Changes In Market Prices Research Articles

Family Social Support Can Reduce Work Stress In Farmers

Background: Farmers often work alone in remote environments with little time for rest or socialization, leading to mental health problems and poor psychological well-being. The imbalance between physical and psychological aspects leads to physical responses such as difficulty concentrating, and psychological responses such as anxiety. Social support, self-efficacy, and coping mechanisms play a role in addressing farmers' work-related stress. Objective: This study aims to analyze family social support can reduce work stress in farmers Methods: The method used in this research is a literature study using 3 databases, namely Google Scholar, Science Direct, and PubMed with a range of publication years 2019-2024. Results: The results showed that farmers' work stress can be caused by various factors, including heavy workload, long working hours, as well as pressure from weather, pests, and changes in market prices. Furthermore, it has been found that high family social support can reduce farmers' work-related stress. However, it has also been found that family social support has a significant impact on farmers' stress but not on farmers' psychological well-being. Conclusion: It is important for nurses to provide education to farmers to address work-related stress by first observing the causes of farmers' work-related stress and their experiences in coping with it. Family social support is one alternative that can be provided as education to farmers to address their work-related stress. Therefore, it is necessary to further understand how family social support affects farmers' work stress and how effective coping strategies can reduce this pressure.

Soliton wave profiles and dynamical analysis of fractional Ivancevic option pricing model

This study dynamically investigates the mathematical Ivancevic option pricing governing system in terms of conformable fractional derivative, which illustrates a confined Brownian motion identified with a non-linear Schrödinger type equation. This model describes the controlled Brownian motion that comes with a non-linear Schrödinger type equation. The solution to comprehend the market price fluctuations for the suggested model is developed through the application of a mathematical strategy. The modified Kudryashov analytical method is applied to find the fractional analytical exact soliton solution. The restrictions on the parameters required for these solutions to exist were also the result of this approach. The dynamical insights are examined and significant aspects of the phenomenon under study are discussed through the use of the bifurcation analysis. In the related dynamical system, the phase portraits of market price fluctuations are displayed at equilibrium points and for different parameter values. Additionally, the chaos analysis was carried out to show the quasi-periodic and periodic chaotic patterns. In order to track changes in market price, the sensitivity analysis of the studied model is also looked at and presented at different initial conditions. It was discovered that the model experienced price fluctuations as a result of minute changes in initial conditions.

Awareness Level of Geographical Indication on Kanyakumari Matti Banana

The purpose of the study is to evaluate the Awareness level of Geographical indication (GI) got by Matti Banana in the district of Kanyakumari. It was discovered that the majority of respondents have less knowledge about the benefits of Geographical Indications. Farmers in the research region were said to be unaware of the true advantages of GI and its significance for the socioeconomic advancement of the local community. Respondents have medium awareness due to change in market price of GI Tagged Banana which would have caused not understanding the benefits and significance of Geographical indication.

Assessing the implications of imported electricity equipment in Indonesia: A comprehensive ECM approach for short-term and long-term analysis

This study aims to explore the implications of imported electrical equipment in Indonesia, analysing both short-term and long-term impacts using a quantitative approach. The research focuses on understanding how various economic factors, such as domestic production, international pricing, national income, and exchange rates, influence the country’s import dynamics in the electrical equipment sector. Employing an Error Correction Model (ECM) for regression analysis, the study utilises time-series data from 2007 to 2021 to delve into the complex interplay of these variables. The methodology involves a comprehensive analysis using the Augmented Dickey-Fuller and Phillips-Perron tests to assess the stationarity of the data. This approach ensures the robustness of the ECM, which is employed to analyse the short-term and long-term effects of the identified variables on electrical equipment imports in Indonesia. The results reveal significant relationships between these economic factors and import levels. In the short term, imports are shown to be sensitive to changes in domestic economic conditions and international market prices, while in the long term, the country’s economic growth, reflected through GDP, emerges as a significant determinant. The findings suggest that Indonesia’s electrical equipment import policies must adapt highly to domestic and international economic changes. In the short term, a responsive approach is required to manage the immediate impacts of market fluctuations. The study highlights the importance of aligning import strategies with broader economic growth and environmental sustainability goals for long-term sustainability. Policymakers are advised to focus on enhancing domestic production capabilities, reducing import dependency, and ensuring that environmental considerations are integral to import policies. This study contributes to understanding import dynamics in a developing country context, offering valuable insights for policymakers and industry stakeholders in shaping strategies for economic growth and sustainability in the electrical equipment sector. The findings underscore the need for a balanced, data-driven approach to managing imports, aligning short-term responses with long-term strategic objectives for Indonesia’s ongoing development and industrial advancement.

