Market Scenario Research Articles

Real-Time Stock Forecasting: Leveraging Live Data and Advanced Algorithms for Accurate Predictions

This groundbreaking research introduces an innovative stock market prediction methodology that integrates financial modeling, machine learning, and real-time data analysis. Anchored in a deep understanding of stock market dynamics, including economic statistics, company performance, and market sentiment, our approach employs natural language processing (NLP) and predictive modeling to analyze live data for accurate stock price predictions. The method involves collecting and preprocessing a dynamic dataset enriched with financial indicators and historical stock prices. Utilizing Long Short-Term Memory (LSTM) algorithms, our model exhibits an impressive 96% accuracy in forecasting stock movements, showcasing adaptability to diverse market scenarios and responsiveness to economic factors and sentiment shifts. The incorporation of live data proves pivotal in providing timely insights for informed decision-making, establishing our model as a valuable tool for navigating the complexities of the modern financial landscape.

RECENT MARKET ANALYSIS OF PLANT PROTEIN-BASED MEAT ALTERNATIVES AND FUTURE PROSPECT

Food shortage is a primary challenge as the human population is increasing rapidly. Therefore, the world is looking for ways to establish sustainability in the food system to generate nutritious food with better sensory qualities. To fulfill the animal protein requirements of the world, different meat alternatives are available in the market, like cultured meat, mycoprotein, and insects, but from a sustainable point of view, plant-based meat alternatives are the most suitable. However, in producing these alternatives, industrialists need to be aware of particular challenges and opportunities in production and marketing while manufacturing. The major challenge for industrialists is the consumer acceptance rate, which depends on plant-based products' physical appearance and taste. They can improve the marketing of plant-based meat products by improving plant-based meat alternatives' physical and sensory characteristics. The focus of this paper is to discuss the technologies necessary for the production of plant-based meat alternatives. The current findings suggest that the market scenario will change for plant-based meat producers, in which customer preference and beliefs will have a massive role in the acceptance and marketing of this meat. The development of plant-based meat alternatives is the first step to achieving the goal of sustainable food; more innovations are needed to establish a healthier and sustainable food system as this type of food has a low carbon footprint compared to other products available. Keywords: Plant-based meat, sustainability, food shortage, consumer preference, opportunities, challenges.

Development of a New Method to Optimize Operations of an Existing Crude Unit

A nonlinear optimization method working with the Simplex Method was developed to find optimum operating conditions of a crude oil distillation unit subject to defined process constraints, product specifications and prevailing market conditions. Then the method was applied to a commercial crude unit. This crude unit was modeled using Aspen Hysys V12.1 process simulation software. Two sets of market scenarios designated as the maximum gasoline scenario and minimum gasoline scenario were applied to the simulation model. It was found that the model minimized naphtha production and maximized kerosene production for minimum gasoline scenarios where the price of kerosene and diesel products were higher than naphtha products. Similarly, the model maximized the naphtha product yield for the maximum gasoline scenario where the naphtha price exceeds the mid-distillate product price. The iterative procedure developed for the study monotonically converged to optimum operating conditions for both market scenarios. It was observed that the optimization scheme developed in this study could generate a significant profit increase in the conventional crude unit investigated in this study without and capital investment.

Working of Stock Price Prediction Using LSTM

Our proposal centers on the development of a robust tool designed to forecast stock prices by leveraging a sophisticated combination of advanced techniques. We're merging spatial features, which essentially encapsulate the distinctive attributes of individual stocks, with the powerful capabilities of long-and-short-term memory networks (LSTM). These networks excel at recognizing and interpreting patterns and trends over extended periods. By integrating these two elements, our model gains the ability to capture both short-lived fluctuations and enduring trends within stock prices.This fusion of spatial features and LSTM networks represents a significant advancement in stock price prediction methodologies. We're confident that this innovative approach will surpass existing methods in terms of accuracy and reliability. To validate our confidence, we're conducting rigorous testing and refinement of the system using diverse sets of data. This ensures that our model performs effectively across various market conditions and scenarios, thereby instilling trust among investors seeking dependable insights into stock market trends. Key Word: Long and Short-Term Memory, Time-Series Data, Yahoo Finance.

