Search Results (44)

This study investigated the forecasting performance of the South African gold sales series using the seasonal autoregressive integrated moving average (SARIMA), generalised autoregressive conditionally heteroscedastic (GARCH), general regression neural network (GRNN) and artificial neural network (ANN)-based extreme learning machine (ELM). This study employed traditional methods and a recently developed ML method for single hidden-layer feed-forward neural networks (SLFNs). The findings revealed that $\mathrm{SARIMA} \left(0,1,1\right){\left(2,1,2\right)}_{12}$ was considered the best model for the gold sales series. The empirical findings demonstrated that the SARIMA model outperforms neural network-based models, providing the South African government and its lenders with a more reliable and cost-effective tool for predicting foreign exchange earnings from gold. This study contributes to the literature by providing one of the first comparative evaluations of traditional time-series models and advanced neural network methods for forecasting South African gold sales. This study is novel as it is a first-of-its-kind comparative application of traditional SARIMA and GARCH models alongside GRNN and ANN-based ELM methods to South African gold sales, revealing the superior forecasting performance of a traditional SARIMA model over advanced ML approaches. Future research should explore the development and application of hybrid models that integrate the strengths of linear SARIMA frameworks with the pattern-recognition capabilities of nonlinear ANN-based ELM models. Full article

This study proposes a machine learning framework tailored to the institutional characteristics of Taiwan’s stock market, aiming to enhance forecasting accuracy for the Taiwan Stock Exchange Capitalization Weighted Stock Index (TAIEX). The model employs the Radius Neighbors Regressor with a dynamic radius-based similarity measure and integrates domain-specific features including technical indicators, volume–price relationships, and Qualified Foreign Institutional Investor (QFII) activity. A custom 60-day input window with a 20-day forecast horizon is applied to capture medium-term market dynamics. The framework was evaluated through extensive backtesting and real-world validation with the TAIEX Futures. The results demonstrate that the model achieves a peak directional accuracy of 85.1% under optimal parameter settings. Moreover, trading simulations confirm its practical viability, yielding a cumulative return on investment (ROI) of approximately 1600% during the short-term evaluation period (2023–2025) and nearly 2000% in the long-term evaluation (2019–2025), even after accounting for transaction costs and stop-loss mechanisms. These findings indicate that combining historical pattern similarity with institutional investor behavior substantially improves predictive power and profitability. Nevertheless, the framework remains constrained by its reliance on Taiwan-specific institutional features and historical trading data, limiting generalizability. Future research should extend applications to other markets while incorporating macroeconomic variables, corporate fundamentals, and news-driven signals to enhance adaptability. Full article

Foreign exchange is a significant financial market that attracts investors and countries seeking profitable investments. Despite the numerous techniques available for exchange rate forecasting and trend analysis, there is still a need for an automated, intelligent model to understand patterns and predict future trends. The creation of such prediction models can provide assistance for investors, financial institutions, and policymakers in governments. To overcome these issues, the proposed study has developed a novel hybrid deep learning model that encompasses a Bidirectional Long Short-Term Memory, an additive attention approach, and a random forest regressor (for long-horizon historical data), attempting to provide a prediction model for the following year’s official exchange rates (LCU per USD). The random forest regressor models the nonlinear interaction of features and assists with generalization, the attention layer focuses on the most influential time steps, and the Bidirectional Long Short-Term Memory (Bi-LSTM) captures all historical data for exchange rate series and temporal dependencies (or dependencies of a sequence of historical data). The use of a time partition (1960–2018 training data + 2019–2023 validation data + 2024 testing data) to train and evaluate the model provides realistic forecasting and prevents temporal leakage. The global panel dataset for more than 250 and 60+ year countries and regions demonstrate that all of the proposed models are better than all classical machine learning models, stand-alone deep learning models, and naive persistence models. The hybrid model shows the most significant prediction error reduction with R² as 0.98, proving long-horizon currency forecasting is extremely robust. Full article