Decomposition of food price inflation in Sri Lanka

The recent upsurge in food prices in Sri Lanka has raised significant concerns, posing a threat to the food and nutritional security of the population. However, the causal factors driving this inflation still need to be better understood. Our study, utilizing Cointegration and Vector Error Correction Models (VECM), investigates the role of supply and demand-side determinants of food inflation. The study identified changes in exchange rate, agricultural wages, fuel prices, money supply, and fertilizer prices as key drivers of food inflation in Sri Lanka. Expansion of the money supply exerted the most substantial influence on food inflation in the long run. At the same time, changes in world market prices were found to be the most influential determinants of food inflation in the short run. Findings underscore the need for a comprehensive strategy that combines supply-side enhancements and demand-side management to control food inflation in Sri Lanka.

A critical analysis of public private partnership model in energy from waste projects

The Public Private Partnerships (PPP) and the Private Finance Initiative (PFI) have been significantly adopted over the last three decades as a mechanism for the delivery and financing of projects. Various infrastructure project types in the energy and the transport sectors have adopted this procurement system for their ‘anticipated’ effectiveness in risk distribution and value for money. Yet, their acclaimed benefits, particularly in ensuring value for money in the waste to energy – or energy-from-wastes (EfW) – sector are under significant scrutiny. Governments and investors are increasingly questioning their fiscal efficiency and overall benefit to taxpayers. Despite these concerns, studies investigating whether PFI provides value of money of PFI in EfW are lacking. This study addresses this gap by comparing the PFI models used in the UK and Canada in currently operative EfW projects using mixed risk epistemology approach combining methods that provide both a real and objective evaluation of issues and risks and a subjective (or opinion-based) evaluation. The former is achieved through a quantitative probabilistic model for simulating EfW feasibility at the operation phase. The quantitative probabilistic model is capable to model and capture how operational phase costs and profitability are affected by lifecycle risks (such as quality of service driven by contractual requirements that are usually passed on to the Operation and Maintenance (O&M) contractor) and a range of technical, payment and incentive dynamic variables and their fluctuations. If the impact of these variables is not considered and modelled accurately, the O&M contractor could incur significant losses undermining the viability of the EfW project. The latter employs a psychometric methodology to delve into the primary concerns of professionals involved in EfW projects regarding associated risks. The results show that common O&M risks identified in EfW PPP projects in UK and Canada prevail around unplanned maintenance, infeed waste reduction, market price, unsustainable debts, and policy changes.

Research on the Granger Causality Experiment of Fukushima Nuclear Wastewater Discharge on Changes in Oil Market Prices

Research on the Granger Causality Experiment of Fukushima Nuclear Wastewater Discharge on Changes in Oil Market Prices

Optimal modes of operation and product cost allocation in sugarcane steam cogeneration plants

Sugar and ethanol production from sugar cane is one of the most competitive sectors of Brazil’s economy. The bagasse generated during the production process is used as fuel in cogeneration plants that provide thermal and electrical energy to the process. In the last decades, many sugar cane factories have produced a surplus of electricity that may be sold to the grid as a new product. This paper applies energy billing optimization and thermoeconomic analysis to a sugarcane steam cogeneration plant to determine the optimal operating mode of the plant, unveils the cost formation process of its internal products (refinery heat, process heat, and consumed electricity), and examines how the results are affected by: (i) the demand for the plant’s energy services, (ii) the availability of bagasse, and (iii) the selling price of surplus electricity. The thermoeconomic analysis involves a detailed study of the cost formation process, which is achieved through the decomposition of the steam cycle of the cogeneration plant into subcycles. Three main subcycles, in addition to the deaeration cycle and other auxiliary subcycles, have been identified: the cogeneration cycle generating work in the high-pressure turbine and refinery heat (subcycle one), the cogeneration cycle generating work in the high- and medium-pressure turbines and process heat (subcycle two), and the condensing cycle that generates only work in the high-, medium-, and low-pressure turbines (subcycle three). These subcycles make up the productive structure of the steam cogeneration plant and explain how water/steam goes through energy conversion processes from the bagasse energy to the heat and electricity produced. Both the optimization model and the thermoeconomic analysis serve as valuable tools for planning in response to potential changes in bagasse and electricity market prices, as well as fluctuating product demand conditions. In the base case, combining optimization with thermo-economic analysis, the unit monetary cost of the final products has been determined: heat for refinery (8.85 R$/MWh), electricity sold (183.60 R$/MWh), internally consumed electricity (41.51 R$/MWh), and process heat (8.85 R$/MWh).

