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\"New media is playing an important role in the financial world. Rapid growth in stock market message boards, chat rooms, and other electronic means for investors to share market information makes clear the ever-increasing demand for online stock trading. In addition to an increasing number of related sites and apps, growth in the number of investors participating has exploded. The U.S. Securities and Exchange Commission and the Federal Trade Commission are especially interested in tracking the activities on stock market message boards in order to protect market credibility.Stock Message Boards provides empirical data to reveal how online communication not only impacts stock returns, but also volatility, trading volume, and liquidity, as well as a firm's value and reputation. Zhang demonstrates the long-term value of stock market message boards by using simple mathematics and statistics to show readers how to measure message board activities. This work argues that online message boards are more effective for small capitalization stocks than large capitalization stocks, and more prominent for financially-distressed firms than financially-sound firms\"-- Provided by publisher.

An introduction to technical analysis with a free software and data offer from one of the top names in the business This indispensable book will guide traders and individual investors through the most important-and profitable-advances in today′s investment arena.

In March 2020, the outbreak of COVID-19 precipitated one of the most significant stock market downturns in recent history. This paper explores the relationship between public sentiment related to COVID-19 and stock market fluctuations during the different phases of the pandemic. Utilizing natural language processing and sentiment analysis, we examine Twitter data for pandemic-related keywords to assess whether these sentiments can predict changes in stock market trends. Our analysis extends to additional datasets: one annotated by market experts to integrate professional financial sentiment with market dynamics, and another comprising long-term social media sentiment data to observe changes in public sentiment from the pandemic phase to the endemic phase. Our findings indicate a strong correlation between the sentiments expressed on social media and market volatility, particularly sentiments directly associated with stocks. These insights validate the effectiveness of our Sentiment(S)-LSTM model, which helps to understand the evolving dynamics between public sentiment and stock market trends from 2020 through 2023, as the situation shifts from pandemic to endemic and approaches new normalcy.

We examined what type of information network structures lie within rural cooperatives and what these structures mean for promoting resource-conserving agriculture. To better understand whether and how environmental outcomes are linked to these microlevel social relations or network structures, we quantified individual farm- and community-level biomass accumulation and carbon stocks associated with the adoption of agroforestry, a set of farming techniques for climate change mitigation, adaptation, and resilience. We also collected social network data on individual farmers across five communities. This empirical evidence was derived from primary fieldwork conducted in the Ghanaian semideciduous cocoa (Theobroma cacao)–growing region. This data set was examined using standard network analysis, combined with exponential random graph models (ERGMs). The key findings suggest that farmers with more biomass accumulation from the adoption of agroforestry practices also tend to be popular advisers to their peers at the local level. Presumably, farmers seek peers who demonstrate clear signs of achieving successful land management goals. Using ERGMs, we also show that commonly observed individual-level results might not scale to the collective level. We discuss how our individual-scale findings could be leveraged to foster farmer-to-farmer social learning and knowledge exchange associated with resource-conserving agricultural practices. However, we also highlight that effective whole networks, such as cooperative collectives in these communities, remain elusive.

Real-time, continuous and accurate blast furnace burden level information is of great significance for controlling the charging process, ensuring a smooth operation of a blast furnace, reducing energy consumption and emissions and improving blast furnace output. However, the burden level information measured by conventional mechanical stock rods and radar probes exhibit problems of weak anti-interference ability, large fluctuations in accuracy, poor stability and discontinuity. Therefore, a space-time fusion prediction and detection method of burden level based on a long-term focus memory network (LFMN) and an efficient structure self-tuning RBF neural network (ESST-RBFNN) is proposed. First, the space dimensional features are extracted by the space regression model based on radar data. Then, the LFMN is designed to predict the burden level and extract the time dimensional features. Finally, the ESST-RBFNN based on a proposed fast eigenvector space clustering algorithm (ESC) is constructed to obtain reliable and continuous burden level information with high accuracy. Both the simulation results and industrial verification indicate that the proposed method can provide real-time and continuous burden level information in real-time, which has great practical value for industrial production.