Compliant cross-border data flows face persistent challenges from fragmented regulatory regimes, inconsistent enforcement, and limited trust among stakeholders. Current approaches typically rely on centralized oversight or excessive data disclosure, both compromising regulatory interoperability and operational security. This paper introduces a trust-oriented blockchain architecture that enables secure cross-border data exchange while ensuring verifiable compliance without revealing sensitive content. The architecture decouples policy enforcement, privacy-preserving validation, and cross-jurisdiction auditability, enabling entities to share cryptographically verifiable compliance proofs rather than raw data. To capture the behavioral dynamics across heterogeneous regulatory environments, we incorporate a strategic interaction layer that models how domestic firms, foreign enterprises, and cross-border data platforms adjust decisions under varying incentive structures. These insights guide the design of an adaptive compliance verification pipeline that maintains trust equilibrium across participants. Our design records only cryptographic digests and structured compliance evidence on-chain, while off-chain components execute privacy-preserving checks using secure computation and decentralized storage. Through a case-driven evaluation, we show that the proposed architecture reduces governance friction, enhances institutional trust, and achieves interoperable compliance validation with minimal disclosure overhead. Through component-level evaluation and architectural analysis, this work establishes a technical foundation for secure, transparent, and regulation-aligned cross-border data governance. The framework provides a blueprint for future multi-party pilot deployments in operational environments. Full article

Grafting is the process of joining parts of two plants, allowing the exchange of molecules such as small RNAs (including microRNAs and small interfering RNAs), messenger RNAs, and proteins between the rootstock and the scion. Genome editing by grafting exploits RNAs, such as tRNA-like sequences (TLS motifs), to deliver the components (RNA) of the clustered regularly interspaced short palindromic repeats (CRISPR)-associated protein 9 (Cas9) system from transgenic rootstock to wild-type scion. The complex Cas9 protein and sgRNA-TLS produced in the scion perform the desired modification without the integration of foreign DNA in the plant genome, resulting in heritable transgene-free genome editing. In this review, we examine the current state of the art of this innovation and how it helps address regulatory problems, improves crop recovery and selection, exceeds the usage of viral vectors, and may reduce potential off-target effects. We also discuss the promise of genome editing by grafting for plants recalcitrant to in vitro culture and for agamic-propagated species that must maintain heterozygosity for plant productivity, fruit quality, and adaptation. Furthermore, we explore the limitations of this technique, including variable efficiency, graft incompatibility among genotypes, and challenges in large-scale application, while highlighting its considerable potential for further improvement and future broader applications for crop breeding. Full article

The COVID-19 pandemic and the implementation of strict lockdown measures have significantly impacted various dimensions of the global economy. This study examines the impact of COVID-19 and lockdown stringency on exchange rate volatility in India using three core variables, i.e., COVID-19 cases, the lockdown stringency index, and exchange rate volatility. To achieve the above objectives, we have employed advanced econometric techniques, such as wavelet coherence and a hybrid non-parametric quantile causality framework, on the dataset spanning from 30 December 2020 to 24 January 2022. Robustness is assessed using Troster–Granger causality in quantiles and Breitung–Candelon Spectral Causality tests. The wavelet coherence analysis indicates that the initial outbreak of COVID-19 increased the exchange rate volatility, while the enforcement of stringent lockdowns in the later phases helped reduce this volatility. Similarly, the hybrid quantile causality results indicate that both COVID-19 cases and lockdown measures possess predictive power over exchange rate fluctuations. The robustness checks confirm these findings and establish a causal relationship between the pandemic, policy responses, and currency market behaviour. This study helps clarify the complex, nonlinear dynamics between pandemic-related variables and exchange rate volatility in emerging markets. Based on the aforementioned result, it is recommended that policymakers implement targeted lockdown strategies coupled with timely monetary interventions (such as foreign exchange reserve management or interest rate adjustments) to mitigate volatility and maintain currency stability during future pandemic-induced shocks. Full article