A blockchain-based dynamic energy pricing model for supply chain resiliency using machine learning

The escalation of energy prices and the pressing environmental concerns associated with excessive energy consumption have compelled consumers to adopt a more optimal approach towards energy usage and an advanced infrastructure such as smart grids. Blockchain technology significantly improves energy management by creating supply chain resiliency in a distributed smart grid. This study proposes a blockchain-based decision-making framework with a dynamic energy pricing model to manage energy distributions, particularly during an energy crisis. Empirical data from U.S. consumers are employed to show the applicability of the proposed model. We include price elasticity to address changes in energy market prices. Findings revealed that the proposed framework reduces total energy costs and performs better when a disruption has occurred. This study provides a post hoc analysis in which four machine learning algorithms are used to predict energy consumption. Results suggest that the autoregressive integrated moving average (ARIMA) algorithm has the highest accuracy compared to other algorithms.

How do price (risk) changes influence farmers’ preferences to reduce fertilizer application?

AbstractThe decision of farmers to reduce fertilizer applications and, thus, the achievement of agri‐environmental policy goals interacts with market price developments. In this study, we analyze how changes in price levels and volatility over time (i.e., 1991–2006 vs. 2007–2022) affected farmers’ preferences to reduce fertilizer application using statistical inferences of stochastic dominances. The analysis considers two cropping systems and fertilizer reduction measures: (i) grassland‐based milk production and the use of legumes and (ii) wheat production and the use of variable rate application. We show that the economic value of reducing fertilizer increased over time in both grassland‐based milk and wheat production. However, only in the case of wheat production was the reduction in fertilizer application observed as more risk‐reducing over time. In contrast, in grassland‐based milk production, the co‐movement of fertilizer and milk prices canceled out the increase in risk reduction. We conclude that changes in market price, along with agri‐environmental subsidies, can increasingly incentivize the reduction of fertilizer use.

Research on Digital Transformation of International Trade A Case Study on Amazon

At present, digital economy is a new economic form that uses new technologies with new ideas to create new value. International trade is a crucial way of global economic flow, and its digital transformation is the product of the combination of the exploring trend of digital economy and international transaction rules, and is an important basis for countries to improve their core technologies in the era of trade globalization. Taking Amazon as an example, this paper focuses on the impact of Amazons transformation on its market share. Based on the data change of Amazon stock market price from 1997 to 2023, this paper mainly analyzes the difficulties and countermeasures encountered by Amazon in the process of transformation. It is found that Amazon mainly relies on the development of its own technology and the clear and rigorous analysis of market environment and supply and demand data, and chooses the most suitable path on the road of transformation. This is of significant value to other similar enterprises and has a guiding role in promoting the global digitalization process.

ДОСЛІДЖЕННЯ СУЧАСНИХ УМОВ ПРОВАДЖЕННЯ ДЕВЕЛОПЕРСЬКОЇ ДІЯЛЬНОСТІ В УКРАЇНІ

The article is devoted to the study of current trends in the real estate market, in the construction industry of Ukraine in order to identify the modern conditions in which companies related to the implementation of development projects operate. It has been established that development projects are characterized by particular complexity, and therefore their implementation requires taking into account external factors that can affect the results of the work of developers and other involved persons. The dynamics of the main indicators characterizing the real estate market from the standpoint of market volumes (number of signed contracts) and price changes in the primary and secondary housing markets were analyzed. The study of the number of real estate agreements (agreements on the alienation of real estate, land plots and land shares), including agreements for the purchase and sale of apartments and residential buildings, made it possible to identify the main trends in the real estate market. It was determined that prices on the primary housing market grew at an accelerated pace during the research period, while price fluctuations were more noticeable on the secondary housing market. The volumes of manufactured construction products and their dynamics, the activity of builders and the indicators of acceptance into operation of residential and non-residential buildings were studied. It has been established that the Indices of construction products are unstable in all their types and are characterized by significant fluctuations, that is, the rate of changes in the volume of construction products is characterized by high variability. It has been established that the construction industry and the real estate market are sensitive to changes in the internal and external political situation in the country. It has been proven that the full-scale invasion of Ukraine and the introduction of martial law led to a drop in the real estate market and a drop in construction. Despite the extreme fall of the last year, the housing market. and therefore the development market is gradually recovering, most developers have resumed their activities and are trying to reach the pre-war level in terms of their volumes, companies with unfinished construction have focused on housing completion. A number of construction companies focused on rebuilding damaged housing and real estate. Developers, who at the beginning of the full-scale invasion were in the initial stages of construction, demonstrate positive dynamics of construction work.

