Search Results (11,519)

Financial time series forecasting remains a challenging task due to the high non-stationarity and concept drift inherent to market data. Existing deep learning models, such as LSTMs and transformers, typically employ static weights after training, limiting their ability to adapt to rapid market regime shifts (e.g., from trends to reversals). To bridge this gap between static parameters and dynamic environments, we propose a novel framework named Game-Theoretic HyperNetwork (GTH-Net), which introduces a context-aware meta-learning mechanism to achieve adaptive forecasting. Specifically, we first introduce an Evolutionary Game-Theoretic Correction Module (E-GTCM) to explicitly extract latent buying and selling pressure based on market microstructure priors through an iterative gated evolution process. Subsequently, we propose a HyperNetwork-based fusion mechanism that treats the extracted game state as a meta-context to dynamically generate the weights of the forecasting head. This allows the model to automatically switch its prediction rules in response to shifting market regimes. Extensive experiments on real-world stock datasets demonstrate that GTH-Net significantly outperforms baselines in terms of machine learning predictive accuracy and simulated financial profitability. Furthermore, ablation studies and parameter analysis confirm that the dynamic weight generation mechanism effectively captures market reversals caused by overcrowded trades. Full article

Unpaid caregivers in Hong Kong, China (Hong Kong) are known to be under tremendous stress. The government of the Hong Kong Special Administrative Region (SAR) has been funnelling resources to non-profit organisations to support these caregivers in recent years. Since 2020, the British government has provided British National (Overseas) passport holders with a pathway to gain citizenship in Britain, and many Hong Kongers, especially young families, have migrated to Britain. This migration includes many former caregivers of older adults who remain in Hong Kong. How do these left-behind elderly parents comprehend the loss of their main caregivers, an extreme case of empty nest? And how do faith-based, especially Evangelical Christian, organisations and churches, support these older adults and their adult children in transnational caregiving? This study employs an ethnographic approach through on-site fieldwork and semi-structured interviews with older adults whose children migrated abroad, social workers at faith-based organisations, and church pastors. These field observations and interviews are supplemented by case studies and interviews published in news outlets. Through this group study, though limited in sample size, this article argues for the importance of faith identity and religious community in supporting both older adults and their caregivers, whether situated locally or remotely, and how faith-based organisations support transnational caregiving through connecting both parties. Full article

The rapid rise of live-streaming e-commerce has fostered a new “content clipping” model, in which secondary creators edit and republish anchors’ live-streaming content to promote product sales. While this model can expand market reach and enhance revenue, it also introduces copyright disputes, regulatory challenges, and profit-sharing conflicts among platforms, anchors, and secondary creators. This study develops a three-party evolutionary game model to examine strategic choices regarding platform regulation, anchor authorization, and secondary content creation. Results reveal that excessive regulation may undermine equilibrium and profitability, while appropriate authorization can balance risk and reward. Secondary creators’ participation is sensitive to commission rates and cost–benefit trade-offs. This research contributes to the literature by integrating copyright governance into live-streaming e-commerce game theory and offers actionable insights for designing regulatory mechanisms, optimizing authorization policies, and fostering sustainable multi-party collaboration. Full article

The media industry has undergone a myriad of challenges in recent years and in Ireland the impact of those challenges has been particularly acute in the regional print press. Changes in media consumption patterns, a shift from mainstream and digital media, advances in technology, reduced income from advertising and a decrease in newspaper circulation have all had a significant impact on the regional print press in Ireland. Semi-structured interviews were carried out with five owners and five editors of regional print newspapers. An overall negative view of the future of the industry, from a regional print perspective, was found. Survival is a priority and a lack of resources is a concern, as recruitment of staff—journalists and photojournalists—is limited or non-existent. All participants cited lack of revenue from advertising and struggles to generate any profit from online advertising as major concerns. While all maintained that editorial independence is a priority, in reality, the separation between newsrooms and commercial sections of media organisations has become less pronounced, amid commercial realities which are a source of disquiet. Full article