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most widely used predictive models, particularly LSTM, GRU, XGBoost, and hybrid deep learning architectures, as well as key evaluation metrics, such as RMSE and MAPE. The findings confirm that AI-based approaches, especially neural networks, outperform traditional statistical methods in capturing non-linear and high-dimensional dynamics. However, the analysis also reveals several critical research gaps. Most notably, current models are rarely embedded into real or simulated trading strategies, limiting their practical applicability. Furthermore, the sensitivity of widely used metrics like MAPE to volatility remains underexplored, particularly in highly unstable environments such as crypto markets. Temporal robustness is also a concern, as many studies fail to validate their models across different market regimes. While data covering one to ten years is most common, few studies assess performance stability over time. By highlighting these limitations, this review not only synthesizes the current state of the art but also outlines essential directions for future research. Specifically, it calls for greater emphasis on model interpretability, strategy-level evaluation, and volatility-aware validation frameworks, thereby contributing to the advancement of AI’s real-world utility in financial forecasting. Full article

We propose the financial generative adversarial network–bidirectional long short-term memory (FINGAN-BiLSTM) model to accurately reproduce the complex statistical properties and stylized facts, namely, heavy-tailed behavior, volatility clustering, and leverage effects observed in the log returns of the foreign exchange (FX) market. The proposed model integrates a bidirectional LSTM (BiLSTM) into the conventional FINGAN framework so that the generator, discriminator, and predictor networks simultaneously incorporate both past and future information, thereby overcoming the information loss inherent in unidirectional LSTM architectures. Experimental results, assessed using metrics such as the Kolmogorov–Smirnov statistic, demonstrate that FINGAN-BiLSTM effectively mimics the distributional and dynamic patterns of actual FX data. In particular, the model significantly reduces the maximum cumulative distribution discrepancy in assets with high standard deviations and extreme values, such as the Canadian dollar (CAD) and the Mexican Peso (MXN), while precisely replicating dynamic features like volatility clustering and leverage effects, thereby outperforming conventional models. The findings suggest that the proposed deep learning–based forecasting model holds significant promise for practical applications in financial risk assessment, derivative pricing, and portfolio optimization, and they highlight the need for further research to enhance its generalization capabilities through the integration of exogenous economic variables. Full article

Debt-for-nature instruments are financial transactions that allow countries to restructure and reduce foreign debt in exchange for investments in environmental conservation measures. Can debt-for-nature instruments attract more capital for biodiversity finance? Debt-for-nature instruments first appeared in the market in the 1980s; however, they have seen a recent surge in popularity, with transactions predominantly focused on marine conservation. These transactions have gained attention for their size, innovative nature, and conservation focus. However, they have also faced criticism surrounding sovereignty, effectiveness, and transaction costs. The descriptive qualitative analysis of a comprehensive and global sample of the eight tripartite type debt-for-nature instruments brought to market since 2015, with a detailed case study of the Belize transaction, indicates that such deals may be costly to negotiate, the use of blue bond labeling can be misleading, conservation benefits are limited, and they have limited replicability. On the positive side, these deals have introduced innovative structures to unlock additional funds for conservation. The best examples are structured with a larger financial commitment to nature and strong enforcement mechanisms. In some cases, the transaction laid the groundwork for future marine conservation funding and commitments. Debt-for-nature instruments are not a silver bullet for either environmental impact or debt refinancing; however, the benefits of recent transactions indicate a role for such innovative instruments in conservation finance. Full article

Predicting foreign exchange prices is a challenging yet important task due to the complex, volatile, and fluctuating nature of the data. Although deep learning models are efficient, accurate predictions of closing prices and future price directions remain difficult. This study proposes a dual-input deep-learning long short-term memory (LSTM) model for forecasting the EUR/USD closing price and predicting price direction using both fundamental and technical indicators. The model outperforms the second-best model, achieving a 29% reduction in mean absolute error (MAE) and root mean squared error (RMSE) in the training set and reductions of 24% and 23% in MAE and RMSE, respectively, in the test set. These results are confirmed through forecasting simulations, where performance metrics are consistent with those from the training phase. Finally, the model generates reliable three-day price forecasts, providing valuable insights into price direction. Full article

