and reveals research themes related to agricultural production and food security. This data analysis can help researchers target potential collaborators and access influential literature in agricultural production diversity and dietary diversity research. In addition, the results showed that agricultural production diversity potentially influences households' dietary diversity, with mixed results: Agricultural production diversification is the primary way to improve food and nutritional security among smallholder families with low socio-economic status, inaccessible transportation, and poverty; market access and trade have more potential to improve dietary diversity among smallholder households with well-developed markets and higher income levels; the significant measures of agricultural production diversity include Crop Counts, FGPD, SI, and SWDI; the significant measures of dietary diversity include HDDS and IDDS. This paper provides a roadmap for agricultural production and food security researchers by conducting a systematic review of the literature, summarizing some research methods and perspectives applicable to local socio-economic development.

Revolutionizing trade finance: leveraging the power of blockchain and AI in electronic letters of credit

Abstract This article examines the innovative combination of blockchain and artificial intelligence (AI) technologies in the field of electronic letters of credit and international trade finance. It explores how the combined effect of this convergence may greatly improve the security, effectiveness, and clarity of trade finance procedures. The article presents a suggested framework for combining various technologies, focusing on important design considerations including security, trust, interoperability, and adherence to international trade norms. The technological design blends blockchain’s decentralized ledger with AI’s analytical capabilities, highlighting the need of smart contracts, data management, and application programming interfaces for smooth interoperability. The implementation plan and stages are meticulously detailed, emphasizing the systematic approach necessary for effective integration. The article also discusses the problems and dangers related to this technology integration, such as technical obstacles, regulatory compliance, security threats, stakeholder acceptance, and cost factors. The conclusion outlines the impact of blockchain and AI on trade finance, discusses their larger implications for international commerce, and advocates for the adoption of these advanced technologies by collective action. This article seeks to provide significant insights to financial institutions, policy-makers, technologists, and stakeholders in the trade finance sector, promoting the modernization of trade finance via new technology.