This paper introduces Historical State Reconstruction, a novel framework for low-latency financial decision-making using Large Language Models. While agentic systems have demonstrated potential in synthesizing complex financial narratives, they typically rely on Retrieval-Augmented Generation or memory-based architectures. These paradigms introduce significant latency and risk look-ahead bias during real-time inference, rendering them unsuitable for high-frequency trading environments where milliseconds determine profitability. This proposed framework resolves this bottleneck by decoupling the heavy computational cost of context acquisition from the latency-sensitive critical path of decision-making. We propose a system that proactively compiles unstructured regulatory filings (10-K, 10-Q, 8-K) into a structured, bitemporal database. By pre-computing complex state facets, such as financial health ratios, governance structures, and insider trading signals offline, the system allows trading agents to “time travel” to a reconstructed state at any historical moment t with $O(1)$ snapshot retrieval plus $O(k)$ delta application complexity. We implement this approach on the top 50 companies in the S&P 500 ranked by market capitalization, processing over 12,000 filings to demonstrate a pipeline that transforms high-dimensional financial narratives into compact, prompt-ready context. Our evaluation shows that the system reduces context retrieval latency by over 97% compared to traditional baselines while achieving a 300:1 compression ratio for financial health data. Furthermore, the bitemporal architecture guarantees strict temporal integrity, eliminating the risk of data leakage in backtesting and satisfying the reproducibility requirements of regulatory frameworks like SR 11-7. Full article

This study investigates the determinants of capital structure in Moroccan agricultural SMEs, with particular emphasis on the distinction between interest-bearing debt and non-interest-bearing liabilities in a context characterized by persistent credit constraints. While traditional capital structure theories typically treat debt as a homogeneous aggregate, such an approach may obscure important financing dynamics in financially constrained environments. Using a panel dataset of 52 agricultural SMEs observed over the period 2017–2022, the analysis employs a correlated random effects model to control for unobserved heterogeneity. The results indicate a negative relationship between profitability and both total and short-term debt, consistent with the predictions of the Pecking Order Theory. Liquidity, asset tangibility, and firm size are negatively associated with non-interest-bearing current liabilities, suggesting that trade-based financing may serve as an adjustment mechanism when access to formal credit is limited. In contrast, long-term debt is only weakly explained by firm-level characteristics, pointing to potential supply-side constraints in agricultural credit markets. Overall, the findings suggest that financing patterns in agricultural SMEs appear to be more closely associated with credit market imperfections than with optimal trade-off considerations. By distinguishing between different debt components, this study contributes to the literature by highlighting the importance of debt composition when analyzing capital structure in emerging and financially constrained economies. Full article

Chile stands out for its renewable energy resources and its commitment to developing green hydrogen. However, achieving cost parity with gray hydrogen remains an obstacle, mainly due to high capital costs and sensitivity to scale. This study assesses the technical and economic feasibility of green hydrogen production, using five different plants located in the Magallanes region in the south of the country as a reference. The model integrates a detailed framework of wind generation, PEM electrolysis, compression, and high-pressure storage subsystems, as well as a stochastic economic layer that combines the CAPEX, NPV, and LCOH assessments using Monte Carlo simulations. It also incorporates real-world capacity distributions and probabilistic fluctuations in systems. A sensitivity analysis confirms production scale as the main factor affecting profitability, with a break-even threshold of 0.5 MW. The results show that the LCOH decreases from 7.1 USD to 3.4 USD/kgH₂ as capacity increases. The analysis reveals that only 23.88% of small-scale configurations yield positive NPV, underscoring the need for scaling to achieve economic viability. Full article

This paper develops an evaluation of profitability for firms in Greece and Cyprus from 2005 to 2020. More specifically, it contains an investigation of comparative relevance and dominance of accounting versus non-financial variables, which affect the daily operations of firms, on the firms’ level of profitability. Moreover, this research examines the impact of corruption, unemployment, part-time employment and Research and Development (R&D) on the performance of companies, in order to help managers by giving them more information and assisting in long-term strategic planning. The results indicate that these variables do not have a large effect on the firm-level profitability of these two countries, which is largely influenced by profit margin and other interaction variables, such as profit margin on asset turnover ratio and equity multiplier. The findings underline that internal operational efficiency acts as the primary driver of short-term profitability, whereas macro-level indicators display weaker immediate associations. However, managing these structural elements remains strategically relevant for long-term springiness. Full article