Procyclical variation margins in central clearing

This study analyzes the effect of asset prices on the daily exchange of profit and loss from derivatives contracts in central clearing, called variation margin (VM). It provides empirical evidence that the VM exhibits a high volatility and a significant relation to changes in market prices. The magnitude of VM procyclicality has significant implications for systemic risk. This study analytically shows that an internal loan facility can reduce liquidity hoarding and external funding during stress periods and thus contribute to contain systemic risk. In addition, this study proposes a new scheme called the posting of variation margin in securities (PVMS) clause as an example of internal loan facility between the contract counterparties. The PVMS can replace cash VM payments with collateral posting in times of market stress.

The Impact Automated Market Makers on Stock Prices and Returns

Examining the impact Automated market makers have on stock prices and returns is the focus of this study. Automated market makers are state-of-the-art financial instruments that facilitate the bilateral trade of digital assets in the DeFi industry. Market participants trade against pools of liquidity rather than individual buyers and sellers. Market returns, price changes, and liquidity levels will be analyzed both before and after the introduction of automated market makers. Well take a look at how automated market makers affect transaction costs, liquidity, and trading volume. Major stock markets that employ automated market makers will be surveyed for data collection and analysis. The effect of liquidity ratios on market performance will be studied statistically. Understanding the impact Automated market makers have on market returns and pricing is critical for traders, regulators, and researchers. Improved investment returns and the development of decentralized financial ecosystems may result from incorporating the results into market structure and liquidity. The implementation of automated market makers on centralized stock exchanges has been aimed at enhancing market efficiency. This research investigates the impact of automated market makers on stock prices and returns. Using monthly market returns from the closing index and equity price data, the study examines the effects before and after the implementation of automation. The study utilizes a longitudinal research design, analyzing listed firms with data spanning the study period. The findings contribute to the understanding of the influence of automated market makers on stock prices and returns, particularly in regions with limited literature on the subject.

Forecasting Significant Stock Market Price Changes Using Machine Learning: Extra Trees Classifier Leads

Predicting stock market fluctuations is a difficult task due to its intricate and ever-changing nature. To address this challenge, we propose an approach to minimize forecasting errors by utilizing a classification-based technique, which is a widely used set of algorithms in the field of machine learning. Our study focuses on the potential effectiveness of this approach in improving stock market predictions. Specifically, we introduce a new method to predict stock returns using an Extra Trees Classifier. Technical indicators are used as inputs to train our model while the target is the percentage difference between the closing price and the closing price after 10 trading days for 120 companies from various industries. The 10-day time frame strikes a good balance between accuracy and practicality for traders, avoiding the low accuracy of short time frames and the impracticality of longer ones. The Extra Trees Classifier algorithm is ideal for stock market predictions because of its ability to handle large data sets with a high number of input features and improve model robustness by reducing overfitting. Our results show that our Extra Trees Classifier model outperforms the more traditional Random Forest method, achieving an accuracy of 86.1%. These findings suggest that our model can effectively predict significant price changes in the stock market with high precision. Overall, our study provides valuable insights into the potential of classification-based techniques in enhancing stock market predictions.