Self-consumption potential and surplus compensation policy impact on rooftop photovoltaic systems in Spain

This research examines the viability of rooftop photovoltaic systems for electricity self-consumption in Spain's residential sector, analyzing municipality-level data on electricity demand, rooftop availability, solar irradiance, and various electricity market and surplus policy scenarios. Differences are found between rural and urban contexts, with rural areas exhibiting greater potential for photovoltaic systems deployment due to more available rooftop space. The study highlights the crucial role of surplus electricity management, showing that an efficient use can significantly boost self-consumption potential, particularly in rural areas where surplus constitutes about two-thirds of the photovoltaic production. Economic analyses under different electricity market conditions and surplus compensation policies show that these factors critically affect the attractiveness and viability of these systems. Current high electricity prices in Spain have reduced their payback period, but the prevalent surplus compensation policy, a 1-month net billing feed-in tariff, reduces their cost-effectiveness by up to 60 % compared to a hypothetical 12-month net billing approach. The findings underscore the need for policy adjustments to maximize rooftop occupancy and ensure fair compensation of surpluses, highlighting the importance of tailored strategies for effective sustainable photovoltaic systems deployment in residential contexts.

Evaluation and use of recently introduced sheep breeds to Uruguay

Sheep numbers in Uruguay have declined over the last 30 years. In the north and northeast there are extensive wool and dual-purpose systems, respectively, where both wool and meat impact on income. In the south, small-scale family sheep meat production systems have flourished. In this thesis I report work pertaning to production systems in the south (1) and northeast (2) regions. Corriedale (C) is the predominant breed in the country. Milchschaf (M) has been disseminated in the south but C still prevails in the northeast. There is a paucity of rigorous evaluations of these breeds or alternatives that could be more profitable, such as Highlander (H) for the south and Dohne Merino (DM) for the northeast. I experimentally assessed: in 1), wool production, reproduction and growth in C, H and M, and in 2), wool quality and body traits in ewe hoggets from rotational crossbreeding between C and DM. In 1), wool production was better in C, but H and M outperformed C in live weight and reproductive performance. In 2), genotypes with greater proportions of DM produced lower fleece weight, and those with greater proportions of C produced coarser fibre diameter, with no differences in wool quality or morphological traits related to meat aptitude. Gross margins (GM) integrate physical performance with product values and costs, allowing monetary comparison between genotypes. I calculated GMs for a number of production and market scenarios. In 1), most often GM was greatest for H, followed by M. The differences in favor of H and M over C decreased when I considered higher feed requirements for H and M. The GM of H and M increased by assuming they were wool-less (the cost of harvesting exceeds the value of the wool produced). In 2), I considered low and high prices for wool of different fibre diameter. GM was greatest for ¾DM and ⅝DM at low prices, and for ¾DM and ½DM at high prices. C had the lowest GM in all scenarios. I recommend H for southern systems and research on hair sheep breeds. In extensive dual-purpose systems, rotational crossbreeding between C and DM allows for improved wool income without losing meat potential. I formulate recommendations about future genotype evaluations, integrating them with current within breed genetic evaluations.

Second‐best socially optimal R&D under output spillovers

AbstractUsing the standard two‐stage game of process R&D and Cournot competition with R&D output spillovers, this paper provides a thorough second‐best welfare analysis. The planner's solution is compared with the standard noncooperative scenario, the R&D cartel, the cartelized research joint venture, and the social research joint venture solution in terms of propensities for R&D as well as welfare levels. The main result is that, when spillovers are not too high, a cartelized joint venture unexpectedly outperforms the planner's solution in terms of propensities for R&D and resulting welfare level, though it is the only market scenario to do so. We also assess the performance of a social joint venture, relative to all the well‐known scenarios for the organization of R&D. Finally, we observe that the gap between market outcomes and planner's solutions, in terms of welfare levels, increases as R&D becomes less appropriable.

Beyond Jack-Ups: A Moonshot for Future Offshore Wind Turbine Installation Vessels for an Uncertain Market

This paper addresses the growing offshore wind market's demand for larger turbines in deeper waters by highlighting limitations in existing installation solutions and proposing a new concept with a floating monohull, named Moonshot, which will thus be different than traditional jack-up or semi-submersible crane installation vessel options. This paper discusses the design process, which combines Ulstein Rotterdam’s Controlled Innovation and Blended Design to develop the concept. This process is used to explore various market scenarios to determine optimal vessel parameters. Results demonstrate how optimizing for financial performance or seakeeping behavior impacts the design. Moonshot's initial parameters are established, and its performance is compared to existing installation solutions.