Promoting resilience, increasing productivity and sustainability, and profit maximization remain key challenges facing farmers globally. These are exacerbated by factors such as climate change, low to no access to technological advancement, financial constraints, poor technical and management skills, inadequate government support, and limited access to resources. However, there are diverse strategies that abound, including on-farm diversification, that farmers could leverage on to address these numerous and complex challenges. This study investigated the determinants of on-farm diversification strategies among smallholders in Mpumalanga Province. The study employed a quantitative approach using closed-ended survey questionnaires to elicit information from a total of 465 farmers who were randomly sampled from a total population of 14,411. The data gathered were analysed using descriptive statistics to determine the on-farm diversification strategies employed by farmers and the factors influencing the use of these strategies. A binary logistic regression model was employed to establish the relationship between on-farm diversification strategies and the determining factors. More than half of the farmers were female (51.8%), with only 48.2% male. The majority (59.1%) of the farmers were between the ages of 36 and 60, with only 20.2% youth participation in farming. Slightly more than half (50.8%) of the farmers practise mixed farming as their on-farm diversification strategy, while only 4.3% of the farmers practise mono-cropping. The study identified significant variables such as level of education (p = 0.001), secondary source of income (p = 0.057), farmland size (p = 0.022), number of farm assistants (p = 0.016), and on-farm diversification awareness as key determinants of on-farm diversification among smallholder farmers in Mpumalanga Province. Therefore, it is recommended that policies within the agricultural sector be revised to encourage on-farm diversification in order to motivate farmers to transition to agripreneurship for poverty alleviation, food security and rural economic development (RED). Full article

Driven by the global energy transition, the proportion of new and renewable sources of energy (NRSE) such as wind and solar power in the electricity systems of many countries continues to rise. However, this also exacerbates frequency fluctuations in the power system, giving rise to new issues such as curtailment of wind and solar power generation and a continuous decline in inertia levels. The hybrid energy storage system composed of a flywheel and a battery can fully utilize the advantages of their power and energy characteristics, respectively, becoming an effective solution to this problem. Firstly, the characteristics of NRSE and various energy storage technologies were introduced in the paper. Then, the frequency regulation requirements and process of NRSE were discussed, as well as the common architecture and control methods of flywheel–battery hybrid energy storage systems, and the application research and current development status of the flywheel–battery hybrid energy storage system on the power supply side and grid side of the power system were elaborated, including the control strategies for participating in NRSE and methods to reduce costs and increase profits. Finally, the future research directions of flywheel–battery hybrid energy storage systems were discussed and anticipated. Full article

Stock price prediction remains a prominent area of interest among investors due to its potential impact on financial decision making. We developed a deep learning-based system for stock market analysis, forecasting, and automated trading. Utilizing historical financial data, technical indicators, and sentiment information, long short-term memory (LSTM) networks were employed to model and predict stock price movements. The predicted outcomes were integrated into a rule-based automated trading system to simulate real-time buy and sell decisions. Experimental evaluations conducted on the Taiwan Stock Exchange (TWSE) indicate that the developed model surpasses baseline models in both prediction accuracy and trading profitability. The system presents the capability of deep learning to improve forecasting precision and facilitate intelligent, automated trading strategies within contemporary financial markets. Full article