This article reports on a case study where students of Initial Teacher Education in Spain and Germany collaborated with in-service teachers from around Europe on the theme of Foreign Language materials development. It examines to what extent engagement in this model of virtual exchange contributes to student teachers’ and in-service teachers’ intercultural and didactic competence development. The study also explores how students’ perspectives on teaching foreign languages and their future profession change through collaboration with in-service teachers and how the student teachers’ and in-service teachers’ roles unfold in this type of collaboration. It is based on a qualitative content analysis of focus-group interviews, learning portfolios, recordings of online meetings, and questionnaires with open-ended questions. The findings indicate that this type of collaboration can reduce the gap between theory and practice: through the classroom experiences of in-service teachers, student teachers gain intercultural, professional knowledge and motivation, while in-service teachers gain knowledge about recent methodologies and technology through the alternative perspective of student teachers. We use the findings of our study to make recommendations on how other teacher trainers can use this Virtual Exchange model in the classroom. Full article

Occupying the prestigious second place globally, the readymade export garment industry is the most important manufacturing and export industry in Bangladesh. The industry took root in the 1980s and has subsequently grown very rapidly since the 1990s, and now it contributes significantly to employment, income, exports, foreign exchange earnings, national output, and the overall social and economic development of the country. This paper focuses on the growth and development of the industry over the years, along with a critical discussion of some major social sustainability challenged facing this industry, particularly those pertaining to child labor issues, worker income, the gender gap, and worker and workplace safety, among others. The paper uses a mixture of quantitative and non-quantitative analysis and utilizes data and information from secondary sources to analyze the issues. The authors fill a critical gap in the existing literature on social sustainability challenges facing the export garment industry and finds that the country has made significant progress; however, however, more needs to be done improve social sustainability. The authors argue that not addressing these challenges, particularly external ones, may threaten the long-term viability and sustainability of this industry in the global stage. The paper discusses the progress made in these fronts, and the prospects for the future, so that the industry continue to play a pivotal role at the global stage and in the overall economic, social, and human development of the country. Full article

Background: Increasing evidence shows that the COVID-19 pandemic has disproportionately impacted certain populations, particularly those facing structural marginalization, such as immigrants and refugees. Additionally, research highlights that structurally marginalized populations living with chronic conditions, such as cancer and/or mental health and addiction (MH&A) disorders, are more vulnerable to the adverse effects of COVID-19. These individuals face higher susceptibility to infection and worse health outcomes, including increased rates of hospitalization, severe illness, and death. To better understand the challenges faced by people living at the intersection of social and clinical disadvantages, we organized a series of Think Tank sessions to engage stakeholders in exploring barriers and identifying community-based solutions for immigrants and refugees living with cancer and/or MH&A disorders during the current and future pandemics. Objectives: Our main objectives were to gauge how earlier findings resonated with stakeholders, to identify any gaps in the work, and to co-develop actionable solutions to safeguard health and well-being during COVID-19 and future crises. Methods: Two virtual Think Tank sessions were held in September 2023 as integrative knowledge exchange forums. The Cancer Think Tank was attended by 40 participants, while the MH&A disorders Think Tank included 41 participants. Each group comprised immigrants and refugees living with or affected by cancer (in the Cancer Think Tank) or MH&A disorders (in the MH&A disorders Think Tank), alongside service providers, policymakers, and researchers from Ontario. This paper presents the key discussions and outcomes of these sessions. Results: Participants identified and prioritized actionable strategies during the Think Tank sessions. In the Cancer Think Tank, participants emphasized the importance of leveraging foreign-trained healthcare providers to address workforce shortages, creating clinical health ambassadors to bridge gaps in care, and connecting immigrants with healthcare providers immediately upon their arrival in Canada. In the MH&A disorders Think Tank, participants highlighted the need to remove silos by fostering intersectoral collaboration, empowering communities and building capacity to support mental health, and moving away from one-size-fits-all approaches to develop tailored interventions that better address diverse needs. Conclusions: The Think Tank sessions enhanced our understanding of how the COVID-19 pandemic has impacted immigrants and refugees living with cancer and/or MH&A disorders. The insights gained informed a series of actionable recommendations to address the unique needs of these populations during the current pandemic and in future public health crises. Full article