Encoder–Decoder Based LSTM and GRU Architectures for Stocks and Cryptocurrency Prediction

This work addresses the intricate task of predicting the prices of diverse financial assets, including stocks, indices, and cryptocurrencies, each exhibiting distinct characteristics and behaviors under varied market conditions. To tackle the challenge effectively, novel encoder–decoder architectures, AE-LSTM and AE-GRU, integrating the encoder–decoder principle with LSTM and GRU, are designed. The experimentation involves multiple activation functions and hyperparameter tuning. With extensive experimentation and enhancements applied to AE-LSTM, the proposed AE-GRU architecture still demonstrates significant superiority in forecasting the annual prices of volatile financial assets from the multiple sectors mentioned above. Thus, the novel AE-GRU architecture emerges as a superior choice for price prediction across diverse sectors and fluctuating volatile market scenarios by extracting important non-linear features of financial data and retaining the long-term context from past observations.

Effects of the U.S. inflation reduction act on SMR economics

The U.S. Inflation Reduction Act (IRA) of 2022 provides a wide array of tax credits and other incentives for low-carbon energy. The technology-neutral clean generation production tax credit (PTC) (Section 45Y of the U.S. Internal Revenue Code) and the technology-neutral investment tax credit (ITC) (Section 48E) lower the net cost of new electricity generation projects with zero or negative greenhouse gas emission rates. We evaluate the impact of the IRA legislation—specifically the PTC and ITC—on the cost-competitiveness of small modular reactors (SMRs). We use the Argonne Low-carbon Energy Analysis Framework (A-LEAF) model to calculate the capacity factor of an SMR with a range of hypothetical variable operating and maintenance (O&M) costs in the Electric Reliability Council of Texas (ERCOT) electricity market. We selected ERCOT for market modeling because of its competitive structure, available data, and extensive use in prior literature. We use a discounted cash flow model to calculate the SMR’s net present value based on the market prices and capacity factors from A-LEAF, hypothetical ranges of capital and variable O&M costs, and other input parameters, with or without the IRA tax credits. We determine the SMR owner’s optimal choice of PTC or ITC for the hypothetical ranges of capital and variable O&M costs. We also evaluate potential shifts in the SMR owner’s optimal choice of PTC or ITC based on historical patterns of nuclear capital cost overruns in the United States. We also assess the sensitivity of our results to longer PTC period and electricity prices from the New England market, which tend to be higher than electricity prices in ERCOT. We find that even with the IRA tax credits, only SMRs with low capital and variable O&M costs would be economically feasible in the low-price ERCOT market scenario modeled. A longer PTC period and higher-price market such as New England, however, would significantly expand the economic feasibility of SMRs in the United States.

Investigation on the strategy of business administration level of enterprises in the new economic environment.

In the face of the new economic environment, enterprises must continuously enhance their capabilities to achieve long-term development. In the current market scenario, business management relies on economic principles and legal accounting. Considering the current market situation, the article analyzed enterprises system reform and production planning, proposing corresponding countermeasures. Therefore, in order to achieve rapid development, it was necessary to strengthen the management of enterprises. In this paper, the current problems faced by enterprises, solutions and the significance of enterprises needed to improve their management level were explained, and the situation of enterprises was analyzed through the enterprise strategic management model. Comparing with the traditional management model in terms of the complexity of enterprise management processes, efficiency, management level score, and quarterly profit,findings reveal that the management model in the new economic environment has reduced the complexity of the enterprise process by 0.17 points. The management efficiency has increased by 0.15 points, the management score has increased by 14 points, and the quarterly profit of the company has increased by 30,000 yuan. Furthermore, it is elucidated that, in the new economy, enhancing the management level is essential for enabling enterprises to attain long-term development.

Clean energy for a sustainable future: Analysis of a PV system and LED bulbs in a hotel

Scholars are increasingly prioritising sustainability as the driving force behind their research efforts, and many businesses are placing the customer at the centre of their agendas. Hospitality facilities, such as hotels, are also making concerted efforts to mitigate climate change. The present study proposed a sustainability analysis of the installation of a photovoltaic (PV) system and the replacement of conventional lighting with light emitting diode (LED) bulbs in a hotel, considering economic, environmental and social sustainability dimensions.The results showed that NPV varied between 967 and 3624 €/kW for the PV system, with a 100 % probability of achieving a positive NPV in a high market scenario and a 79–84 % probability of the same in a low market scenario. Concerning energy efficiency, the adoption of LED bulbs determined a NPV in the range of 105–216 €/bulb. These figures were highly dependent on the percentage of self-consumed energy and the purchase price of electricity. Environmental analyses indicated that the installation of a PV system could lead to an emissions reduction of 422 gCO2eq/kWh, translating to 61 tCO2eq/year. In addition, social analyses underscored the significance of renewable installations and energy-efficiency interventions to support the ecological transition. However, consumer choices within hotels did not consistently align with sustainable models. Notably, a green premium of 13.2 €/day was recognised for hotels using renewable systems, and 8.3 €/day for those using smart lighting. The results advocate for a pragmatic sustainability model that sees green investments enhancing a hotel's sustainability. However, a shift in consumer attitudes is necessary to fully realise this potential.