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article

This study analyzes how sustainability practices and digitalization jointly influence the financial performance of European industrial firms, emphasizing differences between Western and Eastern Europe. The empirical analysis relies on a large multi-country panel dataset and employs fixed effects regression models with robust standard errors to account for unobserved firm-specific heterogeneity and common time shocks. Environmental sustainability is captured by the environmental component of ESG scores, digitalization is measured by digital investment intensity, and financial performance is proxied by return on equity (ROE). The findings indicate that stronger environmental practices are positively associated with profitability across the full sample. Digital investment intensity also has a positive and statistically significant effect on ROE. Importantly, the interaction term between environmental performance and digitalization is positive and significant for Western European firms but not for the full sample, suggesting that the relationship between environmental practices and financial performance may vary with the level of digital investment under specific regional conditions. However, the results reveal substantial regional heterogeneity. The positive effects of environmental practices, digitalization, and their interaction are primarily driven by firms in Western Europe, whereas the relationships are weaker and statistically insignificant in Eastern Europe. These findings underline the complementary role of digital transformation and the importance of institutional and technological readiness. Full article

China is one of the world’s leading producers of celery. In recent years, the market price of celery has often experienced rollercoaster-like fluctuations. Such volatility has become a significant factor affecting the income of vegetable farmers, market stability, and household consumption. The key to addressing this issue lies in understanding the threshold of the celery planting area at which supply and demand are balanced. However, relevant research has been rarely conducted on this topic to date. Shandong Province is a major vegetable-producing region in China, and its celery output and pricing have a crucial impact on the national market. Therefore, this study takes Yucheng City, Shandong Province, as a case study. By leveraging the land vacancy characteristics before the celery planting period, the NDVI data was calculated, and the object-based supervised classification was used to extract the celery planting area from remote sensing imagery. Based on a comprehensive statistical analysis of collected annual celery wholesale prices and break-even prices over the past decade, it was found that when the autumn celery planting area in the study region exceeds 12,000 hectares, oversupply occurs, leading to losses for celery farmers. Moreover, this situation recurs approximately every four years. To prevent celery oversupply, the government should estimate the prospective celery planting area using remote sensing imagery during the one-month land vacancy period before celery transplantation. Once the estimated data reach or exceed the supply–demand balance threshold, proactive guidance should be provided to encourage celery farmers to switch to other vegetables, thereby reducing potential losses for farmers. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices. This study could also maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. This study provides an effective method for the government to intervene in the cultivation of crops with highly volatile prices and could enable farmers to achieve sustained profitability. The sustainable profit could maintain the vegetable production at a constant level and make the celery plantation sustainable in the future. Full article

Produced-water (PW) management in the Permian Basin faces tightening injection constraints, induced seismicity concerns, and volatile saltwater disposal (SWD) costs. At the same time, chemistry-rich PW contains dissolved constituents (e.g., Li, B, and Sr) that may be valorized if SWD recovery performance and market conditions support favorable techno-economics. Here, we develop an integrated decision-support framework that couples (i) chemistry-informed surrogate models for unit process performance (recovery, effluent quality, and energy/chemical intensity) with (ii) a network-based allocation model that routes PW from sources through pretreatment, optional treatment and mineral-recovery modules (e.g., desalination and direct lithium extraction), and end-use nodes (beneficial reuse, hydraulic fracturing reuse, mineral recovery/valorization, or Class II disposal). This is a screening-level demonstration using publicly available chemistry percentiles and representative pilot-reported performance windows; it is not a site-specific facility design or a bankable TEA for a particular operator. The optimization is posed as a tri-objective problem—to maximize expected net present value, minimize SWD, and minimize an injection-risk indicator R—subject to mass balance, capacity, quality, and regulatory constraints. Uncertainty in commodity prices, recovery fractions, and operating costs is propagated via Monte Carlo scenario sampling, yielding PARETO-efficient portfolios that quantify trade-offs between profitability and risk mitigation. Using the PW chemistry percentiles reported by the Texas Produced Water Consortium for the Delaware and Midland Basins, we derive screening-level break-even lithium concentrations and illustrate how lithium-carbonate-equivalent price and recovery govern the extent to which mineral revenue can offset SWD expenditures. Comparative brine benchmarks (Smackover Formation and Salton Sea geothermal systems) contextualize the Permian’s generally lower-Li PW and highlight transferability of the workflow across brine types. The proposed framework provides a transparent, extensible basis for design matrix planning under evolving injection limits, enabling risk-aware PW management strategies that reduce disposal dependence while improving water resilience. Full article