Beyond the merit order effect: Impact of the rapid expansion of renewable energy on electricity market price

A drop in the average electricity market price owing to renewable energy with low marginal costs has been identified and explored as merit order effect. However, diffusion of variable renewable energy leads to concerns about the need for flexibility, decline of supply capacity, and volatility increase of electricity prices. For both decarbonization and stable electricity systems, understanding the drivers of market price is critical, but it is challenging because electricity markets are non-linear and affected by multiple factors. Hence, this research proposes a model based on machine learning techniques and explainable artificial intelligence (XAI) and estimates the multi-directional impact of renewable energy on the Japanese electricity market. The results reveal that the contribution of demand to market price is the largest, followed by solar generation and operable power facility capacity. This research identifies a large decline in the price triggered by solar power during the daytime; however, the effect of solar power varies by the time of day, season, and demand. Additionally, the results suggest that the market price increases when demand is high and solar generation is low, such as during summer evenings. Using XAI, this study quantitatively and visually demonstrated that interactions between solar power, demand, and operable power facility capacity are the key factors behind the high market volatility and price surge. It is important to manage the pace of plant installation and energy transition. Our study provides insights into how and when the market price changes with variable renewable energy and other policy-making factors.

Greed is good? Of equilibrium impacts in environmental regulation

This paper presents a novel method to quantify the market equilibrium impacts of tighter environmental policies. The approach is taken to evaluate the welfare effects of emerging environmental regulations to protect the biodiversity of river ecosystems that put pressure on reducing hydropower generation capacity. A dataset of 129 million bids in the Nordic electricity market is used to calculate how small reductions in hydropower capacity change market outcomes and surpluses hour-by-hour for the years 2011–2022. Reaching the EU Biodiversity Strategy target of 2030 is estimated to reduce 56 MW of hydropower capacity in Finland. Such a change would lead to quantifiable welfare losses of €62 million over time, not counting environmental benefits. Notably, the producer surplus increases by €318 million over time through electricity market price changes, suggesting that instead of lobbying against biodiversity enhancing regulation, agreeing to an industry-wide implementation could benefit both the environment and the firms. Yet the fact that consumer costs increase more than the mere implementation cost reads as a sign of caution when tighter biodiversity policies are introduced.

Projection of shadow price of beef cattle in the Bali beef fattening industry

<span lang="EN-US">Any changes in supply and demand result in the imperfect development of a market mechanism, so a disequilibrium price occurs. Price changes, sometimes, do not reflect the prevailing price, issued by an agricultural business. Therefore, this research aims to project the shadow price of beef cattle (output) in the Bali beef fattening industry, utilizing cost production theory. Technically, the comparison of the total of the shadow prices of beef cattle employed two methods, such as a total of revenue and total cost. The research was conducted in the Regency of Lombok Tengah from July to September 2020. Then, the site and group of farmers were determined by purposive sampling. The criteria of samples were farmers performing the Bali beef fattening industry with an intensive raising system, utilizing cross-section data collected via interview and questionnaire. In conclusion, it is found that the shadow price of beef cattle was higher than the market price. It caused non-responsive market price changes unless a great change occurred. The shadow price was established by a greater marginal cost than marginal revenue.</span>

Valuation Techniques and Financial Statement Disclosures for Non-Bearer Plant Sustainability Performance in Indonesia

This study presents early evidence of the current valuation techniques and financial statement disclosure of non-bearer plants by companies on the Indonesia Stock Exchange. The emphasis is on measurement and valuation techniques amended in 2018-2019—the compliance in financial statement disclosure items provided by the companies. The valuation technique of a biological asset is quite challenging because this process includes measurements due to changes in market price and biological transformation. The study was conducted through a review of financial statements, primarily focusing on their disclosure. The study compared the change recorded in the initial years after amendments in accounting policy (2018 and 2019). The result showed that companies applied no uniform techniques to the valuation of biological assets. Therefore, it significantly affected their disclosure mainly due to the scarcity of technical guidance concerning biological assets. This study was limited to observing disclosure information and its accounting treatment by companies listed in Indonesian Stock Exchange under Agriculture and related industries. This study is useful to academics, accounting standard setters, and regulatory bodies. The paper is useful as feedback to accounting standard setters regarding implementing Financial Accounting Standard No.69: Agriculture in Indonesia. The findings aim to provide insights to regulatory bodies regarding financial information provided by companies in the logging sector. Keywords: measurement, valuation technique, financial statement disclosure, non-bearer plant, compliance, Indonesia

Sequential monitoring of stock market price changes

In 2016, Eugene Fama mentioned that he wanted a systematic way of identifying and predicting a stock market bubble. This paper develops a statistical method to sequentially monitor the stock market price changes. A new simple boundary function is proposed and asymptotic properties are established. In the empirical application, we successfully detect that the U.S. dot-com bubble occurred around June 1997 using the S&P 500 and Nasdaq indices.