Following the introduction of EUR/USD futures and USD/JPY futures on 31 October 2022, Thailand Futures Exchange first entered the top 11 list of derivatives exchanges based on foreign exchange derivative volumes in 2022. This paper investigates the dynamics of foreign exchange futures trading volumes in Thailand through the VAR(2) model. Trading volumes of EUR/USD futures, USD/JPY futures, and USD/THB futures are considered over the sample period from 31 October 2022 to 12 January 2024. The empirical results provide no evidence that the trading volume of EUR/USD futures is dependent on the past trading volumes of USD/JPY futures and USD/THB futures. The Granger causality test results show the existence of bidirectional causality between the trading volumes of USD/JPY futures and USD/THB futures. The results of the impulse response function are consistent with the sign results of the VAR(2) model, showing that the USD/JPY futures trading volume has a negative impact on the USD/THB futures trading volume, and vice versa. The analysis of variance decomposition shows that the variability of the USD/JPY futures trading volume and USD/THB futures trading volume, apart from its own shock, is explained by other FX futures trading volume shocks. Therefore, traders should pay more attention to new FX futures trading activity due to its negative impact on the USD/THB futures trading volume and its contribution to the variance in the USD/THB futures trading volume. Understanding the futures trading volume relationship also helps Thailand Futures Exchange develop new products and services that can foster market liquidity and stability. Full article

The level of coordination between cultural, ecological, and economic systems directly affects the sustainable development of the Yellow River Basin (YRB). However, researchers have neglected the importance of cultural elements in the social-ecological system and have paid insufficient attention to the interaction of cultural, ecological, and economic systems in the YRB. Therefore, a framework of coupled cultural-ecological-economic (CEE) systems was constructed based on service-dominant logic, and the spatiotemporal distribution, evolutionary trends, and factors influencing the coupled coordination of different systems in 76 major cities in the YRB were analyzed by using an entropy-weighted TOPSIS model, coupled coordination model, spatial Markov chain, and panel spatial Dubin model. The results were as follows: (1) the cultural, ecological, and economic systems of the YRB showed a growing trend, the economic system grew faster than the cultural system and the ecosystem, and the ecosystems dominated sustainable development in the YRB. (2) From 2011 to 2022, the type of coupled CEE system coordination in the YRB was mainly in a state of slight incongruity, with the different regions showing temporal consistency and synchronized growth, with the upstream area mainly in a state of moderate incongruity, the midstream area mainly in a state of slight incongruity, and the downstream area concentrating in general coordination. (3) The spatial coordination level of CEE system coupling in the YRB showed the characteristic of “gradually converging to coordination from upstream to downstream” and exhibited upstream low-value agglomeration and downstream high-value agglomeration. Meanwhile, there was a clear trend of spatial spillover in terms of balanced regional development, and 67.11% of the cities in the region and neighboring areas maintained stable development. (4) Tourism development (TD), foreign trade (FT), the human environment (HE), government control (GC), and other factors significantly positively impacted the sustainable development in the YRB. In the future, the focus should be on improving the transregional infrastructure and transportation service systems in the YRB, to enhance cooperation and exchanges between different regions. This research provides new insights and methods for the coordinated development of cultural, ecological, and economic systems at a watershed scale. Full article