Bitcoin is one of the most well-known cryptocurrencies worldwide. Recently, as the COVID-19 pandemic raged globally, a new wave of price volatility and interest in Bitcoin was witnessed. Identifying the roles played by different information sources in the emergence and diffusion of content through Internet resources can reveal the influential factors affecting cryptocurrencies’ value. This study aims to reveal the forces behind cryptocurrencies’ monetary value—the market price movements on major exchanges before, during, and post the March 2020, COVID-19 market crash. The daily prices of the two largest cryptocurrencies, Bitcoin and Ether, were obtained from CoinDesk. By integrating Google Trends data, we found that Google searches increase when the number of tweets on COVID-19 soars, with a one-period lag (one day). Furthermore, search trends have a significant impact on cryptocurrencies’ future returns such that increased (decreased) searches for a negative event indicate lower (higher) future cryptocurrency prices.

Exploring the spatiotemporal evolution of building stock in African countries is of great significance for understanding the urbanization process, regional development disparities, and sustainable development pathways in the Global South. Integrating long-term (1975–2020), 100 m resolution building stock data for Tanzania with multi-source environmental and socioeconomic datasets, this study employed GIS spatial analysis techniques—including optimized hotspot analysis, standard deviational ellipse, and geographical detector—to investigate the spatiotemporal evolution characteristics and influencing factors of building differentiation. The results indicate that over the 45-year period, Tanzania’s building stock underwent rapid expansion, with a 3.83-fold increase in volume and a 4.93-fold increase in area, while the average height decreased continuously by 1.04 m. This growth was predominantly driven by the expansion of residential buildings. The spatial distribution of buildings exhibited a “north-dense, south-sparse” pattern with agglomeration along traffic axes. During 1975–1990, building growth hotspots were concentrated in western and southern regions, shifting to areas surrounding Lake Victoria and central administrative centers during 2005–2020. In contrast, coldspots expanded progressively from northern, northeastern regions and Zanzibar Island to parts of the southern and eastern coasts. The building distribution consistently maintained a northwest–southeast spatial orientation, with increasingly prominent directional characteristics; the centroid of building distribution moved more than 90 km northwestward, and the agglomeration intensity continued to increase. Socioeconomic factors—including population density, road network density, and GDP density—have a significantly stronger influence on building distribution than natural factors. Among natural factors, only river network density exhibits a significant effect, while constraints such as slope and terrain relief are relatively insignificant.

PurposeThe authors explicitly evaluate the dynamic impact of five most concerned supply chain disruption scenarios, including: (1) a short-term shortage and price jump of corn supply in hog farms; (2) a shortage of market hogs to packing facilities; (3) disruption in breeding stock adjustments; (4) disruption in pork import; and (5) a combination of scenario (1)–(4).Design/methodology/approachThe agricultural supply chain experienced tremendous disruptions from the COVID-19 pandemic. To evaluate the impact of disruptions, the authors employ a system dynamics model of hog market to simulate and project the impact of COVID-19 on China hog production and pork consumption. In the model the authors explicitly characterize the cyclical pattern of hog market. The hog cycle model is calibrated using market data from 2018–2019 to represent the market situation during an ongoing African swine fever.FindingsThe authors find that the impacts of supply chain disruption are generally short-lived. Market hog transportation disruption has immediate impact on price and consumption. But the impact is smoothed out in six months. Delay in import shipment temporarily reduces consumption and raises hog price. A temporary increase of corn price or delay in breeding stock acquisition does not produce significant impact on national hog market as a whole, despite mass media coverage on certain severely affected regions.Originality/valueThis is the first evaluation of short-term supply chain disruption on China hog market from COVID-19. The authors employ a system dynamics model of hog markets with an international trade component. The model allows for monthly time step analysis and projection of the COVID-19 impact over a five-year period. The results and discussion have far-reaching implications for agricultural markets around the world.