Flexible industrial power-to-X production enabling large-scale wind power integration: A case study of future hydrogen direct reduction iron production in Finland

Hydrogen Direct Reduction of Iron (HDRI) combined with renewable electricity is an attractive option for low-carbon steel production. In this paper, we present a novel and computationally efficient techno-economic power-to-x-plant optimization model which is then applied to an HDRI plant located in Finland. Plant dimensioning was carried out in several current and future power market and regulatory scenarios. We predict a production cost of 373 €/t for hot briquetted iron, and 351 €/t for a future scenario of 2025–2030. When the recently introduced EU rules for renewable fuels of non-biological origin are applied, the production costs increased by 30–46 €/t. The rules also have a significant increasing effect on the required hydrogen storage. The flexibility of the direct reduction shaft emerged as an important parameter affecting the required hydrogen storage as well as total production cost. The results of this paper hold significance for the optimal design of future low-carbon steel plants.

A Study of Electronic Banking in India

Digital cash and online transactions in the current market scenario have a great effect on e-banking and digital banking. E-banking comprises mainly of electronic funds transfer and usage of online banking services. Electronic banking was offered by the international banks initially since flow of digital cash was not predominant in the Indian market until the advent of demonetization. E-banking plays a major role in the present banking functionality. Electronic banking is inferred to be safe and secured, compared to physical banking. e-banking ensures qualitative banking operations compared to traditional banking. However, it has its own limitations and challenges when we talk about security, on the contrary. After visually perceiving the growth rate of e-banking services it can be verbalized that e-banking is accepted in the banking sector and an abundance of work can be done in this sector to make it more advance, safe and more accommodation provider in finance sector.

Quantile spillovers and connectedness between oil shocks and stock markets of the largest oil producers and consumers

This study explores the connectedness between major oil-producing and consuming countries' stock markets (United States, China, Russia, India) and different oil shocks categorized as demand, supply, and risk shocks, following Ready's (2018) framework. Employing a quantile-based connectedness approach and quantile cross-spectral dependence, our analysis spans from July 02, 2007 to May 31, 2023, encompassing diverse market conditions and events. These methodologies help identify interdependence patterns in extreme market scenarios at different time intervals. Key findings show variations in how these stock markets respond to oil shocks, depending on market conditions and quantiles. Demand-related shocks have the most significant spillover effects on the United States, Russia, and India, while risk-related shocks dominate as transmitters of shocks to the United States, China, and India in median quantiles. Market interconnectedness strengthens during extreme market conditions, reflecting historical events. Additionally, bearish markets offer diversification opportunities between these countries and crude oil. This study emphasizes the need for tailored investment strategies, monitoring global oil demand trends, dynamic portfolio management, crude oil inclusion in portfolios, and proactive responses to market players and geopolitical events. These insights benefit investors and policymakers seeking to optimize strategies in the interconnected global financial landscape.

MEDIA DEVELOPMENT IN THE LIGHT OF FREEDOM OF EXPRESSION: AN INSIGHT IN THE PRESENT INDIAN CONTEXT

Basic principle in journalism is to focus for development for public good and to uplift the society and transform the societal healthy practices. Media in its various capacity tries to inform, educate, and entertain the mass people for the benefit of human society and that should be the motto of media cutting across country line and geographical boundary. In Indian context how media focuses in its principle in the present set up that is interesting to be seen in the light of the critical perspective of freedom of expression as envisages in our constitution. In India journalistic freedom remained a million dollar question not only in pre-independence India but remained a valid question in post independence period and in the modern scenario too. In the present context if we speculate we observe that media organization and their key functionaries are vertically divided into pro and anti against any elected government and this phenomenon is not even permanent in them. They themselves frequently changes their colour for the reasons not best known by themselves alone but mass people are more knowledgeable these days and understand the very fact for this kind of shift. Off course for some time their survival question remains a valid point too in this competitive market scenario. In such a juncture this paper would like to discuss all those critical issues in this complex and media saturated condition more specifically in the light of journalistic freedom and their challenges in the current Indian situation.