The under-performance of supply chains presents a significant hindrance to disease control in developing countries. Stock-outs of essential medicines lead to treatment interruption which can force changes in patient drug regimens, drive drug resistance and increase mortality. This study is one of few to quantitatively evaluate the effectiveness of supply chain policies in reducing shortages and costs. This study develops a systems dynamics simulation model of the downstream supply chain for amikacin, a second-line tuberculosis drug using 10 years of South African data. We evaluate current supply chain performance in terms of reliability, responsiveness and agility, following the widely-used Supply Chain Operation Reference framework. We simulate 141 scenarios that represent different combinations of supplier characteristics, inventory management strategies and demand forecasting methods to identify the Pareto optimal set of management policies that jointly minimize the number of shortages and total cost. Despite long supplier lead times and unpredictable demand, the amikacin supply chain is 98% reliable and agile enough to accommodate a 20% increase in demand without a shortage. However, this is accomplished by overstocking amikacin by 167%, which incurs high holding costs. The responsiveness of suppliers is low: only 57% of orders are delivered to the central provincial drug depot within one month. We identify three Pareto optimal safety stock management policies. Short supplier lead time can produce Pareto optimal outcomes even in the absence of other optimal policies. This study produces concrete, actionable guidelines to cost-effectively reduce stock-outs by implementing optimal supply chain policies. Preferentially selecting drug suppliers with short lead times accommodates unexpected changes in demand. Optimal supply chain management should be an essential component of national policy to reduce the mortality rate. La médiocre performance des chaînes d’approvisionnement constitue un important obstacle à la lutte contre la maladie dans les pays en développement. Les ruptures de stock des médicaments essentiels entraînent une interruption du traitement qui peut contraindre à une modification des régimes posologiques des patients, favoriser la résistance aux médicaments et accroître la mortalité. La présente étude est l’une des rares à évaluer quantitativement l’efficacité des stratégies de la chaîne d’approvisionnement dans le cadre de la réduction des pénuries et des coûts. En se fondant sur les données recueillies au cours de 10 années en Afrique du Sud, l’étude développe un modèle de simulation de la dynamique en aval de la chaîne d’approvisionnement de l’amikacine, un antituberculeux de deuxième intention. Nous évaluons les performances actuelles de la chaîne d’approvisionnement en termes de fiabilité, de réactivité et de rapidité, en suivant le cadre de référence largement utilisé dans la gestion de la chaîne d’approvisionnement. Nous simulons 141 scénarios qui représentent différentes combinaisons de caractéristiques des fournisseurs, de stratégies de gestion des stocks et de méthodes de prévision de la demande afin d’identifier l’ensemble de politiques optimales de gestion au sens de Pareto, politiques qui minimisent aussi bien le nombre de pénuries que le coût total. En dépit de longs délais de livraison des fournisseurs et une demande imprévisible, la chaîne d’approvisionnement de l’amikacine est fiable à 98% et suffisamment rapide pour répondre à une augmentation de 20% de la demande sans occasionner de pénurie. Cependant, on ne peut y parvenir qu’avec un surstockage de l’amikacine à hauteur de 167%, ce qui entraîne des surcoûts considérables. La réactivité des fournisseurs est faible: seulement 57% des commandes sont livrées au dépôt central de médicaments de la province dans un délai d’un mois. Nous identifions trois stratégies de gestion optimale des stocks de sécurité au sens de Pareto. La réduction du délai de livraison des fournisseurs peut produire des résultats optimaux au sens de Pareto, même en l’absence d’autres politiques optimales. La présente étude élabore des directives concrètes et réalisables visant à réduire efficacement les ruptures de stock en mettant en œuvre des politiques optimales dans la chaîne d’approvisionnement. Sélectionner en priorité des fournisseurs de médicaments dont les délais de livraison sont les plus courts permet de s’adapter à des modifications inattendues de la demande. La gestion optimale de la chaîne d’approvisionnement devrait être une composante essentielle de la stratégie nationale visant à réduire le taux de mortalité. 