Study on Sensory Evaluation and the Optimum Levels of Calcutta Betel Vine (Piper betel) Leaves Extract in the Preparation of Shrikhand

The present investigation was conducted at Section of Animal Husbandry and Dairy Science, College of Agriculture, Nagpur (Dr. PDKV, Akola) during 2022-2023. An effort was made to determine the optimum level of Calcutta betel vine leaves extract in the preparation of shrikhand, with an objective to evaluate the product by sensory quality along with its cost structure and physio-chemical properties. As per current market scenario, the limited variety of shrikhand is available in the stores but this study might help in addition to available varieties in the market. The present study was carried out with five treatments including control T1 and shrikhand prepared from cow milk chakka with different levels of Calcutta betel vine leaves extract i.e. 2.0%, 4.0%, 6.0% and 8.0% in treatment T2, T3, T4 and T5, respectively. The results showed that, the mean score of flavour of shrikhand in treatment T4 was highest (8.5) and lowest was recorded in T5 (5.0). The mean score for body and texture of shrikhand was highest in T4 (8.5) and lowest in T5 (5.2). In terms of colour and appearances, the highest score was recorded in T4 (8.5) and lowest in T5 (5.5). The highest overall acceptability score was recorded in T4 (8.5) over other treatments This superiority was found due to the addition of 6.0% betel vine leaves extract in Shrikhand. It is concluded that, shrikhand of acceptable quality can be prepared by adding 6.0% Calcutta betel vine leaves extract.

Construction and evaluation of financial distress early warning model based on machine learning

As the economy of our nation has expanded, the harm resulting from the financial risks associated with listed corporations has intensified, significantly impeding the survival and expansion of businesses. We must create a reliable financial crisis early warning system to prevent financial risks from endangering the business. This article aims to analyse the development of financial early warning models that use machine learning techniques. The model employs the Archimedes optimization method (AOA) method to optimize the parameters of SVM and selects 20 financial risk assessment indices as the input to anticipate the financial crises. The results suggest that the proposed model surpasses current models in terms of both prediction accuracy and resilience. They also highlight the ensemble model's greater predictive capacity when compared to individual algorithms, highlighting its efficacy in spotting early warning signs of financial instability in a variety of market scenarios. Financial organisations and governments may improve their risk management procedures and prevent future crises by incorporating machine learning techniques into the early warning system.

Predicting Social Inequality in Poland Using Price Dispersion on the Real Estate Market

Measuring and predicting price dispersion on the real estate market is an important issue for both investors and policymakers. Price dispersion in the housing market can be seen as an additional dimension for measuring social inequality and one of the main goals of public policies that focus on life satisfaction and the accumulation of permanent wealth. The article considers the Polish real estate market and proposes the prediction methods of four measures of dispersion. They are based on quantiles and allow overall measurement of dispersion. In the prediction of dispersion measures the plug-in predictors utylizing longitudinal mixed models are proposed. Furthermore, the ex ante prediction accuracy measure called the quantile of absolute prediction errors (QAPE) is assessed using the residual bootstrap estimators. QAPE allows for a comprehensive description of the distribution of prediction errors, thus fostering discussion of possible various market scenarios.

Navigating the Stock Market: Modeling Wealth Exchange and Network Interaction with Loss Aversion, Disposition Effect and Anchoring and Adjustment Bias

This study aims to integrate meta-analysis into agent-based models to provide foundational insights into biased agent interactions. It delves into how various behavioral biases, such as anchoring, disposition effect, loss aversion, and others, influence market dynamics and investor decisions. By utilizing agent-based models, it offers simulations of market scenarios and investor behaviors, highlighting the impact of individual decisions on market dynamics. The innovative approach of this study lies in its integration of behavioral finance theories with real market data, offering a nuanced analysis of market behaviors. This work contributes a new perspective to behavioral finance and encourages the use of agent-based models to deepen the understanding of market dynamics and investor behaviors, potentially aiding in financial market analysis and policy formulation. This study aims to provide a foundational infrastructure for research that wishes to integrate meta-analysis into agent-based models and enable the examination of biased agent behaviors. The findings of this study have managed to model the interactions of loss aversion, disposition effect, and anchoring and adjustment bias in the closest manner to the real world by considering agents' socio-demographic and psychological factors. The results are highly conducive to more accurately modeling human behaviors in portfolio optimizations and expanding the applications of Generalized Artificial Intelligence in financial markets.