供应链不佳给发展中国家疾病控制带来了严重阻碍。基本药 物短缺会干扰治疗, 导致患者用药方案被迫改变, 增加耐药和 死亡率。鲜有研究定量评估供应链政策在减少短缺和降低成 本方面的有效性, 本研究是其中之一。本研究采用南非的10年 数据, 建立二线抗结核药阿米卡星下游供应链的系统动力学模 拟模型。我们依据广泛应用的供应链运作参考框架, 评估供应 链的可靠性、反应性和灵活性。模拟了141个情境, 体现不同 的供应方特点、库存管理策略和需求预测方法的组合, 确定可 以最大程度减少短缺和总成本的Pareto最优管理政策组合。 虽然供应方前置时间长, 需求难以预测, 阿米卡星的供应链还 是具有98%的可靠性, 其灵活性也足以应对20%的需求增长而 不出现短缺。但上述结构是通过积压库存达167%实现的, 这 也增加了存货成本。供应方的反应性较低：只有57%的订单 在一个月内运到省中心药品仓库。我们发现了三项Pareto最 优安全库存管理政策。在没有其他最优政策的情况下, 仅缩短 供应方前置时间也可以带来Pareto最优结果。本研究产生了 具体、可行的指南, 通过实行最优供应链政策来有成本效益地 减少药品短缺。优先选择前置时间短的药品供应方可以适应 需求的意外变化。最优供应链管理应作为国家降低死亡率政 策的关键组成部分。 El bajo rendimiento de las cadenas de suministro presenta un obstáculo significativo para el control de enfermedades en los países en desarrollo. El agotamiento de las medicinas esenciales conduce a la interrupción del tratamiento, lo que puede forzar cambios en los regímenes farmacológicos del paciente, impulsar la resistencia a los medicamentos y aumentar la mortalidad. Este estudio es uno de los pocos que evalúa cuantitativamente la efectividad de las políticas de la cadena de suministro en la reducción de escasez y costos. Este estudio desarrolla un modelo de simulación de la dinámica de los sistemas de la cadena de suministro para la amikacina, un fármaco de segunda línea contra la tuberculosis usando 10 años de datos sudafricanos. Evaluamos el desempeño actual de la cadena de suministro en términos de confiabilidad, capacidad de respuesta y agilidad, siguiendo el ampliamente usado marco de Referencia de Operación de la Cadena de Suministro. Simulamos 141 escenarios que representan diferentes combinaciones de características de proveedores, estrategias de manejo de inventario y métodos de pronóstico de demanda para identificar el conjunto óptimo de Pareto de políticas de manejo que minimizan conjuntamente el número de periodos de escasez y el costo total. A pesar de los largos plazos de entrega de los proveedores y la demanda impredecible, la cadena de suministro de amikacina es 98% confiable y lo suficientemente ágil como para acomodar un aumento de 20% en la demanda sin escasez. Sin embargo, esto se logra sobrestimando la amikacina en un 167%, lo que conlleva altos costos de almacenamiento. La capacidad de respuesta de los proveedores es baja: solo el 57% de los pedidos se entregan al depósito central de medicamentos de la provincia en un mes. Identificamos tres políticas de manejo de existencias de seguridad del óptimo de Pareto. El corto plazo de entrega del proveedor puede producir resultados óptimos de Pareto incluso en ausencia de otras políticas óptimas. Este estudio produce pautas concretas y procesables para reducir los desabastecimientos de manera costo-efectiva mediante la implementación de políticas óptimas para la cadena de suministro. La selección preferencial de los proveedores de medicamentos con tiempos de entrega cortos acomoda cambios inesperados en la demanda. La gestión óptima de la cadena de suministro debe ser un componente esencial de la política nacional para reducir la tasa de mortalidad.

Despite the increasing significance and the central role of stock markets, investing in gold has remained a popular choice among market participants. The necessity to forecast gold prices has sparked a voluminous literature on the matter, though there is no consensus regarding the variables that drive gold prices evolution or the methodology that adheres to the true data generating mechanism. In this paper, we forecast gold prices comparing econometric and machine learning methodologies in order to produce a model that can better grasps the dynamics of gold prices. To do so, we filter the most prominent variables proposed by the relevant literature exploiting the ability of the Ensemble Empirical Mode Decomposition algorithm to separate noise from the actual evolution of a timeseries. Then, we train Support Vector Regression models coupled with the linear and nonlinear kernels. Our empirical findings suggest that the proposed model adheres closer to gold price evolution than Ordinary Least Square regression and Least Absolute Shrinkage and Selection Operator models used in the literature, while it can be utilized in shaping profitable portfolios.